{ "cells": [ { "cell_type": "code", "execution_count": 4, "id": "98676d9f", "metadata": { "id": "98676d9f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting datasets\n", " Downloading datasets-4.8.4-py3-none-any.whl (526 kB)\n", "Requirement already satisfied: pillow in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (12.1.1)\n", "Requirement already satisfied: pandas in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (2.3.3)\n", "Collecting tqdm\n", " Using cached tqdm-4.67.3-py3-none-any.whl (78 kB)\n", "Collecting httpx<1.0.0\n", " Using cached httpx-0.28.1-py3-none-any.whl (73 kB)\n", "Requirement already satisfied: requests>=2.32.2 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from datasets) (2.32.5)\n", "Collecting dill<0.4.2,>=0.3.0\n", " Downloading dill-0.4.1-py3-none-any.whl (120 kB)\n", "Collecting multiprocess<0.70.20\n", " Downloading multiprocess-0.70.19-py310-none-any.whl (134 kB)\n", "Requirement already satisfied: fsspec[http]<=2026.2.0,>=2023.1.0 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from datasets) (2026.2.0)\n", "Requirement already satisfied: packaging in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from datasets) (26.0)\n", "Requirement already satisfied: numpy>=1.17 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from datasets) (2.2.6)\n", "Collecting huggingface-hub<2.0,>=0.25.0\n", " Using cached huggingface_hub-1.7.2-py3-none-any.whl (618 kB)\n", "Collecting xxhash\n", " Using cached xxhash-3.6.0-cp310-cp310-win_amd64.whl (31 kB)\n", "Requirement already satisfied: pyyaml>=5.1 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from datasets) (6.0.3)\n", "Requirement already satisfied: filelock in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from datasets) (3.25.2)\n", "Requirement already satisfied: pyarrow>=21.0.0 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from datasets) (23.0.1)\n", "Requirement already satisfied: pytz>=2020.1 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from pandas) (2026.1.post1)\n", "Requirement already satisfied: tzdata>=2022.7 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from pandas) (2025.3)\n", "Requirement already satisfied: python-dateutil>=2.8.2 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from pandas) (2.9.0.post0)\n", "Requirement already satisfied: colorama in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from tqdm) (0.4.6)\n", "Collecting aiohttp!=4.0.0a0,!=4.0.0a1\n", " Using cached aiohttp-3.13.3-cp310-cp310-win_amd64.whl (456 kB)\n", "Collecting frozenlist>=1.1.1\n", " Using cached frozenlist-1.8.0-cp310-cp310-win_amd64.whl (43 kB)\n", "Requirement already satisfied: attrs>=17.3.0 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2026.2.0,>=2023.1.0->datasets) (26.1.0)\n", "Collecting aiosignal>=1.4.0\n", " Using cached aiosignal-1.4.0-py3-none-any.whl (7.5 kB)\n", "Collecting propcache>=0.2.0\n", " Using cached propcache-0.4.1-cp310-cp310-win_amd64.whl (41 kB)\n", "Collecting yarl<2.0,>=1.17.0\n", " Using cached yarl-1.23.0-cp310-cp310-win_amd64.whl (87 kB)\n", "Collecting aiohappyeyeballs>=2.5.0\n", " Using cached aiohappyeyeballs-2.6.1-py3-none-any.whl (15 kB)\n", "Collecting multidict<7.0,>=4.5\n", " Using cached multidict-6.7.1-cp310-cp310-win_amd64.whl (46 kB)\n", "Collecting async-timeout<6.0,>=4.0\n", " Using cached async_timeout-5.0.1-py3-none-any.whl (6.2 kB)\n", "Requirement already satisfied: typing-extensions>=4.2 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from aiosignal>=1.4.0->aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]<=2026.2.0,>=2023.1.0->datasets) (4.15.0)\n", "Collecting anyio\n", " Downloading anyio-4.13.0-py3-none-any.whl (114 kB)\n", "Collecting httpcore==1.*\n", " Using cached httpcore-1.0.9-py3-none-any.whl (78 kB)\n", "Requirement already satisfied: idna in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from httpx<1.0.0->datasets) (3.11)\n", "Requirement already satisfied: certifi in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from httpx<1.0.0->datasets) (2026.2.25)\n", "Collecting h11>=0.16\n", " Using cached h11-0.16.0-py3-none-any.whl (37 kB)\n", "Collecting typer\n", " Using cached typer-0.24.1-py3-none-any.whl (56 kB)\n", "Collecting hf-xet<2.0.0,>=1.4.2\n", " Using cached hf_xet-1.4.2-cp37-abi3-win_amd64.whl (3.7 MB)\n", "Requirement already satisfied: six>=1.5 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n", "Requirement already satisfied: charset_normalizer<4,>=2 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from requests>=2.32.2->datasets) (3.4.6)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from requests>=2.32.2->datasets) (2.6.3)\n", "Requirement already satisfied: exceptiongroup>=1.0.2 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from anyio->httpx<1.0.0->datasets) (1.3.1)\n", "Requirement already satisfied: click>=8.2.1 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from typer->huggingface-hub<2.0,>=0.25.0->datasets) (8.3.1)\n", "Collecting annotated-doc>=0.0.2\n", " Using cached annotated_doc-0.0.4-py3-none-any.whl (5.3 kB)\n", "Collecting shellingham>=1.3.0\n", " Using cached shellingham-1.5.4-py2.py3-none-any.whl (9.8 kB)\n", "Requirement already satisfied: rich>=12.3.0 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from typer->huggingface-hub<2.0,>=0.25.0->datasets) (14.3.3)\n", "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from rich>=12.3.0->typer->huggingface-hub<2.0,>=0.25.0->datasets) (2.19.2)\n", "Requirement already satisfied: markdown-it-py>=2.2.0 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from rich>=12.3.0->typer->huggingface-hub<2.0,>=0.25.0->datasets) (4.0.0)\n", "Requirement already satisfied: mdurl~=0.1 in e:\\srs projects - 2026\\smartvision\\.venv\\lib\\site-packages (from markdown-it-py>=2.2.0->rich>=12.3.0->typer->huggingface-hub<2.0,>=0.25.0->datasets) (0.1.2)\n", "Installing collected packages: propcache, multidict, h11, frozenlist, yarl, shellingham, httpcore, async-timeout, anyio, annotated-doc, aiosignal, aiohappyeyeballs, typer, tqdm, httpx, hf-xet, dill, aiohttp, xxhash, multiprocess, huggingface-hub, datasets\n", "Successfully installed aiohappyeyeballs-2.6.1 aiohttp-3.13.3 aiosignal-1.4.0 annotated-doc-0.0.4 anyio-4.13.0 async-timeout-5.0.1 datasets-4.8.4 dill-0.4.1 frozenlist-1.8.0 h11-0.16.0 hf-xet-1.4.2 httpcore-1.0.9 httpx-0.28.1 huggingface-hub-1.7.2 multidict-6.7.1 multiprocess-0.70.19 propcache-0.4.1 shellingham-1.5.4 tqdm-4.67.3 typer-0.24.1 xxhash-3.6.0 yarl-1.23.0\n", "Note: you may need to restart the kernel to use updated packages.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: You are using pip version 21.2.3; however, version 26.0.1 is available.\n", "You should consider upgrading via the 'e:\\SRS Projects - 2026\\SmartVision\\.venv\\Scripts\\python.exe -m pip install --upgrade pip' command.\n" ] } ], "source": [ "pip install datasets pillow pandas tqdm" ] }, { "cell_type": "code", "execution_count": 2, "id": "c73c91a8", "metadata": { "id": "c73c91a8" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "e:\\SRS Projects - 2026\\SmartVision\\.venv\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "## STEP 1: IMPORTS AND CONFIGURATION\n", "\n", "import os\n", "import json\n", "from datasets import load_dataset\n", "from PIL import Image\n", "from collections import defaultdict\n", "from tqdm import tqdm\n", "import random" ] }, { "cell_type": "markdown", "id": "BYxZpR9EMSjN", "metadata": { "id": "BYxZpR9EMSjN" }, "source": [ "### 25 Selected Classes with COCO Category IDs\n", "\n", "**Vehicles:** car(3), truck(8), bus(6), motorcycle(4), bicycle(2), airplane(5) \n", "**Person:** person(1) \n", "**Outdoor:** traffic light(10), stop sign(13), bench(15) \n", "**Animals:** dog(18), cat(17), horse(19), bird(16), cow(21), elephant(22) \n", "**Kitchen & Food:** bottle(44), cup(47), bowl(51), pizza(59), cake(61) \n", "**Furniture:** chair(62), couch(63), bed(65), potted plant(64)" ] }, { "cell_type": "code", "execution_count": 3, "id": "531afa7c", "metadata": { "id": "531afa7c" }, "outputs": [], "source": [ "# 25 Selected Classes (CORRECT indices from detection-datasets/coco)\n", "\n", "SELECTED_CLASSES = {\n", " 'person': 0,\n", " 'bicycle': 1,\n", " 'car': 2,\n", " 'motorcycle': 3,\n", " 'airplane': 4,\n", " 'bus': 5,\n", " 'train': 6,\n", " 'truck': 7,\n", " 'traffic light': 9,\n", " 'stop sign': 11,\n", " 'bench': 13,\n", " 'bird': 14,\n", " 'cat': 15,\n", " 'dog': 16,\n", " 'horse': 17,\n", " 'cow': 19,\n", " 'elephant': 20,\n", " 'bottle': 39,\n", " 'cup': 41,\n", " 'bowl': 45,\n", " 'pizza': 53,\n", " 'cake': 55,\n", " 'chair': 56,\n", " 'couch': 57,\n", " 'potted plant': 58,\n", " 'bed': 59\n", "}\n", "\n", "IMAGES_PER_CLASS = 100\n", "BASE_DIR = \"smartvision_dataset\"" ] }, { "cell_type": "code", "execution_count": 4, "id": "91700ca9", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 272, "referenced_widgets": [ "ea4ca1e8ff404a5582e1e3539156e241", "995f2f9dcddd4a0780af48863baecf6b", "8e6ecd6d126e48d98c79f1c3ddacca7f", "da4a5f14edc44558aa14a47483fa39da", "a0302ec4177c48a0b9ae0fa9a943d7a6", "c1172a5798d1462c885f51b2ec93ee0e", "44595592c7644cedbccf51cef0c28380", "1bcf3e94f36b4141bf8faf1bf3504bcf", "07a41735942b4874b1e7bff31b83f1e0", "dfaee4f75b6b45b8b27cabd834072fc1", "eb54b29b0cee4cd38bc3a9127bbddd13", "57bacff4917c409cbf2383a82d932143", "fc0dd30888034a5bbcb688cc933589bf", "6be3a48252e1474eb0a518f28368e952", "4bf4421b698649898418baf1a75c1fe9", "7e0f8f1830954e80afa4b4ae6c6cf4f5", "c25a23066ef14c76bd541dd0fdbd1985", "8d55cec8dd7b4f0e82947de951e2089a", "a19e39c93dd942abaf8330d78a747f8e", "e60ef01a3fdd4e598c310a68aa05d8f4", "9027ff7547ad4145bddb0422dff717b1", "18c700bfd12b47d8beb50d96d91298be", "9ad3d91590d4476bbbfc5906d6d53886", "ef8a462adb1f48738c15f751d6ce8c04", "d9ea75c165424ed1a778528ddebc4b6c", "468b72f09f78420ca743c8082ba44cea", "d062883e96524e698727a84de0872b5d", "68e535db3c974d0785750d1c54dc26b0", "15829b9700c349faa4357a53f0c8ff69", "18af9e645acb47aabcb76c946fb888eb", "ae8c7182f5354de9994c6dabb8833ac5", "9f6868d254474d3a8aaf555ba68335b9", "aad289a5723647b080452b4709e4d106" ] }, "id": "91700ca9", "outputId": "f2019775-1649-4e7e-88ce-1eb658b3264a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "πŸ“₯ Loading COCO dataset in STREAMING mode (no download)...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "βœ… Dataset loaded in streaming mode!\n" ] } ], "source": [ "## STEP 2: LOAD COCO DATASET FROM HUGGING FACE\n", "\n", "print(\"πŸ“₯ Loading COCO dataset in STREAMING mode (no download)...\")\n", "dataset = load_dataset(\"detection-datasets/coco\", split=\"train\", streaming=True)\n", "print(\"βœ… Dataset loaded in streaming mode!\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "c3056a4a", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "c3056a4a", "outputId": "927f9325-5a93-438b-87d8-bc1a42cf42ec" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "πŸ” Starting image collection from COCO dataset stream...\n", "🎯 Target: 100 images per class\n", "\n", "⏳ Processing images from stream...\n", "πŸ’‘ Progress updates every 100 images collected\n", "\n", "βœ“ Collected 100/2600 images\n", "βœ“ Collected 200/2600 images\n", "βœ“ Collected 300/2600 images\n", "βœ“ Collected 400/2600 images\n", "βœ“ Collected 500/2600 images\n", "βœ“ Collected 600/2600 images\n", "βœ“ Collected 700/2600 images\n", "βœ“ Collected 800/2600 images\n", "βœ“ Collected 900/2600 images\n", "βœ“ Collected 1000/2600 images\n", "βœ“ Collected 1100/2600 images\n", "βœ“ Collected 1200/2600 images\n", "βœ“ Collected 1300/2600 images\n", "βœ“ Collected 1400/2600 images\n", "βœ“ Collected 1500/2600 images\n", "βœ“ Collected 1600/2600 images\n", "πŸ“Š Processed 1000 images | Collected 1682/2600\n", "βœ“ Collected 1700/2600 images\n", "βœ“ Collected 1800/2600 images\n", "βœ“ Collected 1900/2600 images\n", "βœ“ Collected 2000/2600 images\n", "βœ“ Collected 2100/2600 images\n", "βœ“ Collected 2200/2600 images\n", "βœ“ Collected 2300/2600 images\n", "πŸ“Š Processed 2000 images | Collected 2364/2600\n", "βœ“ Collected 2400/2600 images\n", "βœ“ Collected 2500/2600 images\n", "πŸ“Š Processed 3000 images | Collected 2543/2600\n", "πŸ“Š Processed 4000 images | Collected 2559/2600\n", "πŸ“Š Processed 5000 images | Collected 2574/2600\n", "βœ“ Collected 2600/2600 images\n", "πŸŽ‰ Successfully collected 100 images for ALL classes!\n", "\n", "============================================================\n", "πŸ“Š COLLECTION COMPLETE:\n", "============================================================\n", "Images Processed: 5979\n", "Images Collected: 2600\n", "\n", "βœ… airplane : 100 images\n", "βœ… bed : 100 images\n", "βœ… bench : 100 images\n", "βœ… bicycle : 100 images\n", "βœ… bird : 100 images\n", "βœ… bottle : 100 images\n", "βœ… bowl : 100 images\n", "βœ… bus : 100 images\n", "βœ… cake : 100 images\n", "βœ… car : 100 images\n", "βœ… cat : 100 images\n", "βœ… chair : 100 images\n", "βœ… couch : 100 images\n", "βœ… cow : 100 images\n", "βœ… cup : 100 images\n", "βœ… dog : 100 images\n", "βœ… elephant : 100 images\n", "βœ… horse : 100 images\n", "βœ… motorcycle : 100 images\n", "βœ… person : 100 images\n", "βœ… pizza : 100 images\n", "βœ… potted plant : 100 images\n", "βœ… stop sign : 100 images\n", "βœ… traffic light : 100 images\n", "βœ… train : 100 images\n", "βœ… truck : 100 images\n", "============================================================\n" ] } ], "source": [ "## STEP 3: COLLECT IMAGES FROM STREAM\n", "\n", "print(\"\\nπŸ” Starting image collection from COCO dataset stream...\")\n", "print(f\"🎯 Target: {IMAGES_PER_CLASS} images per class\")\n", "print()\n", "\n", "# Initialize storage for collected images\n", "class_images = {class_name: [] for class_name in SELECTED_CLASSES.keys()}\n", "class_counts = {class_name: 0 for class_name in SELECTED_CLASSES.keys()}\n", "\n", "# Progress tracking\n", "total_collected = 0\n", "images_processed = 0\n", "max_iterations = 50000 # Safety limit\n", "\n", "print(\"⏳ Processing images from stream...\")\n", "print(\"πŸ’‘ Progress updates every 100 images collected\")\n", "print()\n", "\n", "# Iterate through streaming dataset\n", "for idx, item in enumerate(dataset):\n", "\n", " images_processed += 1\n", "\n", " # Progress update every 1000 images processed\n", " if images_processed % 1000 == 0:\n", " print(f\"πŸ“Š Processed {images_processed} images | Collected {total_collected}/{len(SELECTED_CLASSES) * IMAGES_PER_CLASS}\")\n", "\n", " # Safety check\n", " if images_processed >= max_iterations:\n", " print(f\"⚠️ Reached safety limit of {max_iterations} iterations\")\n", " break\n", "\n", " # Check if we have enough images for ALL classes\n", " if all(count >= IMAGES_PER_CLASS for count in class_counts.values()):\n", " print(\"πŸŽ‰ Successfully collected 100 images for ALL classes!\")\n", " break\n", "\n", " # Get annotations from current image\n", " annotations = item['objects']\n", " categories = annotations['category']\n", "\n", " # Check if any of our target classes are in this image\n", " for cat_id in categories:\n", " for class_name, class_id in SELECTED_CLASSES.items():\n", " if cat_id == class_id and class_counts[class_name] < IMAGES_PER_CLASS:\n", "\n", " # Store the ACTUAL image data (not just index!)\n", " class_images[class_name].append({\n", " 'image': item['image'], # PIL Image object\n", " 'annotations': item['objects'], # Annotations\n", " 'idx': images_processed # For naming\n", " })\n", "\n", " class_counts[class_name] += 1\n", " total_collected += 1\n", "\n", " # Progress update every 100 collected\n", " if total_collected % 100 == 0:\n", " print(f\"βœ“ Collected {total_collected}/{len(SELECTED_CLASSES) * IMAGES_PER_CLASS} images\")\n", "\n", " break # Only count once per class\n", "\n", "print()\n", "print(\"=\"*60)\n", "print(\"πŸ“Š COLLECTION COMPLETE:\")\n", "print(\"=\"*60)\n", "print(f\"Images Processed: {images_processed}\")\n", "print(f\"Images Collected: {total_collected}\")\n", "print()\n", "for class_name, count in sorted(class_counts.items()):\n", " status = \"βœ…\" if count >= IMAGES_PER_CLASS else \"⚠️\"\n", " print(f\"{status} {class_name:20s}: {count:3d} images\")\n", "print(\"=\"*60)" ] }, { "cell_type": "code", "execution_count": 6, "id": "216b344d", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "216b344d", "outputId": "a31379de-2872-493b-e326-e1ea4edeb196" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "πŸ“ Creating project folder structure...\n", "\n", "βœ… Folder structure created successfully!\n", "\n", "πŸ“‚ Structure:\n", "\n", "smartvision_dataset/\n", "β”œβ”€β”€ classification/\n", "β”‚ β”œβ”€β”€ train/\n", "β”‚ β”‚ β”œβ”€β”€ person/\n", "β”‚ β”‚ β”œβ”€β”€ car/\n", "β”‚ β”‚ └── ... (25 class folders)\n", "β”‚ β”œβ”€β”€ val/\n", "β”‚ β”‚ └── ... (25 class folders)\n", "β”‚ └── test/\n", "β”‚ └── ... (25 class folders)\n", "β”‚\n", "└── detection/\n", " β”œβ”€β”€ images/\n", " └── labels/\n", "\n" ] } ], "source": [ "## STEP 4: CREATE FOLDER STRUCTURE\n", "\n", "print(\"\\nπŸ“ Creating project folder structure...\")\n", "print()\n", "\n", "# Create main directory\n", "os.makedirs(BASE_DIR, exist_ok=True)\n", "\n", "# Create subdirectories for Classification task\n", "os.makedirs(f\"{BASE_DIR}/classification/train\", exist_ok=True)\n", "os.makedirs(f\"{BASE_DIR}/classification/val\", exist_ok=True)\n", "os.makedirs(f\"{BASE_DIR}/classification/test\", exist_ok=True)\n", "\n", "# Create subdirectories for Detection task\n", "os.makedirs(f\"{BASE_DIR}/detection/images\", exist_ok=True)\n", "os.makedirs(f\"{BASE_DIR}/detection/labels\", exist_ok=True)\n", "\n", "# Create class folders inside train/val/test\n", "for class_name in SELECTED_CLASSES.keys():\n", " os.makedirs(f\"{BASE_DIR}/classification/train/{class_name}\", exist_ok=True)\n", " os.makedirs(f\"{BASE_DIR}/classification/val/{class_name}\", exist_ok=True)\n", " os.makedirs(f\"{BASE_DIR}/classification/test/{class_name}\", exist_ok=True)\n", "\n", "print(\"βœ… Folder structure created successfully!\")\n", "print()\n", "print(\"πŸ“‚ Structure:\")\n", "print(f\"\"\"\n", "{BASE_DIR}/\n", "β”œβ”€β”€ classification/\n", "β”‚ β”œβ”€β”€ train/\n", "β”‚ β”‚ β”œβ”€β”€ person/\n", "β”‚ β”‚ β”œβ”€β”€ car/\n", "β”‚ β”‚ └── ... (25 class folders)\n", "β”‚ β”œβ”€β”€ val/\n", "β”‚ β”‚ └── ... (25 class folders)\n", "β”‚ └── test/\n", "β”‚ └── ... (25 class folders)\n", "β”‚\n", "└── detection/\n", " β”œβ”€β”€ images/\n", " └── labels/\n", "\"\"\")" ] }, { "cell_type": "code", "execution_count": 7, "id": "d99b88ce", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "d99b88ce", "outputId": "caa6e5c1-17e4-46b7-8438-97f41e20309b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "======================================================================\n", "πŸ”€ Preparing Train/Val/Test splits...\n", "πŸ“Š Split Ratio: 70% Train / 15% Val / 15% Test\n", "======================================================================\n", "\n", "person : Train= 70 | Val=15 | Test=15\n", "bicycle : Train= 70 | Val=15 | Test=15\n", "car : Train= 70 | Val=15 | Test=15\n", "motorcycle : Train= 70 | Val=15 | Test=15\n", "airplane : Train= 70 | Val=15 | Test=15\n", "bus : Train= 70 | Val=15 | Test=15\n", "train : Train= 70 | Val=15 | Test=15\n", "truck : Train= 70 | Val=15 | Test=15\n", "traffic light : Train= 70 | Val=15 | Test=15\n", "stop sign : Train= 70 | Val=15 | Test=15\n", "bench : Train= 70 | Val=15 | Test=15\n", "bird : Train= 70 | Val=15 | Test=15\n", "cat : Train= 70 | Val=15 | Test=15\n", "dog : Train= 70 | Val=15 | Test=15\n", "horse : Train= 70 | Val=15 | Test=15\n", "cow : Train= 70 | Val=15 | Test=15\n", "elephant : Train= 70 | Val=15 | Test=15\n", "bottle : Train= 70 | Val=15 | Test=15\n", "cup : Train= 70 | Val=15 | Test=15\n", "bowl : Train= 70 | Val=15 | Test=15\n", "pizza : Train= 70 | Val=15 | Test=15\n", "cake : Train= 70 | Val=15 | Test=15\n", "chair : Train= 70 | Val=15 | Test=15\n", "couch : Train= 70 | Val=15 | Test=15\n", "potted plant : Train= 70 | Val=15 | Test=15\n", "bed : Train= 70 | Val=15 | Test=15\n" ] } ], "source": [ "## STEP 5: TRAIN/VAL/TEST SPLIT (70/15/15)\n", "\n", "print(\"=\"*70)\n", "print(\"πŸ”€ Preparing Train/Val/Test splits...\")\n", "print(\"πŸ“Š Split Ratio: 70% Train / 15% Val / 15% Test\")\n", "print(\"=\"*70)\n", "print()\n", "\n", "# Initialize metadata dictionary\n", "metadata = {\n", " 'total_images': 0,\n", " 'classes': {},\n", " 'splits': {'train': 0, 'val': 0, 'test': 0}\n", "}\n", "\n", "# Create split dictionaries for each class\n", "train_data = {}\n", "val_data = {}\n", "test_data = {}\n", "\n", "# Process each class\n", "for class_name in SELECTED_CLASSES.keys():\n", "\n", " all_items = class_images.get(class_name, [])\n", "\n", " if not all_items:\n", " print(f\"⚠️ Warning: No images found for {class_name}\")\n", " continue\n", "\n", " # Calculate split indices\n", " n = len(all_items)\n", " train_split = int(0.7 * n) # 70% for training\n", " val_split = int(0.85 * n) # 15% for validation\n", " # Remaining 15% for test\n", "\n", " # Split the data\n", " train_data[class_name] = all_items[:train_split]\n", " val_data[class_name] = all_items[train_split:val_split]\n", " test_data[class_name] = all_items[val_split:]\n", "\n", " # Store split info in metadata\n", " metadata['classes'][class_name] = {\n", " 'train': len(train_data[class_name]),\n", " 'val': len(val_data[class_name]),\n", " 'test': len(test_data[class_name]),\n", " 'total': len(all_items)\n", " }\n", "\n", " metadata['splits']['train'] += len(train_data[class_name])\n", " metadata['splits']['val'] += len(val_data[class_name])\n", " metadata['splits']['test'] += len(test_data[class_name])\n", " metadata['total_images'] += len(all_items)\n", "\n", " print(f\"{class_name:20s}: Train={len(train_data[class_name]):3d} | Val={len(val_data[class_name]):2d} | Test={len(test_data[class_name]):2d}\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "48556611", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "48556611", "outputId": "3380e0d9-ba72-4af5-891b-10ca05d932cc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "======================================================================\n", "πŸ’Ύ STEP 6: SAVING IMAGES TO DISK\n", "======================================================================\n", "\n", "πŸ“ PART A: Saving Classification Images...\n", " Format: Cropped objects, 224x224 pixels\n", "\n", "πŸ“‚ Processing TRAIN split...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " train: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 26/26 [00:07<00:00, 3.65it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "πŸ“‚ Processing VAL split...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " val: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 26/26 [00:01<00:00, 17.40it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "πŸ“‚ Processing TEST split...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " test: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 26/26 [00:01<00:00, 17.89it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "======================================================================\n", "βœ… CLASSIFICATION IMAGES SAVED!\n", "======================================================================\n", "πŸ“Š Train: 1820 images\n", "πŸ“Š Val: 390 images\n", "πŸ“Š Test: 390 images\n", "πŸ“Š Total: 2600 images\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "import os\n", "from PIL import Image\n", "from tqdm import tqdm\n", "import json\n", "\n", "print(\"=\"*70)\n", "print(\"πŸ’Ύ STEP 6: SAVING IMAGES TO DISK\")\n", "print(\"=\"*70)\n", "print()\n", "\n", "# PART A: SAVE CLASSIFICATION IMAGES\n", "\n", "\n", "print(\"πŸ“ PART A: Saving Classification Images...\")\n", "print(\" Format: Cropped objects, 224x224 pixels\\n\")\n", "\n", "classification_stats = {'train': 0, 'val': 0, 'test': 0}\n", "\n", "# Process each split\n", "for split_name, split_data in [('train', train_data), ('val', val_data), ('test', test_data)]:\n", "\n", " print(f\"πŸ“‚ Processing {split_name.upper()} split...\")\n", "\n", " # Process each class\n", " for class_name, items in tqdm(split_data.items(), desc=f\" {split_name}\"):\n", "\n", " class_folder = f\"{BASE_DIR}/classification/{split_name}/{class_name}\"\n", "\n", " # Save each image\n", " for img_idx, item in enumerate(items):\n", "\n", " img = item['image']\n", " annotations = item['annotations']\n", " bboxes = annotations['bbox']\n", " categories = annotations['category']\n", "\n", " class_id = SELECTED_CLASSES[class_name]\n", "\n", " # Find bbox for this class\n", " for bbox, cat_id in zip(bboxes, categories):\n", " if cat_id == class_id:\n", " x, y, w, h = bbox\n", "\n", " try:\n", " # Crop and resize\n", " cropped_img = img.crop((x, y, x + w, y + h))\n", " cropped_img = cropped_img.resize((224, 224), Image.LANCZOS)\n", "\n", " # Save\n", " img_filename = f\"{class_name}_{split_name}_{img_idx:04d}.jpg\"\n", " img_path = os.path.join(class_folder, img_filename)\n", " cropped_img.save(img_path, quality=95)\n", "\n", " classification_stats[split_name] += 1\n", "\n", " except Exception as e:\n", " print(f\"⚠️ Error: {class_name} image {img_idx}: {e}\")\n", "\n", " break\n", "\n", "print()\n", "print(\"=\"*70)\n", "print(\"βœ… CLASSIFICATION IMAGES SAVED!\")\n", "print(\"=\"*70)\n", "print(f\"πŸ“Š Train: {classification_stats['train']} images\")\n", "print(f\"πŸ“Š Val: {classification_stats['val']} images\")\n", "print(f\"πŸ“Š Test: {classification_stats['test']} images\")\n", "print(f\"πŸ“Š Total: {sum(classification_stats.values())} images\")\n", "print()" ] }, { "cell_type": "code", "execution_count": 9, "id": "df35bbc9", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "df35bbc9", "outputId": "7e0720a1-3e14-434e-bb81-dde27a6a1460" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "======================================================================\n", "πŸ“ PART B: Saving Detection Images & Annotations...\n", " Format: Full images with YOLO .txt labels\n", "\n", "πŸ“Š Total detection images: 2210\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Saving detection data: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2210/2210 [00:08<00:00, 271.46it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "======================================================================\n", "βœ… DETECTION DATASET CREATED!\n", "======================================================================\n", "πŸ“Š Images: 2210\n", "πŸ“Š Labels: 2210\n", "πŸ“Š Objects: 22163\n", "πŸ“Š Avg/image: 10.03\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# PART B: SAVE DETECTION IMAGES (YOLO FORMAT)\n", "\n", "print(\"=\"*70)\n", "print(\"πŸ“ PART B: Saving Detection Images & Annotations...\")\n", "print(\" Format: Full images with YOLO .txt labels\\n\")\n", "\n", "detection_stats = {'images': 0, 'annotations': 0, 'objects': 0}\n", "\n", "# COCO to YOLO class mapping\n", "coco_to_yolo = {class_id: idx for idx, class_id in enumerate(SELECTED_CLASSES.values())}\n", "\n", "# Combine train + val for detection\n", "all_detection_data = []\n", "for class_name in SELECTED_CLASSES.keys():\n", " all_detection_data.extend(train_data.get(class_name, []))\n", " all_detection_data.extend(val_data.get(class_name, []))\n", "\n", "print(f\"πŸ“Š Total detection images: {len(all_detection_data)}\\n\")\n", "\n", "# Save images and create YOLO labels\n", "for img_idx, item in enumerate(tqdm(all_detection_data, desc=\"Saving detection data\")):\n", "\n", " img = item['image']\n", " img_width, img_height = img.size\n", "\n", " # Save full image\n", " img_filename = f\"image_{img_idx:06d}.jpg\"\n", " img_path = os.path.join(f\"{BASE_DIR}/detection/images\", img_filename)\n", " img.save(img_path, quality=95)\n", " detection_stats['images'] += 1\n", "\n", " # Get annotations\n", " annotations = item['annotations']\n", " bboxes = annotations['bbox']\n", " categories = annotations['category']\n", "\n", " # Create YOLO annotation\n", " label_filename = f\"image_{img_idx:06d}.txt\"\n", " label_path = os.path.join(f\"{BASE_DIR}/detection/labels\", label_filename)\n", "\n", " yolo_annotations = []\n", " objects_count = 0\n", "\n", " for bbox, cat_id in zip(bboxes, categories):\n", " if cat_id in coco_to_yolo:\n", " x, y, w, h = bbox\n", "\n", " # Convert to YOLO format (normalized)\n", " x_center = (x + w/2) / img_width\n", " y_center = (y + h/2) / img_height\n", " w_norm = w / img_width\n", " h_norm = h / img_height\n", "\n", " yolo_class_id = coco_to_yolo[cat_id]\n", " yolo_line = f\"{yolo_class_id} {x_center:.6f} {y_center:.6f} {w_norm:.6f} {h_norm:.6f}\"\n", " yolo_annotations.append(yolo_line)\n", " objects_count += 1\n", "\n", " # Save label file\n", " if yolo_annotations:\n", " with open(label_path, 'w') as f:\n", " f.write('\\n'.join(yolo_annotations))\n", " detection_stats['annotations'] += 1\n", " detection_stats['objects'] += objects_count\n", "\n", "print()\n", "print(\"=\"*70)\n", "print(\"βœ… DETECTION DATASET CREATED!\")\n", "print(\"=\"*70)\n", "print(f\"πŸ“Š Images: {detection_stats['images']}\")\n", "print(f\"πŸ“Š Labels: {detection_stats['annotations']}\")\n", "print(f\"πŸ“Š Objects: {detection_stats['objects']}\")\n", "print(f\"πŸ“Š Avg/image: {detection_stats['objects']/detection_stats['images']:.2f}\")\n", "print()" ] }, { "cell_type": "code", "execution_count": 10, "id": "f9222e7a", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "f9222e7a", "outputId": "c3f6ebb2-0941-4341-b52b-5859f10f8431" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "πŸ“ Creating YOLO configuration file...\n", "\n", "βœ… Created: smartvision_dataset/detection/data.yaml\n", "\n" ] } ], "source": [ "# PART C: CREATE YOLO CONFIG FILE\n", "\n", "print(\"πŸ“ Creating YOLO configuration file...\\n\")\n", "\n", "yaml_content = f\"\"\"# SmartVision Dataset - YOLOv8 Configuration\n", "path: {os.path.abspath(BASE_DIR)}/detection\n", "train: images\n", "val: images\n", "\n", "names:\n", " 0: person\n", " 1: bicycle\n", " 2: car\n", " 3: motorcycle\n", " 4: airplane\n", " 5: bus\n", " 6: train\n", " 7: truck\n", " 8: traffic light\n", " 9: stop sign\n", " 10: bench\n", " 11: bird\n", " 12: cat\n", " 13: dog\n", " 14: horse\n", " 15: cow\n", " 16: elephant\n", " 17: bottle\n", " 18: cup\n", " 19: bowl\n", " 20: pizza\n", " 21: cake\n", " 22: chair\n", " 23: couch\n", " 24: potted plant\n", " 25: bed\n", "\n", "nc: 26\n", "\"\"\"\n", "\n", "yaml_path = f\"{BASE_DIR}/detection/data.yaml\"\n", "with open(yaml_path, 'w') as f:\n", " f.write(yaml_content)\n", "\n", "print(f\"βœ… Created: {yaml_path}\\n\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "6a2390e9", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6a2390e9", "outputId": "4d9f9ace-efb5-4f76-90ad-0e584d988caf" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "πŸ“Š Saving metadata...\n", "\n", "βœ… Saved: smartvision_dataset/dataset_metadata.json\n", "\n" ] } ], "source": [ "# PART D: SAVE METADATA\n", "\n", "print(\"πŸ“Š Saving metadata...\\n\")\n", "\n", "metadata['classification'] = classification_stats\n", "metadata['detection'] = detection_stats\n", "metadata['dataset_path'] = os.path.abspath(BASE_DIR)\n", "\n", "metadata_path = f\"{BASE_DIR}/dataset_metadata.json\"\n", "with open(metadata_path, 'w') as f:\n", " json.dump(metadata, indent=2, fp=f)\n", "\n", "print(f\"βœ… Saved: {metadata_path}\\n\")" ] }, { "cell_type": "code", "execution_count": 12, "id": "f216bcbb", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "f216bcbb", "outputId": "f64da9ac-8cf6-4a63-ac6d-dd1cbbcca19a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "======================================================================\n", "πŸŽ‰ DATASET SETUP COMPLETE!\n", "======================================================================\n", "\n", "πŸ“ Location: e:\\SRS Projects - 2026\\SmartVision\\smartvision_dataset\n", "\n", "πŸ“‚ Classification Dataset:\n", " β”œβ”€ Train: 1820 images (70%)\n", " β”œβ”€ Val: 390 images (15%)\n", " β”œβ”€ Test: 390 images (15%)\n", " └─ Total: 2600 cropped images (224x224)\n", "\n", "πŸ“‚ Detection Dataset:\n", " β”œβ”€ Images: 2210 full images\n", " β”œβ”€ Labels: 2210 YOLO .txt files\n", " └─ Objects: 22163 annotated objects\n", "\n", "======================================================================\n", "βœ… LEARNERS CAN NOW START:\n", "======================================================================\n", "Step 7: Exploratory Data Analysis (EDA)\n", "Step 8: Train Classification Models\n", "Step 9: Train YOLO Detection Model\n", "Step 10: Build Streamlit Application\n", "Step 11: Deploy to Hugging Face Spaces\n", "======================================================================\n" ] } ], "source": [ "print(\"=\"*70)\n", "print(\"πŸŽ‰ DATASET SETUP COMPLETE!\")\n", "print(\"=\"*70)\n", "print()\n", "print(f\"πŸ“ Location: {os.path.abspath(BASE_DIR)}\")\n", "print()\n", "print(\"πŸ“‚ Classification Dataset:\")\n", "print(f\" β”œβ”€ Train: {classification_stats['train']} images (70%)\")\n", "print(f\" β”œβ”€ Val: {classification_stats['val']} images (15%)\")\n", "print(f\" β”œβ”€ Test: {classification_stats['test']} images (15%)\")\n", "print(f\" └─ Total: {sum(classification_stats.values())} cropped images (224x224)\")\n", "print()\n", "print(\"πŸ“‚ Detection Dataset:\")\n", "print(f\" β”œβ”€ Images: {detection_stats['images']} full images\")\n", "print(f\" β”œβ”€ Labels: {detection_stats['annotations']} YOLO .txt files\")\n", "print(f\" └─ Objects: {detection_stats['objects']} annotated objects\")\n", "print()\n", "print(\"=\"*70)\n", "print(\"βœ… LEARNERS CAN NOW START:\")\n", "print(\"=\"*70)\n", "print(\"Step 7: Exploratory Data Analysis (EDA)\")\n", "print(\"Step 8: Train Classification Models\")\n", "print(\"Step 9: Train YOLO Detection Model\")\n", "print(\"Step 10: Build Streamlit Application\")\n", "print(\"Step 11: Deploy to Hugging Face Spaces\")\n", "print(\"=\"*70)" ] }, { "cell_type": "markdown", "id": "Rz-JCMgW8HMY", "metadata": { "id": "Rz-JCMgW8HMY" }, "source": [ "### **Exploratory Data Analysis (EDA)**" ] }, { "cell_type": "markdown", "id": "vbOhJUvD0gM9", "metadata": { "id": "vbOhJUvD0gM9" }, "source": [ "STEP 7 β€” Classification Dataset EDA" ] }, { "cell_type": "code", "execution_count": 14, "id": "980b4836", "metadata": { "id": "980b4836" }, "outputs": [], "source": [ "import os\n", "import random\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from PIL import Image\n", "from collections import defaultdict\n", "\n", "BASE_DIR = \"smartvision_dataset\"" ] }, { "cell_type": "markdown", "id": "lR_aZyXy0qjk", "metadata": { "id": "lR_aZyXy0qjk" }, "source": [ "Class Distribution (Train / Val / Test)" ] }, { "cell_type": "code", "execution_count": 15, "id": "7mTqIFNt0nmc", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "7mTqIFNt0nmc", "outputId": "e2d47f75-9e6d-499f-827b-32fc5ccf20e6" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "============================================================\n", "πŸ“Š CLASSIFICATION DATASET DISTRIBUTION\n", "============================================================\n", "\n", "TRAIN SET:\n", "airplane : 70\n", "bed : 70\n", "bench : 70\n", "bicycle : 70\n", "bird : 70\n", "bottle : 70\n", "bowl : 70\n", "bus : 70\n", "cake : 70\n", "car : 70\n", "cat : 70\n", "chair : 70\n", "couch : 70\n", "cow : 70\n", "cup : 70\n", "dog : 70\n", "elephant : 70\n", "horse : 70\n", "motorcycle : 70\n", "person : 70\n", "pizza : 70\n", "potted plant : 70\n", "stop sign : 70\n", "traffic light : 70\n", "train : 70\n", "truck : 70\n", "\n", "VAL SET:\n", "airplane : 15\n", "bed : 15\n", "bench : 15\n", "bicycle : 15\n", "bird : 15\n", "bottle : 15\n", "bowl : 15\n", "bus : 15\n", "cake : 15\n", "car : 15\n", "cat : 15\n", "chair : 15\n", "couch : 15\n", "cow : 15\n", "cup : 15\n", "dog : 15\n", "elephant : 15\n", "horse : 15\n", "motorcycle : 15\n", "person : 15\n", "pizza : 15\n", "potted plant : 15\n", "stop sign : 15\n", "traffic light : 15\n", "train : 15\n", "truck : 15\n", "\n", "TEST SET:\n", "airplane : 15\n", "bed : 15\n", "bench : 15\n", "bicycle : 15\n", "bird : 15\n", "bottle : 15\n", "bowl : 15\n", "bus : 15\n", "cake : 15\n", "car : 15\n", "cat : 15\n", "chair : 15\n", "couch : 15\n", "cow : 15\n", "cup : 15\n", "dog : 15\n", "elephant : 15\n", "horse : 15\n", "motorcycle : 15\n", "person : 15\n", "pizza : 15\n", "potted plant : 15\n", "stop sign : 15\n", "traffic light : 15\n", "train : 15\n", "truck : 15\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"=\"*60)\n", "print(\"πŸ“Š CLASSIFICATION DATASET DISTRIBUTION\")\n", "print(\"=\"*60)\n", "\n", "splits = ['train', 'val', 'test']\n", "class_distribution = {}\n", "\n", "for split in splits:\n", " split_path = os.path.join(BASE_DIR, \"classification\", split)\n", " class_counts = {}\n", "\n", " for class_name in os.listdir(split_path):\n", " class_folder = os.path.join(split_path, class_name)\n", " if os.path.isdir(class_folder):\n", " class_counts[class_name] = len(os.listdir(class_folder))\n", "\n", " class_distribution[split] = class_counts\n", "\n", " print(f\"\\n{split.upper()} SET:\")\n", " for k, v in sorted(class_counts.items()):\n", " print(f\"{k:20s}: {v}\")\n", "\n", "# Plot Train Distribution\n", "plt.figure(figsize=(14,6))\n", "sns.barplot(\n", " x=list(class_distribution['train'].keys()),\n", " y=list(class_distribution['train'].values())\n", ")\n", "plt.xticks(rotation=45)\n", "plt.title(\"Train Set Class Distribution\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "WaexFd9x0vHs", "metadata": { "id": "WaexFd9x0vHs" }, "source": [ "Sample Images Visualization" ] }, { "cell_type": "code", "execution_count": 16, "id": "VmaKQQ9a0yec", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 378 }, "id": "VmaKQQ9a0yec", "outputId": "49416eb4-f17c-4374-f9b9-64fc2a52fdd9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "πŸ“· Showing Sample Images\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"\\nπŸ“· Showing Sample Images\")\n", "\n", "train_path = os.path.join(BASE_DIR, \"classification\", \"train\")\n", "\n", "plt.figure(figsize=(20,10))\n", "\n", "classes = list(os.listdir(train_path))[:10] # show first 10 classes\n", "\n", "for i, class_name in enumerate(classes):\n", " class_folder = os.path.join(train_path, class_name)\n", " img_name = random.choice(os.listdir(class_folder))\n", " img_path = os.path.join(class_folder, img_name)\n", "\n", " img = Image.open(img_path)\n", "\n", " plt.subplot(2,5,i+1)\n", " plt.imshow(img)\n", " plt.title(class_name)\n", " plt.axis(\"off\")\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "UUwVbg9E0227", "metadata": { "id": "UUwVbg9E0227" }, "source": [ "Image Size Statistics" ] }, { "cell_type": "code", "execution_count": 17, "id": "PJG65_vd05NT", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PJG65_vd05NT", "outputId": "dc11ee75-c7f0-4485-d330-9428c7eb5b18" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "πŸ“ IMAGE SIZE ANALYSIS\n", "Min Width: 224\n", "Max Width: 224\n", "Mean Width: 224.0\n", "Min Height: 224\n", "Max Height: 224\n", "Mean Height: 224.0\n" ] } ], "source": [ "print(\"\\nπŸ“ IMAGE SIZE ANALYSIS\")\n", "\n", "widths = []\n", "heights = []\n", "\n", "for split in splits:\n", " split_path = os.path.join(BASE_DIR, \"classification\", split)\n", "\n", " for class_name in os.listdir(split_path):\n", " class_folder = os.path.join(split_path, class_name)\n", "\n", " for img_file in os.listdir(class_folder):\n", " img_path = os.path.join(class_folder, img_file)\n", " img = Image.open(img_path)\n", " w, h = img.size\n", " widths.append(w)\n", " heights.append(h)\n", "\n", "print(f\"Min Width: {min(widths)}\")\n", "print(f\"Max Width: {max(widths)}\")\n", "print(f\"Mean Width: {np.mean(widths)}\")\n", "\n", "print(f\"Min Height: {min(heights)}\")\n", "print(f\"Max Height: {max(heights)}\")\n", "print(f\"Mean Height: {np.mean(heights)}\")" ] }, { "cell_type": "markdown", "id": "-R_nt4BT09nz", "metadata": { "id": "-R_nt4BT09nz" }, "source": [ "Detection Dataset EDA (YOLO Format)" ] }, { "cell_type": "code", "execution_count": 18, "id": "n6e1fUQD09ar", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "n6e1fUQD09ar", "outputId": "cc9ffe2e-e9fe-414f-88c7-9c2d3bfa15bd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "============================================================\n", "πŸ“¦ DETECTION DATASET OVERVIEW\n", "============================================================\n", "Total Images: 2210\n", "Total Label Files: 2210\n" ] } ], "source": [ "print(\"=\"*60)\n", "print(\"πŸ“¦ DETECTION DATASET OVERVIEW\")\n", "print(\"=\"*60)\n", "\n", "image_dir = os.path.join(BASE_DIR, \"detection\", \"images\")\n", "label_dir = os.path.join(BASE_DIR, \"detection\", \"labels\")\n", "\n", "images = os.listdir(image_dir)\n", "labels = os.listdir(label_dir)\n", "\n", "print(f\"Total Images: {len(images)}\")\n", "print(f\"Total Label Files: {len(labels)}\")" ] }, { "cell_type": "markdown", "id": "PYNzZnZc1Df7", "metadata": { "id": "PYNzZnZc1Df7" }, "source": [ "Object Count Per Class" ] }, { "cell_type": "code", "execution_count": 19, "id": "ArnlbPv71GbL", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 927 }, "id": "ArnlbPv71GbL", "outputId": "fe03f6d3-b9de-4cdb-a103-be29975c83d2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "πŸ“Š Object Distribution Per Class\n", "\n", "Objects per Class ID:\n", "Class 0: 6502\n", "Class 1: 656\n", "Class 2: 2187\n", "Class 3: 484\n", "Class 4: 302\n", "Class 5: 515\n", "Class 6: 257\n", "Class 7: 573\n", "Class 8: 892\n", "Class 9: 105\n", "Class 10: 403\n", "Class 11: 862\n", "Class 12: 162\n", "Class 13: 359\n", "Class 14: 405\n", "Class 15: 374\n", "Class 16: 435\n", "Class 17: 1242\n", "Class 18: 1265\n", "Class 19: 633\n", "Class 20: 276\n", "Class 21: 828\n", "Class 22: 1536\n", "Class 23: 268\n", "Class 24: 519\n", "Class 25: 123\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"\\nπŸ“Š Object Distribution Per Class\")\n", "\n", "class_counts = defaultdict(int)\n", "objects_per_image = []\n", "\n", "for label_file in labels:\n", " label_path = os.path.join(label_dir, label_file)\n", "\n", " with open(label_path, 'r') as f:\n", " lines = f.readlines()\n", " objects_per_image.append(len(lines))\n", "\n", " for line in lines:\n", " class_id = int(line.split()[0])\n", " class_counts[class_id] += 1\n", "\n", "print(\"\\nObjects per Class ID:\")\n", "for k, v in sorted(class_counts.items()):\n", " print(f\"Class {k}: {v}\")\n", "\n", "# Plot\n", "plt.figure(figsize=(12,6))\n", "sns.barplot(\n", " x=list(class_counts.keys()),\n", " y=list(class_counts.values())\n", ")\n", "plt.title(\"Detection Object Count per Class\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "q0djUUk81JeC", "metadata": { "id": "q0djUUk81JeC" }, "source": [ "Object Count Per Class" ] }, { "cell_type": "code", "execution_count": 20, "id": "WZL-Lv4M5dWP", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 927 }, "id": "WZL-Lv4M5dWP", "outputId": "398c025b-3eb7-4582-f9cb-17f450f3a996" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "πŸ“Š Object Distribution Per Class\n", "\n", "Objects per Class ID:\n", "Class 0: 6502\n", "Class 1: 656\n", "Class 2: 2187\n", "Class 3: 484\n", "Class 4: 302\n", "Class 5: 515\n", "Class 6: 257\n", "Class 7: 573\n", "Class 8: 892\n", "Class 9: 105\n", "Class 10: 403\n", "Class 11: 862\n", "Class 12: 162\n", "Class 13: 359\n", "Class 14: 405\n", "Class 15: 374\n", "Class 16: 435\n", "Class 17: 1242\n", "Class 18: 1265\n", "Class 19: 633\n", "Class 20: 276\n", "Class 21: 828\n", "Class 22: 1536\n", "Class 23: 268\n", "Class 24: 519\n", "Class 25: 123\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(\"\\nπŸ“Š Object Distribution Per Class\")\n", "\n", "class_counts = defaultdict(int)\n", "objects_per_image = []\n", "\n", "for label_file in labels:\n", " label_path = os.path.join(label_dir, label_file)\n", "\n", " with open(label_path, 'r') as f:\n", " lines = f.readlines()\n", " objects_per_image.append(len(lines))\n", "\n", " for line in lines:\n", " class_id = int(line.split()[0])\n", " class_counts[class_id] += 1\n", "\n", "print(\"\\nObjects per Class ID:\")\n", "for k, v in sorted(class_counts.items()):\n", " print(f\"Class {k}: {v}\")\n", "\n", "# Plot\n", "plt.figure(figsize=(12,6))\n", "sns.barplot(\n", " x=list(class_counts.keys()),\n", " y=list(class_counts.values())\n", ")\n", "plt.title(\"Detection Object Count per Class\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "DK-jPelk5iMn", "metadata": { "id": "DK-jPelk5iMn" }, "source": [ "Objects Per Image Distribution" ] }, { "cell_type": "code", "execution_count": 21, "id": "p3ALYI4i5lA-", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 440 }, "id": "p3ALYI4i5lA-", "outputId": "141aef69-a1c2-41ee-c882-924564273ea5" }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Average objects per image: 10.03\n" ] } ], "source": [ "plt.figure(figsize=(10,5))\n", "plt.hist(objects_per_image, bins=20)\n", "plt.title(\"Objects Per Image Distribution\")\n", "plt.xlabel(\"Number of Objects\")\n", "plt.ylabel(\"Number of Images\")\n", "plt.show()\n", "\n", "print(f\"Average objects per image: {np.mean(objects_per_image):.2f}\")" ] }, { "cell_type": "markdown", "id": "PIe7eToR5qr-", "metadata": { "id": "PIe7eToR5qr-" }, "source": [ "Visualize YOLO Bounding Boxes" ] }, { "cell_type": "code", "execution_count": 23, "id": "NO4axlru5w3f", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NO4axlru5w3f", "outputId": "c72615f8-f152-4d61-c692-d18d205d5d96" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting opencv-python\n", " Downloading opencv_python-4.13.0.92-cp37-abi3-manylinux_2_28_x86_64.whl.metadata (19 kB)\n", "Requirement already satisfied: numpy>=2 in /usr/local/lib/python3.12/dist-packages (from opencv-python) (2.0.2)\n", "Downloading opencv_python-4.13.0.92-cp37-abi3-manylinux_2_28_x86_64.whl (72.9 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.9/72.9 MB\u001b[0m \u001b[31m16.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: opencv-python\n", "Successfully installed opencv-python-4.13.0.92\n" ] } ], "source": [ "pip install opencv-python" ] }, { "cell_type": "code", "execution_count": 22, "id": "ifXNIy5h5rmu", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 566 }, "id": "ifXNIy5h5rmu", "outputId": "2e966a03-35f3-4b31-df61-2d4a369520cd" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<>:3: SyntaxWarning: invalid escape sequence '\\V'\n", "<>:3: SyntaxWarning: invalid escape sequence '\\V'\n", "/tmp/ipython-input-374566210.py:3: SyntaxWarning: invalid escape sequence '\\V'\n", " print(\"\\Visualizing Sample Detection Image with Bounding Boxes\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\\Visualizing Sample Detection Image with Bounding Boxes\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import cv2\n", "\n", "print(\"\\Visualizing Sample Detection Image with Bounding Boxes\")\n", "\n", "sample_image = random.choice(images)\n", "img_path = os.path.join(image_dir, sample_image)\n", "label_path = os.path.join(label_dir, sample_image.replace(\".jpg\", \".txt\"))\n", "\n", "img = cv2.imread(img_path)\n", "h, w, _ = img.shape\n", "\n", "with open(label_path, 'r') as f:\n", " lines = f.readlines()\n", "\n", "for line in lines:\n", " class_id, x_center, y_center, bw, bh = map(float, line.split())\n", "\n", " x_center *= w\n", " y_center *= h\n", " bw *= w\n", " bh *= h\n", "\n", " x1 = int(x_center - bw/2)\n", " y1 = int(y_center - bh/2)\n", " x2 = int(x_center + bw/2)\n", " y2 = int(y_center + bh/2)\n", "\n", " cv2.rectangle(img, (x1,y1), (x2,y2), (0,255,0), 2)\n", " cv2.putText(img, str(int(class_id)), (x1,y1-5),\n", " cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,255,0), 2)\n", "\n", "plt.figure(figsize=(8,8))\n", "plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n", "plt.axis(\"off\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "QWhsVDJ06GnN", "metadata": { "id": "QWhsVDJ06GnN" }, "source": [ "STEP 8 β€” Train Classification Models (Transfer Learning)" ] }, { "cell_type": "code", "execution_count": null, "id": "aL4bXLgB6KsV", "metadata": { "id": "aL4bXLgB6KsV" }, "outputs": [], "source": [ "!pip install tensorflow --quiet" ] }, { "cell_type": "code", "execution_count": 24, "id": "uq8-HuKE6e0N", "metadata": { "id": "uq8-HuKE6e0N" }, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import tensorflow as tf\n", "\n", "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", "from tensorflow.keras.models import Model\n", "from tensorflow.keras.layers import Dense, Dropout, GlobalAveragePooling2D\n", "from tensorflow.keras.optimizers import Adam\n", "\n", "from tensorflow.keras.applications import VGG16, ResNet50, MobileNetV2, EfficientNetB0" ] }, { "cell_type": "markdown", "id": "nWTizQP26gmO", "metadata": { "id": "nWTizQP26gmO" }, "source": [ "Data Generators" ] }, { "cell_type": "code", "execution_count": 25, "id": "rKexeVQ06jHk", "metadata": { "id": "rKexeVQ06jHk" }, "outputs": [], "source": [ "BASE_DIR = \"smartvision_dataset\"\n", "\n", "train_dir = os.path.join(BASE_DIR, \"classification/train\")\n", "val_dir = os.path.join(BASE_DIR, \"classification/val\")\n", "test_dir = os.path.join(BASE_DIR, \"classification/test\")\n", "\n", "IMG_SIZE = (224, 224)\n", "BATCH_SIZE = 32" ] }, { "cell_type": "code", "execution_count": 26, "id": "thPOKHpF6nEk", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "thPOKHpF6nEk", "outputId": "1cddd61a-fb16-45a9-e8bd-f056ebccfcf4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 1820 images belonging to 26 classes.\n", "Found 390 images belonging to 26 classes.\n", "Found 390 images belonging to 26 classes.\n", "Number of classes: 26\n" ] } ], "source": [ "train_datagen = ImageDataGenerator(\n", " rescale=1./255,\n", " rotation_range=15,\n", " width_shift_range=0.1,\n", " height_shift_range=0.1,\n", " zoom_range=0.1,\n", " horizontal_flip=True\n", ")\n", "\n", "val_datagen = ImageDataGenerator(rescale=1./255)\n", "test_datagen = ImageDataGenerator(rescale=1./255)\n", "\n", "train_generator = train_datagen.flow_from_directory(\n", " train_dir,\n", " target_size=IMG_SIZE,\n", " batch_size=BATCH_SIZE,\n", " class_mode='categorical'\n", ")\n", "\n", "val_generator = val_datagen.flow_from_directory(\n", " val_dir,\n", " target_size=IMG_SIZE,\n", " batch_size=BATCH_SIZE,\n", " class_mode='categorical'\n", ")\n", "\n", "test_generator = test_datagen.flow_from_directory(\n", " test_dir,\n", " target_size=IMG_SIZE,\n", " batch_size=BATCH_SIZE,\n", " class_mode='categorical',\n", " shuffle=False\n", ")\n", "\n", "NUM_CLASSES = train_generator.num_classes\n", "print(\"Number of classes:\", NUM_CLASSES)" ] }, { "cell_type": "markdown", "id": "fVOlwIRs6uXk", "metadata": { "id": "fVOlwIRs6uXk" }, "source": [ "Model Builder Function" ] }, { "cell_type": "code", "execution_count": 27, "id": "onbWcrF16rSs", "metadata": { "id": "onbWcrF16rSs" }, "outputs": [], "source": [ "def build_model(base_model_class):\n", "\n", " base_model = base_model_class(\n", " weights='imagenet',\n", " include_top=False,\n", " input_shape=(224,224,3)\n", " )\n", "\n", " base_model.trainable = False # Freeze pretrained layers\n", "\n", " x = base_model.output\n", " x = GlobalAveragePooling2D()(x)\n", " x = Dense(512, activation='relu')(x)\n", " x = Dropout(0.5)(x)\n", " outputs = Dense(NUM_CLASSES, activation='softmax')(x)\n", "\n", " model = Model(inputs=base_model.input, outputs=outputs)\n", "\n", " model.compile(\n", " optimizer=Adam(learning_rate=1e-4),\n", " loss='categorical_crossentropy',\n", " metrics=['accuracy']\n", " )\n", "\n", " return model" ] }, { "cell_type": "markdown", "id": "T2Q_jiw963NT", "metadata": { "id": "T2Q_jiw963NT" }, "source": [ "Train Models" ] }, { "cell_type": "markdown", "id": "-VGwJk5T660b", "metadata": { "id": "-VGwJk5T660b" }, "source": [ "VGG16" ] }, { "cell_type": "code", "execution_count": 29, "id": "NXA-4uh76zCT", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NXA-4uh76zCT", "outputId": "cd5013c7-f2bd-4777-ed3f-052c171b1458" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18s/step - accuracy: 0.0338 - loss: 3.5216 " ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.12/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n", " self._warn_if_super_not_called()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1282s\u001b[0m 22s/step - accuracy: 0.0338 - loss: 3.5204 - val_accuracy: 0.0564 - val_loss: 3.2427\n", "Epoch 2/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1225s\u001b[0m 22s/step - accuracy: 0.0493 - loss: 3.3195 - val_accuracy: 0.1692 - val_loss: 3.1897\n", "Epoch 3/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1227s\u001b[0m 22s/step - accuracy: 0.0680 - loss: 3.2793 - val_accuracy: 0.1487 - val_loss: 3.1539\n", "Epoch 4/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1225s\u001b[0m 22s/step - accuracy: 0.0746 - loss: 3.2127 - val_accuracy: 0.1949 - val_loss: 3.1227\n", "Epoch 5/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1273s\u001b[0m 22s/step - accuracy: 0.0858 - loss: 3.1611 - val_accuracy: 0.1769 - val_loss: 3.0912\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n" ] } ], "source": [ "model_vgg = build_model(VGG16)\n", "\n", "history_vgg = model_vgg.fit(\n", " train_generator,\n", " validation_data=val_generator,\n", " epochs=5\n", ")\n", "\n", "model_vgg.save(\"vgg16_smartvision.h5\")" ] }, { "cell_type": "markdown", "id": "GfyXYd-c7B3D", "metadata": { "id": "GfyXYd-c7B3D" }, "source": [ "ResNet50" ] }, { "cell_type": "code", "execution_count": 30, "id": "Bg9Y9lxT7FMT", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Bg9Y9lxT7FMT", "outputId": "79345772-5cce-418c-be47-6ec18127b646" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n", "\u001b[1m94765736/94765736\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n", "Epoch 1/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m401s\u001b[0m 7s/step - accuracy: 0.0492 - loss: 3.6634 - val_accuracy: 0.0487 - val_loss: 3.2625\n", "Epoch 2/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m392s\u001b[0m 7s/step - accuracy: 0.0379 - loss: 3.4397 - val_accuracy: 0.0615 - val_loss: 3.2535\n", "Epoch 3/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m453s\u001b[0m 7s/step - accuracy: 0.0368 - loss: 3.3249 - val_accuracy: 0.0436 - val_loss: 3.2493\n", "Epoch 4/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m390s\u001b[0m 7s/step - accuracy: 0.0483 - loss: 3.3072 - val_accuracy: 0.0641 - val_loss: 3.2452\n", "Epoch 5/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m394s\u001b[0m 7s/step - accuracy: 0.0422 - loss: 3.2666 - val_accuracy: 0.0641 - val_loss: 3.2414\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n" ] } ], "source": [ "model_resnet = build_model(ResNet50)\n", "\n", "history_resnet = model_resnet.fit(\n", " train_generator,\n", " validation_data=val_generator,\n", " epochs=5\n", ")\n", "\n", "model_resnet.save(\"resnet50_smartvision.h5\")" ] }, { "cell_type": "markdown", "id": "HiXh6YSn7JBH", "metadata": { "id": "HiXh6YSn7JBH" }, "source": [ "MobileNetV2" ] }, { "cell_type": "code", "execution_count": 31, "id": "XU0QOGms7LUr", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XU0QOGms7LUr", "outputId": "85361dd3-58c1-45bb-dafb-ce7fe50e27eb" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n", "\u001b[1m9406464/9406464\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n", "Epoch 1/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m118s\u001b[0m 2s/step - accuracy: 0.0652 - loss: 3.5567 - val_accuracy: 0.1436 - val_loss: 3.0169\n", "Epoch 2/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m114s\u001b[0m 2s/step - accuracy: 0.2128 - loss: 2.8428 - val_accuracy: 0.2667 - val_loss: 2.7224\n", "Epoch 3/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 2s/step - accuracy: 0.3420 - loss: 2.4177 - val_accuracy: 0.3077 - val_loss: 2.4924\n", "Epoch 4/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m109s\u001b[0m 2s/step - accuracy: 0.4146 - loss: 2.1793 - val_accuracy: 0.3333 - val_loss: 2.3541\n", "Epoch 5/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m147s\u001b[0m 2s/step - accuracy: 0.4968 - loss: 1.9123 - val_accuracy: 0.3410 - val_loss: 2.2525\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n" ] } ], "source": [ "model_mobilenet = build_model(MobileNetV2)\n", "\n", "history_mobilenet = model_mobilenet.fit(\n", " train_generator,\n", " validation_data=val_generator,\n", " epochs=5\n", ")\n", "\n", "model_mobilenet.save(\"mobilenetv2_smartvision.h5\")" ] }, { "cell_type": "markdown", "id": "AB1v8qGr7Pjr", "metadata": { "id": "AB1v8qGr7Pjr" }, "source": [ "EfficientNetB0" ] }, { "cell_type": "code", "execution_count": 33, "id": "_Ttf49cR7QYi", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_Ttf49cR7QYi", "outputId": "2d7f41ba-b611-4c55-d269-e9d0f6be89d2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0_notop.h5\n", "\u001b[1m16705208/16705208\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n", "Epoch 1/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m194s\u001b[0m 3s/step - accuracy: 0.0417 - loss: 3.2931 - val_accuracy: 0.0385 - val_loss: 3.2605\n", "Epoch 2/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m174s\u001b[0m 3s/step - accuracy: 0.0318 - loss: 3.2825 - val_accuracy: 0.0385 - val_loss: 3.2604\n", "Epoch 3/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m171s\u001b[0m 3s/step - accuracy: 0.0372 - loss: 3.2711 - val_accuracy: 0.0385 - val_loss: 3.2590\n", "Epoch 4/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m173s\u001b[0m 3s/step - accuracy: 0.0427 - loss: 3.2625 - val_accuracy: 0.0385 - val_loss: 3.2592\n", "Epoch 5/5\n", "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m177s\u001b[0m 3s/step - accuracy: 0.0434 - loss: 3.2654 - val_accuracy: 0.0462 - val_loss: 3.2586\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n" ] } ], "source": [ "model_efficient = build_model(EfficientNetB0)\n", "\n", "history_efficient = model_efficient.fit(\n", " train_generator,\n", " validation_data=val_generator,\n", " epochs=5\n", ")\n", "\n", "model_efficient.save(\"efficientnetb0_smartvision.h5\")" ] }, { "cell_type": "markdown", "id": "tb0ckArW7Sk6", "metadata": { "id": "tb0ckArW7Sk6" }, "source": [ "Evaluate Models" ] }, { "cell_type": "code", "execution_count": 34, "id": "cMgtLEdJ7Vbi", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cMgtLEdJ7Vbi", "outputId": "7c7e608f-6b1d-4f9e-9ea4-69d4a0a31f55" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Evaluating Models on Test Set\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.12/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n", " self._warn_if_super_not_called()\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m216s\u001b[0m 16s/step - accuracy: 0.1652 - loss: 3.1130\n", "VGG16: 0.1564\n", "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 5s/step - accuracy: 0.0618 - loss: 3.2352\n", "ResNet50: 0.0769\n", "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 1s/step - accuracy: 0.3812 - loss: 2.2449\n", "MobileNetV2: 0.3231\n", "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 2s/step - accuracy: 0.0789 - loss: 3.2618\n", "EfficientNetB0: 0.0436\n" ] } ], "source": [ "print(\"Evaluating Models on Test Set\\n\")\n", "\n", "models = {\n", " \"VGG16\": model_vgg,\n", " \"ResNet50\": model_resnet,\n", " \"MobileNetV2\": model_mobilenet,\n", " \"EfficientNetB0\": model_efficient\n", "}\n", "\n", "results = {}\n", "\n", "for name, model in models.items():\n", " loss, acc = model.evaluate(test_generator)\n", " results[name] = acc\n", " print(f\"{name}: {acc:.4f}\")" ] }, { "cell_type": "markdown", "id": "QLfO69wT7Z8y", "metadata": { "id": "QLfO69wT7Z8y" }, "source": [ "Compare Performance" ] }, { "cell_type": "code", "execution_count": 35, "id": "5Cjredwm7aa9", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 468 }, "id": "5Cjredwm7aa9", "outputId": "1bce7af7-7007-4334-abf1-ea6626b90091" }, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,5))\n", "plt.bar(results.keys(), results.values())\n", "plt.title(\"Model Comparison - Test Accuracy\")\n", "plt.ylabel(\"Accuracy\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "cJqeQcme8d0M", "metadata": { "id": "cJqeQcme8d0M" }, "source": [ "Train YOLOv8 Object Detection Model" ] }, { "cell_type": "code", "execution_count": 36, "id": "ZA8ZYAXP8gus", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ZA8ZYAXP8gus", "outputId": "d9e7eeb2-4409-45b5-b32d-d57dba48e87e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.2 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90mβ•Ί\u001b[0m\u001b[90m━━━━\u001b[0m \u001b[32m1.0/1.2 MB\u001b[0m \u001b[31m31.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m17.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h" ] } ], "source": [ "!pip install ultralytics --quiet" ] }, { "cell_type": "markdown", "id": "yWhl5R7b8n18", "metadata": { "id": "yWhl5R7b8n18" }, "source": [ "Verify Dataset YAML" ] }, { "cell_type": "code", "execution_count": 37, "id": "qFnR7OOb8kW9", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qFnR7OOb8kW9", "outputId": "ef5bab1b-2d2d-46d4-be6d-d6033774fe88" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "YAML exists: True\n", "# SmartVision Dataset - YOLOv8 Configuration\n", "path: /content/smartvision_dataset/detection\n", "train: images\n", "val: images\n", "\n", "names:\n", " 0: person\n", " 1: bicycle\n", " 2: car\n", " 3: motorcycle\n", " 4: airplane\n", " 5: bus\n", " 6: train\n", " 7: truck\n", " 8: traffic light\n", " 9: stop sign\n", " 10: bench\n", " 11: bird\n", " 12: cat\n", " 13: dog\n", " 14: horse\n", " 15: cow\n", " 16: elephant\n", " 17: bottle\n", " 18: cup\n", " 19: bowl\n", " 20: pizza\n", " 21: cake\n", " 22: chair\n", " 23: couch\n", " 24: potted plant\n", " 25: bed\n", "\n", "nc: 26\n", "\n" ] } ], "source": [ "import os\n", "\n", "yaml_path = \"smartvision_dataset/detection/data.yaml\"\n", "\n", "print(\"YAML exists:\", os.path.exists(yaml_path))\n", "\n", "with open(yaml_path, 'r') as f:\n", " print(f.read())" ] }, { "cell_type": "markdown", "id": "wiKV1ftC8tKM", "metadata": { "id": "wiKV1ftC8tKM" }, "source": [ "Train YOLOv8" ] }, { "cell_type": "code", "execution_count": 1, "id": "72v0ObtC8vrj", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "72v0ObtC8vrj", "outputId": "566f3499-799a-4a18-97e1-daa97f2dd402" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ultralytics 8.4.14 πŸš€ Python-3.12.12 torch-2.10.0+cpu CPU (Intel Xeon CPU @ 2.20GHz)\n", "\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, angle=1.0, augment=False, auto_augment=randaugment, batch=16, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=smartvision_dataset/detection/data.yaml, degrees=0.0, deterministic=True, device=cpu, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, end2end=None, epochs=10, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.01, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8n.pt, momentum=0.937, mosaic=1.0, multi_scale=0.0, name=smartvision_yolov83, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, rle=1.0, save=True, save_conf=False, save_crop=False, save_dir=/content/runs/detect/smartvision_yolov83, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n", "Overriding model.yaml nc=80 with nc=26\n", "\n", " from n params module arguments \n", " 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] \n", " 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] \n", " 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] \n", " 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] \n", " 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] \n", " 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] \n", " 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] \n", " 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n", " 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] \n", " 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] \n", " 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] \n", " 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] \n", " 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n", " 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] \n", " 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n", " 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] \n", " 22 [15, 18, 21] 1 756382 ultralytics.nn.modules.head.Detect [26, 16, None, [64, 128, 256]]\n", "Model summary: 130 layers, 3,015,918 parameters, 3,015,902 gradients, 8.2 GFLOPs\n", "\n", "Transferred 319/355 items from pretrained weights\n", "Freezing layer 'model.22.dfl.conv.weight'\n", "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access βœ… (ping: 0.1Β±0.0 ms, read: 39.5Β±4.9 MB/s, size: 137.5 KB)\n", "\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/smartvision_dataset/detection/labels.cache... 2210 images, 0 backgrounds, 1687 corrupt: 100% ━━━━━━━━━━━━ 2210/2210 257.5Mit/s 0.0s\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000001.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000002.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000003.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000004.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000005.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000006.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000010.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000011.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000012.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000013.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000014.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000015.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000016.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0568]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000017.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0568]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000018.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0568]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000019.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0568]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000020.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0568]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000021.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0377]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000022.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000023.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000024.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000025.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000026.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000027.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000028.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000029.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000030.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000031.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000032.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000033.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000034.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000035.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000036.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000037.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000038.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000039.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000040.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000043.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000044.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000045.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000046.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000047.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000048.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000049.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000050.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000051.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000052.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000053.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000054.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000055.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000056.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.39]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000059.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0213 1.086 1.071]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000060.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0213 1.086 1.071]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000061.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0213 1.086 1.071]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000062.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0213 1.086 1.071]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000063.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4517 1.3824]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000064.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4517 1.3824]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000065.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000066.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000067.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000068.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000069.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000070.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000071.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000072.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000073.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000074.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000075.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000076.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000077.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000078.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000079.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000080.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000081.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000082.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000083.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000085.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000086.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0573 1.1913 1.4816]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000087.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0573 1.1913 1.4816]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000088.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0573 1.1913 1.4816]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000089.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3385 1.3641 1.3384 1.4065 1.3079 1.0907 1.4273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000090.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3385 1.3641 1.3384 1.4065 1.3079 1.0907 1.4273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000091.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3488]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000092.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3492 1.1307]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000093.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3492 1.1307]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000094.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000095.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000096.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000097.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000098.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000099.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000100.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000101.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000102.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000103.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000104.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000106.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2477 1.0966 1.0254 1.1976 1.0638 1.4171 1.1151 1.0168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000107.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2477 1.0966 1.0254 1.1976 1.0638 1.4171 1.1151 1.0168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000108.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000109.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000110.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575 1.1136 1.3602 1.1078 1.3157 1.1806]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000111.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575 1.1136 1.3602 1.1078 1.3157 1.1806]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000113.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000114.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000115.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000116.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000117.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000118.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000119.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000120.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000121.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000122.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0167 1.3468 1.0565 1.3635 1.1786]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000125.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2942 1.3317 1.1404 1.0905 1.4771 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000126.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000127.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000128.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0389]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000129.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0389]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000131.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0217 1.0899]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000132.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0217 1.0899]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000133.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0857 1.2957 1.4019 1.4657 1.3516 1.2175]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000134.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0814 1.1501 1.3078 1.132]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000135.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0814 1.1501 1.3078 1.132]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000136.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0814 1.1501 1.3078 1.132]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000137.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2408 1.0816 1.0947 1.1683 1.3866 1.2441 1.3405 1.2633]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000149.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3813 1.1073 1.1568 1.1564 1.0852 1.1014 1.0644 1.087 1.0961 1.0906 1.0491 1.0929 1.1042 1.3517 1.2582 1.3639 1.0882 1.1112 1.0979]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000150.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3813 1.1073 1.1568 1.1564 1.0852 1.1014 1.0644 1.087 1.0961 1.0906 1.0491 1.0929 1.1042 1.3517 1.2582 1.3639 1.0882 1.1112 1.0979]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000151.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3813 1.1073 1.1568 1.1564 1.0852 1.1014 1.0644 1.087 1.0961 1.0906 1.0491 1.0929 1.1042 1.3517 1.2582 1.3639 1.0882 1.1112 1.0979]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000152.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3813 1.1073 1.1568 1.1564 1.0852 1.1014 1.0644 1.087 1.0961 1.0906 1.0491 1.0929 1.1042 1.3517 1.2582 1.3639 1.0882 1.1112 1.0979]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000153.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3447 1.0975]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000154.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000155.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000156.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000157.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000158.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000159.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000160.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000161.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000162.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000163.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000164.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000165.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.46 1.4187]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000166.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000167.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000168.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000169.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1948 1.1834]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000170.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.032]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000171.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000172.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000173.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000174.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000175.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000176.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000177.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000178.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000179.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000180.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000181.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000182.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000183.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000184.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0118 1.3348 1.0334]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000185.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000186.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000187.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000188.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000189.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000190.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000191.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000192.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000193.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000194.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000196.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0864]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000198.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3322 1.3673]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000199.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.0291 1.2534]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000200.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.0291 1.2534]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000201.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.0291 1.2534]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000202.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000203.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000204.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000205.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000206.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000207.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000208.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000209.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000210.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000211.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000212.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000213.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000214.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000215.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000216.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.148]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000217.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2216]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000218.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2216]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000219.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0472 1.0888 1.1318 1.3638 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000220.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0472 1.0888 1.1318 1.3638 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000221.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0472 1.0888 1.1318 1.3638 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000222.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0472 1.0888 1.1318 1.3638 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000223.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0472 1.0888 1.1318 1.3638 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000224.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0472 1.0888 1.1318 1.3638 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000225.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.0657 1.0768]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000226.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.0657 1.0768]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000231.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3488]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000232.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.379 1.1509 1.2056 1.0189]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000233.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.034 1.0878]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000234.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.034 1.0878]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000235.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.034 1.0878]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000236.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.034 1.0878]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000237.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.034 1.0878]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000239.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000240.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000241.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000242.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000243.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000244.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000245.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000246.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000247.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000248.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000249.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000250.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3583]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000251.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3583]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000252.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3583]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000253.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000254.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0177 1.3068]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000257.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0377]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000259.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0872]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000260.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2216]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000261.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0819 1.0649]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000262.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0984 1.0379 1.2711]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000263.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3488]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000264.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2424 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000265.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000266.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2477 1.0966 1.0254 1.1976 1.0638 1.4171 1.1151 1.0168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000268.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0251 1.3488 1.0667]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000270.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000271.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000272.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000273.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000274.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000275.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000276.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000277.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000278.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000279.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000280.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000281.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000282.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000283.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000284.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.1607 1.1498 1.2068 1.1738 1.1713 1.177 1.4336 1.179 1.1247 1.1285 1.1228 1.1898 1.0465 1.0555 1.1407 1.162 1.1592 1.1576 1.3573 1.1937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000285.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2962 1.1061 1.2492]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000286.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2962 1.1061 1.2492]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000287.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2962 1.1061 1.2492]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000288.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2962 1.1061 1.2492]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000289.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2962 1.1061 1.2492]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000290.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0607 1.0462 1.3976 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000292.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0711 1.0652 1.0427 1.0439]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000293.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0711 1.0652 1.0427 1.0439]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000294.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0507 1.1707 1.1062 1.0498 1.4468 1.2013 1.0681]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000299.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2408 1.0816 1.0947 1.1683 1.3866 1.2441 1.3405 1.2633]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000300.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2408 1.0816 1.0947 1.1683 1.3866 1.2441 1.3405 1.2633]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000301.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3447 1.0975]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000302.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0724 1.3465]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000303.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4128]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000304.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3465 1.4145 1.0238 1.3548]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000305.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3465 1.4145 1.0238 1.3548]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000306.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0648 1.3159]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000307.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0584]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000308.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1916]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000309.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000310.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000311.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000312.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000313.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3171 1.1059 1.2]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000314.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000316.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0195 1.0134 1.2051 1.0651 1.3172 1.0999 1.3003 1.238 1.432 1.4685 1.1767 1.2398 1.4213 1.2114 1.2702 1.0145 1.0843]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000318.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2184]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000320.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.098 1.3046 1.4014]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000321.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.098 1.3046 1.4014]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000322.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.098 1.3046 1.4014]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000324.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2433]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000325.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2128 1.3769 1.337 1.2589 1.2109 1.1046 1.1607 1.3543 1.4789 1.4321 1.4126 1.298 1.3879]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000328.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3795 1.3697]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000329.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1473 1.1461]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000330.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1473 1.1461]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000331.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1473 1.1461]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000332.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1473 1.1461]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000333.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0331 1.082 1.2072 1.178]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000335.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2599 1.1365]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000336.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.3181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000337.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.3181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000338.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.3181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000339.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.3181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000341.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000342.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000344.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000345.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0919 1.0994 1.0493]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000350.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2861 1.0694 1.2839 1.3754]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000351.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2861 1.0694 1.2839 1.3754]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000352.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2861 1.0694 1.2839 1.3754]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000354.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2424 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000355.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000356.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000357.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000358.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000359.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0432 1.261]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000360.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0728]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000361.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1293 1.0559 1.0301]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000362.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1293 1.0559 1.0301]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000364.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1415]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000365.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0879 1.0447 1.0649]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000367.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1433 1.0584]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000368.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1658 1.0536 1.1723 1.0751]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000369.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0988]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000370.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0988]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000371.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0988]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000372.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0988]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000373.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1191 1.3373 1.4758 1.3819 1.3537]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000374.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.067 1.0303 1.042 1.0528 1.0443 1.0271 1.0577 1.0358 1.0675 1.0429 1.047 1.0727 1.0297]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000382.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1023 1.0417 1.2845 1.0395]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000388.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1308 1.4184]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000390.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1625 1.2811 1.0798 1.2282 1.3015]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000391.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.266]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000392.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.266]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000393.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.266]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000394.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.266]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000395.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.266]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000396.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1867 1.1554 1.1629 1.1675]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000397.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000398.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000399.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000400.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000401.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.078 1.2619]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000402.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.078 1.2619]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000403.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.078 1.2619]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000406.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.361 1.2929 1.0871 1.0424 1.0563 1.1481 1.2253 1.0351 1.2423 1.4025 1.2569 1.4244]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000407.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.361 1.2929 1.0871 1.0424 1.0563 1.1481 1.2253 1.0351 1.2423 1.4025 1.2569 1.4244]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000408.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.361 1.2929 1.0871 1.0424 1.0563 1.1481 1.2253 1.0351 1.2423 1.4025 1.2569 1.4244]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000409.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.361 1.2929 1.0871 1.0424 1.0563 1.1481 1.2253 1.0351 1.2423 1.4025 1.2569 1.4244]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000410.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.361 1.2929 1.0871 1.0424 1.0563 1.1481 1.2253 1.0351 1.2423 1.4025 1.2569 1.4244]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000413.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.025 1.0101 1.1705]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000414.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3054 1.2719 1.3924 1.2629 1.3638 1.1235 1.2335 1.0921 1.1293 1.0976 1.0477 1.1907 1.2481 1.2469 1.4638 1.0224 1.0726]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000418.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2735 1.3458 1.1181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000419.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2735 1.3458 1.1181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000420.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2735 1.3458 1.1181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000421.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2735 1.3458 1.1181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000422.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2735 1.3458 1.1181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000429.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000430.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000431.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3385 1.3641 1.3384 1.4065 1.3079 1.0907 1.4273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000433.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.0657 1.0768]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000434.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0242]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000436.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000437.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.05 1.3044 1.4222 1.4496 1.4635 1.4757 1.1427 1.3243 1.4071 1.079 1.3459 1.3661 1.3827 1.3939 1.439 1.4925 1.0395 1.3579 1.3657 1.2323 1.3455 1.2116 1.038 1.3302]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000438.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.05 1.3044 1.4222 1.4496 1.4635 1.4757 1.1427 1.3243 1.4071 1.079 1.3459 1.3661 1.3827 1.3939 1.439 1.4925 1.0395 1.3579 1.3657 1.2323 1.3455 1.2116 1.038 1.3302]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000439.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.05 1.3044 1.4222 1.4496 1.4635 1.4757 1.1427 1.3243 1.4071 1.079 1.3459 1.3661 1.3827 1.3939 1.439 1.4925 1.0395 1.3579 1.3657 1.2323 1.3455 1.2116 1.038 1.3302]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000440.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4233 1.0268 1.4472 1.4143]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000442.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0102 1.3403 1.041 1.0854 1.4127 1.4578 1.0778 1.2834]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000443.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3599]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000444.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3599]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000445.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4707 1.0701]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000446.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2014 1.026 1.3853 1.2427 1.1528 1.1792 1.0983 1.2052]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000447.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0469 1.0219 1.2866 1.4379 1.3544 1.4822 1.0766 1.2366 1.2719 1.1818]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000448.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0469 1.0219 1.2866 1.4379 1.3544 1.4822 1.0766 1.2366 1.2719 1.1818]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000449.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0469 1.0219 1.2866 1.4379 1.3544 1.4822 1.0766 1.2366 1.2719 1.1818]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000450.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0469 1.0219 1.2866 1.4379 1.3544 1.4822 1.0766 1.2366 1.2719 1.1818]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000451.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0469 1.0219 1.2866 1.4379 1.3544 1.4822 1.0766 1.2366 1.2719 1.1818]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000452.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276 1.0754 1.0253]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000453.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2059 1.1574]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000454.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0952 1.219 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000455.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4092]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000456.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1614 1.261 1.0286]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000458.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4164 1.268 1.1395 1.0109]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000459.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0389]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000460.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2171]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000461.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2171]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000462.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2171]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000463.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2171]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000464.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2171]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000466.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3606 1.0951]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000467.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0814 1.1501 1.3078 1.132]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000468.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3813 1.1073 1.1568 1.1564 1.0852 1.1014 1.0644 1.087 1.0961 1.0906 1.0491 1.0929 1.1042 1.3517 1.2582 1.3639 1.0882 1.1112 1.0979]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000469.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3023]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000470.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3023]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000471.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3023]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000472.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0732 1.0623]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000473.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3306 1.4458 1.4114 1.2858 1.1532 1.4603 1.4826]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000474.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2569 1.0143 1.1132 1.404 1.1943 1.0717 1.2386]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000475.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3266 1.0494 1.3057 1.2081]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000478.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000479.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000480.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000481.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000482.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000483.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000484.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000485.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000486.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000487.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000488.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000489.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000490.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000491.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.136 1.2483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000492.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000493.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000494.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0112 1.0766 1.0268 1.0716 1.0643 1.1068 1.0362 1.087 1.0399 1.0375 1.0568 1.174 1.031]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000499.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.367 1.3068 1.3222 1.3171 1.4502 1.3871 1.343]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000500.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.367 1.3068 1.3222 1.3171 1.4502 1.3871 1.343]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000501.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1948 1.1834]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000502.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1254 1.2028 1.1766 1.2052 1.2084 1.1524 1.1032 1.1977 1.2937 1.0171 1.0787 1.2017 1.2965 1.1468]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000503.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2296]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000504.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2716 1.3971]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000506.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3134 1.214 1.2461 1.2872 1.209 1.1122 1.1169 1.462 1.4809 1.3203 1.0778 1.1088 1.0905 1.0872 1.1193 1.0923 1.0567 1.0825 1.4075 1.3613]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000507.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3134 1.214 1.2461 1.2872 1.209 1.1122 1.1169 1.462 1.4809 1.3203 1.0778 1.1088 1.0905 1.0872 1.1193 1.0923 1.0567 1.0825 1.4075 1.3613]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000508.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3134 1.214 1.2461 1.2872 1.209 1.1122 1.1169 1.462 1.4809 1.3203 1.0778 1.1088 1.0905 1.0872 1.1193 1.0923 1.0567 1.0825 1.4075 1.3613]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000509.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3134 1.214 1.2461 1.2872 1.209 1.1122 1.1169 1.462 1.4809 1.3203 1.0778 1.1088 1.0905 1.0872 1.1193 1.0923 1.0567 1.0825 1.4075 1.3613]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000510.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000511.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.39]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000512.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1057 1.1904]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000516.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000517.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.03]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000518.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.22]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000519.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1256 1.1202]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000520.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0432 1.261]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000521.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4123]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000523.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4325]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000524.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4325]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000526.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000527.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0916]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000528.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.195]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000529.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000530.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000531.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000532.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000533.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000534.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000535.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000538.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1539]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000539.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1539]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000541.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2076]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000542.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2076]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000543.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2076]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000545.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1614 1.261 1.0286]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000549.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0812]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000550.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2837 1.2465 1.3159]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000551.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2852]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000552.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.459 1.3948]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000554.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0712]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000555.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0712]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000559.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0119 1.1955 1.0194 1.0647 1.0211 1.0261 1.2364 1.0347 1.314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000565.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3823 1.3545 1.4137 1.154 1.3619 1.3342 1.3551 1.3339 1.359]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000566.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3243 1.225]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000568.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0282]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000569.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0282]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000572.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1074 1.0704]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000576.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3914 1.3058 1.2712 1.2336 1.444 1.2158]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000577.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4566]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000580.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1967]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000581.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2598 1.3114 1.3175 1.0246 1.4372 1.0436 1.3609 1.191 1.3376 1.2616 1.2555 1.3 1.2475 1.2016]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000582.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2598 1.3114 1.3175 1.0246 1.4372 1.0436 1.3609 1.191 1.3376 1.2616 1.2555 1.3 1.2475 1.2016]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000583.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2113 1.2059 1.4601]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000584.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1425 1.1342 1.3027 1.2293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000586.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0571 1.327 1.2399 1.1931 1.1115 1.1608 1.0154 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000587.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0453]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000588.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3536 1.0144 1.0818 1.1754 1.2144 1.2513 1.3003 1.3568 1.315 1.1019 1.2382]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000589.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3536 1.0144 1.0818 1.1754 1.2144 1.2513 1.3003 1.3568 1.315 1.1019 1.2382]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000592.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2468 1.0382 1.0773]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000593.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2468 1.0382 1.0773]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000594.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2468 1.0382 1.0773]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000595.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.032]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000596.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000597.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000598.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0118 1.3348 1.0334]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000599.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000600.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000601.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000602.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000603.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000604.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000605.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000606.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000608.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1339]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000609.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1256 1.1202]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000610.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000611.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0194 1.2706 1.0633]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000612.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1784 1.3131]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000613.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4367 1.0919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000614.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4233 1.0268 1.4472 1.4143]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000615.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0414 1.2557 1.1841 1.3403 1.4323]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000617.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1415]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000618.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2477 1.0966 1.0254 1.1976 1.0638 1.4171 1.1151 1.0168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000621.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2014 1.026 1.3853 1.2427 1.1528 1.1792 1.0983 1.2052]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000622.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000623.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000624.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0879 1.0447 1.0649]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000625.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1897 1.1388]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000626.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000627.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000628.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.1607 1.1498 1.2068 1.1738 1.1713 1.177 1.4336 1.179 1.1247 1.1285 1.1228 1.1898 1.0465 1.0555 1.1407 1.162 1.1592 1.1576 1.3573 1.1937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000630.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3847 1.0703 1.4425 1.1328 1.3777 1.3455]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000631.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4322 1.4157 1.036]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000632.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0516 1.3319 1.1111 1.0484 1.0286 1.1049]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000633.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2916 1.275 1.4302 1.4422 1.4361 1.1156 1.4189 1.4698 1.4517 1.4587 1.4151 1.3592 1.2751 1.4392 1.4403 1.1641 1.4179 1.4494 1.3842 1.451 1.3966 1.4557 1.4013 1.3754 1.3953 1.3896\n", " 1.4612 1.299 1.2979 1.4233 1.2894]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000634.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1658 1.0536 1.1723 1.0751]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000635.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1658 1.0536 1.1723 1.0751]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000636.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1658 1.0536 1.1723 1.0751]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000637.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1173 1.168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000638.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1173 1.168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000639.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0952 1.219 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000640.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1152 1.1614 1.0889 1.138]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000641.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3154 1.3377 1.0388 1.0693 1.0537 1.275 1.3388 1.2903 1.2488 1.2648]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000642.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0607 1.0462 1.3976 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000643.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1614 1.261 1.0286]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000644.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0288 1.0281 1.0117 1.0186 1.2035 1.0144 1.0121]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000645.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0389]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000647.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0454]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000648.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0203]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000652.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0507 1.1707 1.1062 1.0498 1.4468 1.2013 1.0681]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000653.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2408 1.0816 1.0947 1.1683 1.3866 1.2441 1.3405 1.2633]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000654.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1599 1.015]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000656.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2265 1.3069]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000657.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3736 1.3781 1.358 1.2635 1.3725 1.0657 1.4004 1.3866 1.352 1.3398 1.3075 1.3549 1.375 1.4535 1.3392 1.364 1.3276 1.3996 1.3412 1.3606 1.354]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000658.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0732 1.0623]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000659.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1023 1.0417 1.2845 1.0395]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000660.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1023 1.0417 1.2845 1.0395]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000661.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1023 1.0417 1.2845 1.0395]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000662.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.46 1.4187]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000664.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3465 1.4145 1.0238 1.3548]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000665.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1916]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000666.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.068 1.2033 1.0507 1.0876 1.3228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000667.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.068 1.2033 1.0507 1.0876 1.3228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000668.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2417 1.3584 1.1288]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000669.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2417 1.3584 1.1288]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000670.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2852]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000671.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1625 1.2811 1.0798 1.2282 1.3015]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000672.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1625 1.2811 1.0798 1.2282 1.3015]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000673.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1625 1.2811 1.0798 1.2282 1.3015]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000674.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1625 1.2811 1.0798 1.2282 1.3015]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000675.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.077 1.1251 1.0913 1.3971 1.0682 1.1161 1.1624 1.1036 1.3631 1.1464 1.2978 1.1313 1.1067 1.2159 1.1498 1.0697 1.1656 1.026]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000676.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000677.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.272 1.4368]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000678.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.272 1.4368]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000679.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1254 1.2028 1.1766 1.2052 1.2084 1.1524 1.1032 1.1977 1.2937 1.0171 1.0787 1.2017 1.2965 1.1468]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000680.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000681.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000682.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000683.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000684.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000685.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000686.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000687.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000688.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000689.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3322 1.3673]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000690.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3322 1.3673]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000691.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3322 1.3673]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000693.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000694.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0946]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000695.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0946]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000696.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0946]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000697.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000698.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000699.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000700.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.22]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000701.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.22]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000702.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4367 1.0919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000703.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4367 1.0919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000704.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4233 1.0268 1.4472 1.4143]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000705.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4233 1.0268 1.4472 1.4143]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000706.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3754 1.4037]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000707.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3754 1.4037]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000708.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0251 1.3488 1.0667]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000709.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0916]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000710.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0916]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000711.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0916]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000712.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.0953 1.3587]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000713.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.0953 1.3587]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000714.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.0953 1.3587]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000715.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.1607 1.1498 1.2068 1.1738 1.1713 1.177 1.4336 1.179 1.1247 1.1285 1.1228 1.1898 1.0465 1.0555 1.1407 1.162 1.1592 1.1576 1.3573 1.1937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000716.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.1607 1.1498 1.2068 1.1738 1.1713 1.177 1.4336 1.179 1.1247 1.1285 1.1228 1.1898 1.0465 1.0555 1.1407 1.162 1.1592 1.1576 1.3573 1.1937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000717.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.046 1.2007 1.3681]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000718.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.046 1.2007 1.3681]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000719.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2916 1.275 1.4302 1.4422 1.4361 1.1156 1.4189 1.4698 1.4517 1.4587 1.4151 1.3592 1.2751 1.4392 1.4403 1.1641 1.4179 1.4494 1.3842 1.451 1.3966 1.4557 1.4013 1.3754 1.3953 1.3896\n", " 1.4612 1.299 1.2979 1.4233 1.2894]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000720.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2916 1.275 1.4302 1.4422 1.4361 1.1156 1.4189 1.4698 1.4517 1.4587 1.4151 1.3592 1.2751 1.4392 1.4403 1.1641 1.4179 1.4494 1.3842 1.451 1.3966 1.4557 1.4013 1.3754 1.3953 1.3896\n", " 1.4612 1.299 1.2979 1.4233 1.2894]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000721.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2916 1.275 1.4302 1.4422 1.4361 1.1156 1.4189 1.4698 1.4517 1.4587 1.4151 1.3592 1.2751 1.4392 1.4403 1.1641 1.4179 1.4494 1.3842 1.451 1.3966 1.4557 1.4013 1.3754 1.3953 1.3896\n", " 1.4612 1.299 1.2979 1.4233 1.2894]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000722.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2916 1.275 1.4302 1.4422 1.4361 1.1156 1.4189 1.4698 1.4517 1.4587 1.4151 1.3592 1.2751 1.4392 1.4403 1.1641 1.4179 1.4494 1.3842 1.451 1.3966 1.4557 1.4013 1.3754 1.3953 1.3896\n", " 1.4612 1.299 1.2979 1.4233 1.2894]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000723.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2916 1.275 1.4302 1.4422 1.4361 1.1156 1.4189 1.4698 1.4517 1.4587 1.4151 1.3592 1.2751 1.4392 1.4403 1.1641 1.4179 1.4494 1.3842 1.451 1.3966 1.4557 1.4013 1.3754 1.3953 1.3896\n", " 1.4612 1.299 1.2979 1.4233 1.2894]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000724.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0952 1.219 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000725.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0952 1.219 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000726.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0952 1.219 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000727.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3154 1.3377 1.0388 1.0693 1.0537 1.275 1.3388 1.2903 1.2488 1.2648]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000728.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3154 1.3377 1.0388 1.0693 1.0537 1.275 1.3388 1.2903 1.2488 1.2648]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000729.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000730.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000731.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000732.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000733.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0389]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000734.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481 1.2492 1.4525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000735.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481 1.2492 1.4525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000736.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481 1.2492 1.4525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000737.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481 1.2492 1.4525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000738.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481 1.2492 1.4525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000743.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3606 1.0951]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000744.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.0137 1.1085]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000745.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1202 1.2178 1.0562 1.0855 1.0295 1.4284 1.0975 1.1485 1.1605 1.0695 1.0893 1.1028 1.0967 1.31 1.1245]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000748.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.136 1.2483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000749.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.459 1.3948]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000750.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.459 1.3948]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000751.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000752.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000753.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000754.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000755.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000756.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000757.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000758.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000759.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000760.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000761.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000762.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000763.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000764.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.367 1.3068 1.3222 1.3171 1.4502 1.3871 1.343]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000765.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.032]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000766.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.39]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000767.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2059 1.1574]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000768.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3986 1.4119 1.3449 1.4049 1.163 1.408 1.0589 1.4209 1.3837 1.3754 1.246 1.4011 1.1946 1.1903 1.3871 1.252 1.3917]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000769.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3986 1.4119 1.3449 1.4049 1.163 1.408 1.0589 1.4209 1.3837 1.3754 1.246 1.4011 1.1946 1.1903 1.3871 1.252 1.3917]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000772.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.314 1.1316 1.061]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000775.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0668]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000777.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2649 1.2485 1.2453 1.2704 1.2382 1.2625 1.2872 1.234 1.2177 1.1553]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000778.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0492 1.0264 1.0191 1.0769 1.4468 1.1342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000779.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0546 1.2324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000780.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1284]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000784.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1327 1.0996]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000785.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1097]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000789.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0772]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000791.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3269 1.2657 1.2163 1.329 1.331 1.3371 1.0877 1.3574 1.2046 1.36 1.1283 1.2561]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000792.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.091]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000793.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.13]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000795.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0724 1.1465 1.1757 1.114 1.1201 1.1337 1.0671 1.0832 1.0644 1.0992 1.1717 1.1067 1.2286 1.0985 1.1826 1.0288 1.1958 1.085 1.1086 1.0146 1.1079 1.107 1.0316 1.0853 1.132 1.0802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000800.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.05]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000801.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3756 1.395 1.3772]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000802.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1524 1.0273 1.1426 1.1536 1.077 1.3664 1.0455 1.1725 1.0692 1.3674 1.0485 1.1537 1.0777]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000803.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3229 1.464 1.2557 1.1493]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000804.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3229 1.464 1.2557 1.1493]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000808.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2761 1.1774 1.1593 1.2493 1.16 1.1589 1.3185 1.1951 1.1904 1.3984 1.1755 1.1715 1.167 1.184 1.4111 1.2075]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000810.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4552 1.4155 1.3837 1.2465]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000811.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0632 1.0241 1.3898 1.2013]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000812.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.32 1.2879 1.0713 1.1626 1.2899]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000814.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2653 1.0717 1.0413 1.0219 1.1907 1.3297 1.3178 1.131 1.0974 1.033 1.0292 1.0212 1.0834 1.127]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000816.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2162]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000818.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000820.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4657 1.4056 1.4249 1.3656 1.3089 1.363]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000821.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0191 1.4111 1.0328]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000822.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.054 1.4505 1.2707 1.2308 1.1827 1.343 1.2302 1.0518 1.253 1.2448 1.3444]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000823.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.054 1.4505 1.2707 1.2308 1.1827 1.343 1.2302 1.0518 1.253 1.2448 1.3444]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000824.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4292]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000825.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2141 1.2382 1.2764 1.2476 1.3899 1.1673 1.2986 1.2267 1.2142 1.436 1.2327]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000826.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2642 1.3742 1.2885 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000827.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4312 1.3573 1.3779 1.3922 1.3743 1.3659 1.4256 1.4039 1.3227 1.3479 1.3559 1.308 1.3917 1.3706 1.3534]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000829.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2115]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000830.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3753]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000831.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0505 1.0278]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000832.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2782]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000833.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2433 1.0302 1.0205 1.1711 1.2713]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000834.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4256 1.3302]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000835.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2771 1.3713]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000836.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2771 1.3713]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000837.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3939]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000838.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3643 1.0459 1.4085]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000839.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0589 1.3825]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000840.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2816 1.0492 1.2663 1.2933 1.3978 1.273 1.2444 1.296 1.2382 1.2494 1.2247 1.1607 1.1884 1.238 1.2492 1.0777 1.2691 1.152 1.1418 1.2344]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000841.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3301 1.3912 1.4421 1.4759 1.3544 1.2422 1.0575 1.4611]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000844.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.171 1.2591 1.2601]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000848.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.297 1.1302 1.2356 1.3275 1.4435 1.0936 1.224]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000850.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000851.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000852.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000853.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000854.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000855.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000858.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2234 1.2273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000860.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000861.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1706 1.0647 1.0452]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000862.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1706 1.0647 1.0452]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000863.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000864.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000866.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4376 1.1876 1.3234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000868.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1174]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000869.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000870.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1076 1.4457 1.4495 1.2955 1.4506]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000871.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0963 1.2388]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000872.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0963 1.2388]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000873.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2136]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000875.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3206 1.1809]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000876.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2079 1.3944 1.2641 1.2551 1.3111 1.3779]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000877.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2079 1.3944 1.2641 1.2551 1.3111 1.3779]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000878.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2079 1.3944 1.2641 1.2551 1.3111 1.3779]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000879.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0815]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000880.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0815]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000881.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000882.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000883.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000884.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000885.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000886.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000887.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3847 1.0703 1.4425 1.1328 1.3777 1.3455]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000890.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.115 1.0837]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000891.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4092]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000892.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276 1.3572 1.0554 1.4834 1.2971 1.2622 1.0802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000893.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276 1.3572 1.0554 1.4834 1.2971 1.2622 1.0802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000894.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276 1.3572 1.0554 1.4834 1.2971 1.2622 1.0802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000898.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3622 1.2944 1.4458 1.237 1.3262 1.1019 1.3491 1.2381 1.2309 1.1671]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000903.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.309]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000904.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2384 1.2567 1.2147 1.3016 1.2493]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000905.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1377]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000906.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1461]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000907.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1314 1.0269 1.1178 1.055 1.0339]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000908.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1314 1.0269 1.1178 1.055 1.0339]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000909.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1314 1.0269 1.1178 1.055 1.0339]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000910.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1314 1.0269 1.1178 1.055 1.0339]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000911.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000912.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000913.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3306 1.4458 1.4114 1.2858 1.1532 1.4603 1.4826]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000914.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3702 1.242]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000915.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3702 1.242]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000916.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.46 1.4187]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000917.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.182 1.241]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000918.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.182 1.241]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000919.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1633 1.2284 1.2117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000920.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1633 1.2284 1.2117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000921.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3266 1.0494 1.3057 1.2081]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000922.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4101]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000923.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.068 1.2033 1.0507 1.0876 1.3228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000924.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.068 1.2033 1.0507 1.0876 1.3228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000925.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.068 1.2033 1.0507 1.0876 1.3228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000927.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0237 1.2134 1.3665 1.4545 1.4121 1.0725 1.0888 1.1255 1.328 1.283]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000928.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0237 1.2134 1.3665 1.4545 1.4121 1.0725 1.0888 1.1255 1.328 1.283]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000929.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0237 1.2134 1.3665 1.4545 1.4121 1.0725 1.0888 1.1255 1.328 1.283]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000930.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1969 1.3824 1.4004 1.4641 1.493 1.2]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000931.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1969 1.3824 1.4004 1.4641 1.493 1.2]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000932.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1969 1.3824 1.4004 1.4641 1.493 1.2]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000933.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.13]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000935.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000936.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000937.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000938.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000939.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000940.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000941.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000942.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000943.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000944.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000945.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000946.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000947.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000948.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000949.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000950.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000951.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000952.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000953.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000966.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0987]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000967.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000968.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000969.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000970.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000971.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000972.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000973.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000974.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000975.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000976.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000977.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000978.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000979.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000981.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000982.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000983.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000984.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000985.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000986.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000987.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000988.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000989.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000990.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000991.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000992.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000993.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000994.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000995.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000996.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000997.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000998.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000999.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001000.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001001.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001002.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001003.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001004.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001005.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001006.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001007.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001020.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2635]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001022.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3253]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001023.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.148]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001024.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0146]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001025.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001026.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001033.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.047 1.1259]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001034.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.047 1.1259]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001035.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1188]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001037.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2507 1.1559 1.2561 1.2419]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001041.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0282 1.0832]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001046.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1965]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001049.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0412]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001051.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.286]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001056.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1377]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001057.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2694]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001059.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0286 1.186]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001060.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.414 1.2287 1.3823 1.3763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001061.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.272]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001062.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0785]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001066.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1359]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001067.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1359]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001069.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1544]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001071.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4237 1.254]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001072.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001073.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0367]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001074.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.352 1.2153]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001075.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.352 1.2153]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001076.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001083.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0443 1.2404 1.461 1.3867]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001087.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4346]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001089.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3943 1.386]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001090.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1709 1.0168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001092.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1457]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001093.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2852]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001094.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2852]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001097.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1161 1.0308 1.1174]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001100.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0379 1.2789 1.1818]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001103.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0116 1.114 1.4075 1.1921 1.2107 1.428]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001106.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001107.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001108.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0864]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001109.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1277]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001111.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0581]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001114.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001117.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.047 1.1259]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001118.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2789 1.0191 1.1662]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001119.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0194 1.2706 1.0633]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001122.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3156 1.1853]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001123.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0906]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001124.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0906]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001125.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001126.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001127.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001128.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001129.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001130.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001131.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001132.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001133.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001134.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0251 1.3488 1.0667]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001138.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2136]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001139.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2136]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001140.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0949 1.206]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001143.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0162 1.4574 1.433]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001146.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0417 1.3097 1.1494]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001148.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2092 1.2012 1.2004 1.0987 1.2662 1.1445 1.2515 1.0877 1.2308 1.0146 1.0347]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001151.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.065]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001152.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1314 1.0269 1.1178 1.055 1.0339]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001153.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1633 1.2284 1.2117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001154.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0275 1.2791]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001157.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2069]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001159.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4348]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001160.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4348]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001161.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0207]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001162.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0207]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001163.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1002 1.4521 1.4133 1.4874]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001164.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1178 1.2011]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001165.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1794]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001176.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0209 1.1373 1.3803 1.47 1.4105 1.0566]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001178.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1725 1.178]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001179.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2747 1.039]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001180.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2859]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001181.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1762]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001182.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0737 1.3978 1.0118 1.1678 1.3534 1.1618]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001185.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.052]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001186.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1193]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001187.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1193]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001188.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2193]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001190.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001191.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001192.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0105]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001193.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1188]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001195.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1305 1.3196 1.3168 1.2685 1.2089 1.2903 1.3913 1.2551 1.3263 1.2114 1.2164 1.2267 1.3943 1.1789 1.1463 1.2441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001196.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1305 1.3196 1.3168 1.2685 1.2089 1.2903 1.3913 1.2551 1.3263 1.2114 1.2164 1.2267 1.3943 1.1789 1.1463 1.2441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001197.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1305 1.3196 1.3168 1.2685 1.2089 1.2903 1.3913 1.2551 1.3263 1.2114 1.2164 1.2267 1.3943 1.1789 1.1463 1.2441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001198.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1305 1.3196 1.3168 1.2685 1.2089 1.2903 1.3913 1.2551 1.3263 1.2114 1.2164 1.2267 1.3943 1.1789 1.1463 1.2441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001199.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001200.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001201.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001202.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001203.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001205.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001206.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001207.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001208.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001209.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001215.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3449]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001216.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3449]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001217.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4627 1.2884 1.452 1.3891 1.4209]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001218.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4627 1.2884 1.452 1.3891 1.4209]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001221.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.081 1.0702 1.3911 1.2859 1.467 1.3419 1.332 1.0593 1.1911 1.0545 1.472]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001222.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.081 1.0702 1.3911 1.2859 1.467 1.3419 1.332 1.0593 1.1911 1.0545 1.472]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001223.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.081 1.0702 1.3911 1.2859 1.467 1.3419 1.332 1.0593 1.1911 1.0545 1.472]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001224.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.081 1.0702 1.3911 1.2859 1.467 1.3419 1.332 1.0593 1.1911 1.0545 1.472]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001227.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.02 1.1304 1.2213 1.1192 1.2571 1.3272 1.1754]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001228.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2942 1.3317 1.1404 1.0905 1.4771 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001230.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276 1.3572 1.0554 1.4834 1.2971 1.2622 1.0802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001231.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276 1.3572 1.0554 1.4834 1.2971 1.2622 1.0802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001232.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0454]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001233.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0454]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001234.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0454]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001235.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1551 1.3875 1.1524 1.4426 1.352 1.2997 1.3483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001236.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1551 1.3875 1.1524 1.4426 1.352 1.2997 1.3483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001237.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1191 1.3373 1.4758 1.3819 1.3537]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001238.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2265 1.3069]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001239.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2265 1.3069]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001240.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1666 1.0802 1.0646 1.0511 1.0813]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001241.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0458]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001245.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0637]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001246.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0637]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001248.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001249.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001250.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001251.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001252.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001253.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001255.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001256.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001257.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1824 1.2572 1.4396]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001259.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3802 1.0485]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001260.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3802 1.0485]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001261.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3802 1.0485]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001262.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001263.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001264.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001265.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001266.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001267.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001268.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001271.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3586 1.3574 1.2148 1.4258 1.4257 1.2811 1.3354 1.2343 1.3349 1.3575 1.185 1.3623 1.3514 1.1002 1.3435 1.1284 1.411 1.2426 1.4178 1.289 1.1359 1.4374 1.3325 1.2147]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001272.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3586 1.3574 1.2148 1.4258 1.4257 1.2811 1.3354 1.2343 1.3349 1.3575 1.185 1.3623 1.3514 1.1002 1.3435 1.1284 1.411 1.2426 1.4178 1.289 1.1359 1.4374 1.3325 1.2147]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001273.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3586 1.3574 1.2148 1.4258 1.4257 1.2811 1.3354 1.2343 1.3349 1.3575 1.185 1.3623 1.3514 1.1002 1.3435 1.1284 1.411 1.2426 1.4178 1.289 1.1359 1.4374 1.3325 1.2147]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001274.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3586 1.3574 1.2148 1.4258 1.4257 1.2811 1.3354 1.2343 1.3349 1.3575 1.185 1.3623 1.3514 1.1002 1.3435 1.1284 1.411 1.2426 1.4178 1.289 1.1359 1.4374 1.3325 1.2147]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001279.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1784 1.3131]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001280.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1784 1.3131]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001281.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1784 1.3131]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001282.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1784 1.3131]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001283.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1784 1.3131]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001285.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001286.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001287.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1448]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001288.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1926]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001289.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1926]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001296.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3447 1.0975]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001298.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0637]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001299.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0637]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001300.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0637]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001304.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.098 1.3046 1.4014]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001305.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001306.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001307.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001308.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001309.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001310.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001311.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001312.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001313.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1906 1.3987]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001314.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1906 1.3987]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001315.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1906 1.3987]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001316.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2455 1.4706 1.3972 1.3619 1.1353 1.3134 1.3298]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001317.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2455 1.4706 1.3972 1.3619 1.1353 1.3134 1.3298]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001318.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2455 1.4706 1.3972 1.3619 1.1353 1.3134 1.3298]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001319.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0571]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001320.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0571]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001323.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3003 1.0663 1.1168 1.3624 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001324.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3003 1.0663 1.1168 1.3624 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001325.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3003 1.0663 1.1168 1.3624 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001326.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3003 1.0663 1.1168 1.3624 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001327.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3003 1.0663 1.1168 1.3624 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001328.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3515 1.0166 1.3976 1.259]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001329.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.236 1.1255]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001330.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.236 1.1255]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001331.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.236 1.1255]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001332.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.236 1.1255]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001333.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.236 1.1255]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001334.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.123 1.1801 1.1669]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001335.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.123 1.1801 1.1669]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001336.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.341 1.3945 1.459 1.0195]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001337.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.341 1.3945 1.459 1.0195]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001338.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.341 1.3945 1.459 1.0195]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001339.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.341 1.3945 1.459 1.0195]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001340.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001341.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001342.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001343.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001344.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001345.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001346.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001347.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2041]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001349.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1011]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001353.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1143 1.1581]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001354.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1143 1.1581]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001355.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1143 1.1581]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001356.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0817 1.2004 1.2661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001357.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0817 1.2004 1.2661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001358.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0817 1.2004 1.2661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001359.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0817 1.2004 1.2661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001364.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1298 1.2678 1.1358 1.0982 1.0199]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001365.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1298 1.2678 1.1358 1.0982 1.0199]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001366.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1298 1.2678 1.1358 1.0982 1.0199]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001367.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1298 1.2678 1.1358 1.0982 1.0199]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001368.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1298 1.2678 1.1358 1.0982 1.0199]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001369.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1298 1.2678 1.1358 1.0982 1.0199]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001370.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001371.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001372.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001373.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001374.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001375.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001376.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001377.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2061]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001378.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2061]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001379.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2061]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001380.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0133]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001381.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0133]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001382.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2565 1.0135 1.3584 1.3441 1.2876 1.2935 1.2937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001383.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2565 1.0135 1.3584 1.3441 1.2876 1.2935 1.2937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001384.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2565 1.0135 1.3584 1.3441 1.2876 1.2935 1.2937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001387.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001388.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001389.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001390.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001391.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001392.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0892]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001393.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0892]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001394.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0892]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001395.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0892]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001397.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001398.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001399.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001400.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001401.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001402.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001403.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001404.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001405.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001406.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001407.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001408.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001409.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001411.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0516 1.3319 1.1111 1.0484 1.0286 1.1049]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001417.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001418.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001419.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001420.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001421.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001422.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001423.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001424.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001425.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001426.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001427.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1904]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001428.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1904]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001431.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3948]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001432.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3948]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001442.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.303 1.1787 1.2042 1.1864 1.2294 1.2534 1.4164 1.1914 1.2802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001443.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1684]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001444.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1684]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001446.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001447.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001448.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001449.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001450.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001451.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001452.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001453.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001454.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001455.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001456.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001458.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4748 1.1871 1.1566]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001459.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4748 1.1871 1.1566]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001460.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4748 1.1871 1.1566]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001461.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001462.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001463.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0146]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001464.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0416]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001465.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0416]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001466.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2379]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001467.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001468.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001469.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001470.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001471.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001472.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001473.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0294]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001474.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.263]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001475.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001476.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001477.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001478.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001479.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001480.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001481.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001482.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001483.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001484.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001485.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001486.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001487.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001488.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001495.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1706 1.0647 1.0452]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001496.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1706 1.0647 1.0452]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001497.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2211 1.0764 1.1816]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001498.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2617]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001499.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001500.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001501.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001502.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001503.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001504.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001509.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001510.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001511.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001512.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001513.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001514.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001515.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001516.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001517.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001518.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001519.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001520.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001521.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001522.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001523.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001524.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001525.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001526.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.201 1.4511]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001527.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.201 1.4511]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001528.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.201 1.4511]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001529.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.201 1.4511]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001530.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001531.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001532.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001533.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001534.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001535.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001536.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001537.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001538.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001539.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001540.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001541.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001542.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001543.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001544.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001545.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001546.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001547.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001548.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001549.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001550.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001551.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001552.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001553.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001554.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001555.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001556.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001557.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4178 1.0167]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001558.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4178 1.0167]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001559.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4178 1.0167]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001560.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2323 1.2429 1.1507]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001561.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2323 1.2429 1.1507]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001562.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3782]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001564.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0719]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001565.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0719]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001566.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0146]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001567.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0146]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001568.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001569.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001570.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001571.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001572.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001573.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001574.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001575.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001576.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001577.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001578.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001579.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001580.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1324 1.2246 1.1323 1.0348 1.1518 1.4062 1.3396]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001581.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001582.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001583.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001584.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001585.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0294]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001586.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1731 1.1905]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001587.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1731 1.1905]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001588.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4255 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001589.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4255 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001590.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4255 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001591.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3529]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001592.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001593.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001594.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001595.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0325 1.3337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001597.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.047 1.1259]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001599.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0119 1.0116 1.0144]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001600.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2789 1.0191 1.1662]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001601.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2135]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001602.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2135]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001606.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001607.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001608.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001609.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2194]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001610.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2194]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001611.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2194]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001612.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001613.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001614.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0936 1.0384]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001618.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001619.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001620.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001621.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001622.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001623.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001624.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001625.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001626.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001627.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001628.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001629.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001630.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001631.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001632.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001633.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001634.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001635.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001636.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001637.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001638.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4178 1.0167]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001639.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4748 1.1871 1.1566]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001642.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001643.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0137 1.3517 1.4374 1.424]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001644.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001646.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0149]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001647.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2205]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001648.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4255 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001649.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001651.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001654.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0119 1.0116 1.0144]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001655.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0119 1.0116 1.0144]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001656.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0119 1.0116 1.0144]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001657.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2789 1.0191 1.1662]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001662.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0906]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001663.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2637]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001664.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0936 1.0384]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001665.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0936 1.0384]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001668.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0652 1.3586 1.242 1.1107 1.1944]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001669.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0652 1.3586 1.242 1.1107 1.1944]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001670.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0652 1.3586 1.242 1.1107 1.1944]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001671.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1295 1.2927 1.409 1.1628 1.1432 1.2169]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001672.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1295 1.2927 1.409 1.1628 1.1432 1.2169]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001673.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0338 1.1838 1.2401 1.1805 1.4813 1.1295 1.2744 1.2119]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001675.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1776 1.3347 1.0123 1.0676]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001676.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1776 1.3347 1.0123 1.0676]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001678.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0415]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001679.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0282 1.0832]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001680.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0282 1.0832]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001681.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2175 1.0905 1.1523 1.3564 1.0653 1.3505 1.3475 1.0368 1.261]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001682.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2088 1.1474 1.3309 1.0401 1.1921]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001683.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2095 1.0306]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001686.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1964]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001687.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1964]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001690.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3841 1.1723 1.287 1.4304 1.3563 1.469 1.0281 1.0876 1.2985 1.2839 1.1523 1.232 1.2772 1.1218 1.4149]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001691.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3841 1.1723 1.287 1.4304 1.3563 1.469 1.0281 1.0876 1.2985 1.2839 1.1523 1.232 1.2772 1.1218 1.4149]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001692.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3841 1.1723 1.287 1.4304 1.3563 1.469 1.0281 1.0876 1.2985 1.2839 1.1523 1.232 1.2772 1.1218 1.4149]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001693.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.34 1.074]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001694.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.191 1.3208 1.2147 1.1359]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001695.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0659]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001696.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0659]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001697.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2783 1.3093 1.2017 1.1884]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001699.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0939]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001700.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001702.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2323 1.2429 1.1507]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001703.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3782]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001709.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2095 1.0306]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001710.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2095 1.0306]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001711.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2095 1.0306]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001712.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2095 1.0306]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001713.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2095 1.0306]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001714.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001715.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001721.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2067]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001723.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2521 1.0888 1.2359 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001729.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001730.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1254 1.198 1.1768 1.2427 1.0678 1.1613 1.1801]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001731.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3457]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001732.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.335]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001733.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001735.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2816 1.0743 1.2387 1.131 1.239 1.0616]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001736.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2816 1.0743 1.2387 1.131 1.239 1.0616]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001737.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1487 1.3627 1.1307 1.3669 1.2338 1.1603 1.3146 1.0324 1.087 1.3173 1.1007 1.07 1.3735 1.4539]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001738.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2523]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001739.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2523]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001740.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2523]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001741.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4172]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001742.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4172]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001743.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4172]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001744.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0697 1.1177 1.0422 1.0649 1.1312 1.0904 1.0346 1.2556 1.3053 1.0807 1.0768]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001745.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0697 1.1177 1.0422 1.0649 1.1312 1.0904 1.0346 1.2556 1.3053 1.0807 1.0768]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001746.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0697 1.1177 1.0422 1.0649 1.1312 1.0904 1.0346 1.2556 1.3053 1.0807 1.0768]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001748.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1111 1.0718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001749.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1111 1.0718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001750.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1111 1.0718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001752.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3562 1.1125 1.1873 1.3461 1.4313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001753.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3562 1.1125 1.1873 1.3461 1.4313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001754.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0355]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001757.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3943 1.386]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001758.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001759.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001760.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001761.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001762.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001763.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001764.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001765.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001766.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001767.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001768.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.261 1.042 1.2357 1.2181 1.3573 1.1194 1.0664 1.1048 1.2896 1.4323 1.2687]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001773.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.0695]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001774.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.0695]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001775.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3226]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001779.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1686 1.0799]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001780.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3066 1.1678 1.1537 1.0116 1.2324 1.3885]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001781.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3066 1.1678 1.1537 1.0116 1.2324 1.3885]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001782.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.0919 1.2404 1.3115 1.4192 1.4631 1.3047 1.3574 1.1441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001783.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.0919 1.2404 1.3115 1.4192 1.4631 1.3047 1.3574 1.1441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001784.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.0919 1.2404 1.3115 1.4192 1.4631 1.3047 1.3574 1.1441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001786.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001787.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001788.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.087 1.147]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001789.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0137 1.3517 1.4374 1.424]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001790.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0137 1.3517 1.4374 1.424]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001791.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0137 1.3517 1.4374 1.424]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001792.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001794.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001796.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0325 1.3337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001797.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.169 1.4086 1.2435 1.0387 1.4234 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001798.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3001 1.3775]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001799.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4376 1.1876 1.3234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001800.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4376 1.1876 1.3234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001801.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4376 1.1876 1.3234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001802.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3156 1.1853]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001803.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3156 1.1853]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001805.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0133]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001806.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0133]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001807.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001808.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001809.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001810.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001811.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001812.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001813.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001814.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001815.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001816.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001817.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001818.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001819.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001820.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001821.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2146]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001822.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3023]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001823.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001824.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001825.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001826.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001827.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001828.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001829.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001830.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001831.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001832.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001833.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001834.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001835.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001836.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001837.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0659]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001838.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0913]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001839.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2092 1.2012 1.2004 1.0987 1.2662 1.1445 1.2515 1.0877 1.2308 1.0146 1.0347]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001840.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001841.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001842.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001843.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001844.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001845.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001846.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001847.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001848.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001849.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001850.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001851.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1191 1.3373 1.4758 1.3819 1.3537]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001852.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001853.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001854.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001855.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001856.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001857.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001858.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001859.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001860.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001861.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001862.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001863.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001864.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001865.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001868.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.34 1.3341 1.1962 1.1168 1.2329 1.0755 1.1467 1.0857 1.0195 1.1167 1.413]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001869.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.34 1.3341 1.1962 1.1168 1.2329 1.0755 1.1467 1.0857 1.0195 1.1167 1.413]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001870.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001871.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001872.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001873.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001874.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001875.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001876.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001879.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001880.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001881.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001882.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001883.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001884.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001885.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001886.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001887.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001888.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2323 1.2429 1.1507]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001889.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1401 1.1537]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001890.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1401 1.1537]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001891.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001892.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001893.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001894.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001895.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001896.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001897.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001898.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001899.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001900.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001901.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001902.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001903.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001904.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001905.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2234 1.2273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001906.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001907.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1324 1.2246 1.1323 1.0348 1.1518 1.4062 1.3396]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001908.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1324 1.2246 1.1323 1.0348 1.1518 1.4062 1.3396]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001909.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1324 1.2246 1.1323 1.0348 1.1518 1.4062 1.3396]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001910.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0984 1.0379 1.2711]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001911.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3144 1.0185 1.0115 1.0481 1.4546 1.0459 1.4496 1.293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001912.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3144 1.0185 1.0115 1.0481 1.4546 1.0459 1.4496 1.293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001913.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3144 1.0185 1.0115 1.0481 1.4546 1.0459 1.4496 1.293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001914.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3144 1.0185 1.0115 1.0481 1.4546 1.0459 1.4496 1.293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001915.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001916.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001917.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001918.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001919.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.379 1.1509 1.2056 1.0189]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001920.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.379 1.1509 1.2056 1.0189]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001921.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.379 1.1509 1.2056 1.0189]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001922.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.379 1.1509 1.2056 1.0189]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001923.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.379 1.1509 1.2056 1.0189]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001924.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0242]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001925.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0242]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001926.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001927.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001928.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0177 1.3068]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001929.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0177 1.3068]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001930.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0177 1.3068]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001931.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0177 1.3068]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001932.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0177 1.3068]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001933.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001934.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001935.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001936.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001937.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001938.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001939.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001940.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001941.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001942.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001943.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001944.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001945.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001946.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001947.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001948.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001949.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001950.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001951.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0358 1.3669 1.2754 1.0494 1.2859]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001952.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1778 1.1851 1.1566 1.1064]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001953.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0325 1.3337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001955.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001956.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001957.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1542]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001958.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2635]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001959.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2673 1.3585 1.2351 1.3888]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001960.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3253]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001961.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001962.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001963.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3144 1.0185 1.0115 1.0481 1.4546 1.0459 1.4496 1.293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001964.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3144 1.0185 1.0115 1.0481 1.4546 1.0459 1.4496 1.293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001966.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001967.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001968.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001969.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001973.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2559 1.0411 1.2715 1.3184]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001974.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2559 1.0411 1.2715 1.3184]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001975.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.201 1.4511]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001976.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4361 1.3512 1.424]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001977.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0652 1.3586 1.242 1.1107 1.1944]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001978.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0652 1.3586 1.242 1.1107 1.1944]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001979.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1306 1.1646 1.1511 1.057 1.1493]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001980.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1306 1.1646 1.1511 1.057 1.1493]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001983.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1776 1.3347 1.0123 1.0676]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001984.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0415]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001985.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0949 1.206]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001986.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.137 1.1783]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001987.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3336 1.1284 1.227 1.1123 1.1955 1.3635 1.0413]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001988.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3336 1.1284 1.227 1.1123 1.1955 1.3635 1.0413]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001989.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0723 1.2011 1.3391 1.1489 1.1474 1.0335]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001990.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1256 1.0654]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001991.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1256 1.0654]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001992.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3841 1.1723 1.287 1.4304 1.3563 1.469 1.0281 1.0876 1.2985 1.2839 1.1523 1.232 1.2772 1.1218 1.4149]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001993.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4017 1.2871 1.0449 1.2869 1.3957 1.246 1.3421 1.0901 1.049 1.146 1.3777 1.3273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001995.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.029 1.102 1.3175]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001996.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0417 1.3097 1.1494]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001997.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.348 1.1043 1.0665 1.4448 1.4122]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001998.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.286]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002001.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2694]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002002.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0286 1.186]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002003.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3012 1.0477]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002004.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.414 1.2287 1.3823 1.3763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002006.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1527 1.2312 1.2637 1.2645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002007.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1527 1.2312 1.2637 1.2645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002008.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2798 1.321 1.3091 1.0725 1.1324 1.1356 1.3548 1.1002 1.0155 1.0806]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002009.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1933 1.0171 1.1628 1.2245 1.0234 1.0389 1.0834]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002010.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1933 1.0171 1.1628 1.2245 1.0234 1.0389 1.0834]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002011.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1105 1.0516]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002014.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0207]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002015.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3714]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002019.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002020.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002024.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2691]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002025.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2859]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002026.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3948 1.0915]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002027.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0542 1.3148 1.2589 1.4424]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002028.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0716 1.0402 1.2713 1.1127 1.4734 1.0883 1.2271]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002029.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1485 1.0747 1.0474 1.3841 1.0347 1.2545 1.4052]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002030.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2492 1.1554 1.0553]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002031.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2492 1.1554 1.0553]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002033.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3047 1.1845]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002034.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0551 1.2978]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002035.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1208 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002036.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0986]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002037.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2468 1.0382 1.0773]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002038.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.23 1.1966]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002039.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0617 1.3322 1.1368 1.1124 1.1722]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002041.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002042.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002043.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002044.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002045.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002046.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1057 1.1904]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002047.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1057 1.1904]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002048.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1057 1.1904]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002049.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2673 1.3585 1.2351 1.3888]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002050.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2673 1.3585 1.2351 1.3888]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002051.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2673 1.3585 1.2351 1.3888]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002053.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3253]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002054.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002055.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002056.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002057.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002058.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002059.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002060.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002061.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002062.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002063.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002064.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002065.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002066.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002067.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2789 1.0191 1.1662]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002070.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.169 1.4086 1.2435 1.0387 1.4234 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002071.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2559 1.0411 1.2715 1.3184]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002072.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2559 1.0411 1.2715 1.3184]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002073.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0338 1.1838 1.2401 1.1805 1.4813 1.1295 1.2744 1.2119]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002074.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1776 1.3347 1.0123 1.0676]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002075.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1604 1.2529 1.2553]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002076.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1604 1.2529 1.2553]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002077.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1583 1.1798 1.373]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002078.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1583 1.1798 1.373]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002079.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1583 1.1798 1.373]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002080.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1583 1.1798 1.373]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002081.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1583 1.1798 1.373]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002082.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2088 1.1474 1.3309 1.0401 1.1921]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002084.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.137 1.1783]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002086.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0607 1.0462 1.3976 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002087.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3453]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002088.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1545 1.1026 1.3005]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002089.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1545 1.1026 1.3005]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002090.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.286]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002093.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.025 1.2632 1.0793]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002094.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.025 1.2632 1.0793]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002095.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.025 1.2632 1.0793]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002096.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.025 1.2632 1.0793]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002097.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.025 1.2632 1.0793]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002098.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1934]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002099.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.109 1.0539 1.3559 1.4501]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002100.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.109 1.0539 1.3559 1.4501]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002101.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1429]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002102.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002103.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2144 1.4619]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002104.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1527 1.2312 1.2637 1.2645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002107.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2798 1.321 1.3091 1.0725 1.1324 1.1356 1.3548 1.1002 1.0155 1.0806]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002108.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2798 1.321 1.3091 1.0725 1.1324 1.1356 1.3548 1.1002 1.0155 1.0806]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002109.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1933 1.0171 1.1628 1.2245 1.0234 1.0389 1.0834]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002110.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1105 1.0516]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002111.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0941 1.0995 1.3236 1.1913 1.1285]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002112.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002113.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002114.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1544]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002115.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002116.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002117.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002118.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002119.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002120.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002128.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0146]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002131.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2699 1.0158]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002134.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0167]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002135.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1463]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002137.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4017 1.2871 1.0449 1.2869 1.3957 1.246 1.3421 1.0901 1.049 1.146 1.3777 1.3273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002138.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.029 1.102 1.3175]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002139.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0412]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002141.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2047]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002144.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.065]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002147.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0185]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002150.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1429]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002151.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.341 1.0231 1.2322 1.3017]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002152.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0753]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002153.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.272]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002155.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0292]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002156.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0785]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002157.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0275 1.2791]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002158.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0275 1.2791]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002162.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1359]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002165.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0173 1.1067 1.2355]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002166.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0792]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002168.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0492 1.0264 1.0191 1.0769 1.4468 1.1342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002170.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2947]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002171.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3715]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002177.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002180.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1327 1.1733]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002181.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3798]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002182.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0642]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002183.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0986]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002185.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1302 1.1838 1.1564 1.0757 1.0377]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002188.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1102 1.0269]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002189.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1102 1.0269]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002192.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0118 1.0491]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002194.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1929 1.2485]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002197.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4629]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002203.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3197]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002204.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1056 1.12 1.2937 1.1116 1.0538]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002205.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.302]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002209.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0222]\n", "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n", "\u001b[34m\u001b[1mval: \u001b[0mFast image access βœ… (ping: 0.0Β±0.0 ms, read: 43.3Β±10.3 MB/s, size: 143.4 KB)\n", "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /content/smartvision_dataset/detection/labels.cache... 2210 images, 0 backgrounds, 1687 corrupt: 100% ━━━━━━━━━━━━ 2210/2210 237.7Mit/s 0.0s\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000001.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000002.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000003.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000004.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000005.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000006.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000010.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000011.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000012.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000013.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000014.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000015.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000016.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0568]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000017.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0568]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000018.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0568]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000019.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0568]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000020.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0568]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000021.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0377]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000022.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000023.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000024.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000025.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000026.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000027.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000028.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000029.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000030.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000031.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000032.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000033.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000034.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000035.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000036.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000037.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000038.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000039.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000040.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000043.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000044.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000045.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000046.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000047.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000048.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000049.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000050.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000051.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000052.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000053.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000054.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000055.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000056.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.39]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000059.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0213 1.086 1.071]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000060.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0213 1.086 1.071]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000061.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0213 1.086 1.071]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000062.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0213 1.086 1.071]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000063.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4517 1.3824]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000064.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4517 1.3824]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000065.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000066.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000067.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000068.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000069.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000070.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000071.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000072.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000073.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000074.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000075.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000076.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000077.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000078.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000079.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000080.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000081.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000082.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000083.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000085.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000086.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0573 1.1913 1.4816]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000087.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0573 1.1913 1.4816]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000088.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0573 1.1913 1.4816]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000089.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3385 1.3641 1.3384 1.4065 1.3079 1.0907 1.4273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000090.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3385 1.3641 1.3384 1.4065 1.3079 1.0907 1.4273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000091.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3488]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000092.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3492 1.1307]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000093.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3492 1.1307]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000094.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000095.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000096.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000097.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000098.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000099.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000100.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000101.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000102.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000103.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000104.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000106.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2477 1.0966 1.0254 1.1976 1.0638 1.4171 1.1151 1.0168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000107.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2477 1.0966 1.0254 1.1976 1.0638 1.4171 1.1151 1.0168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000108.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000109.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000110.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575 1.1136 1.3602 1.1078 1.3157 1.1806]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000111.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575 1.1136 1.3602 1.1078 1.3157 1.1806]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000113.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000114.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000115.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000116.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000117.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000118.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000119.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000120.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000121.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000122.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0167 1.3468 1.0565 1.3635 1.1786]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000125.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2942 1.3317 1.1404 1.0905 1.4771 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000126.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000127.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000128.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0389]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000129.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0389]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000131.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0217 1.0899]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000132.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0217 1.0899]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000133.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0857 1.2957 1.4019 1.4657 1.3516 1.2175]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000134.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0814 1.1501 1.3078 1.132]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000135.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0814 1.1501 1.3078 1.132]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000136.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0814 1.1501 1.3078 1.132]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000137.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2408 1.0816 1.0947 1.1683 1.3866 1.2441 1.3405 1.2633]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000149.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3813 1.1073 1.1568 1.1564 1.0852 1.1014 1.0644 1.087 1.0961 1.0906 1.0491 1.0929 1.1042 1.3517 1.2582 1.3639 1.0882 1.1112 1.0979]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000150.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3813 1.1073 1.1568 1.1564 1.0852 1.1014 1.0644 1.087 1.0961 1.0906 1.0491 1.0929 1.1042 1.3517 1.2582 1.3639 1.0882 1.1112 1.0979]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000151.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3813 1.1073 1.1568 1.1564 1.0852 1.1014 1.0644 1.087 1.0961 1.0906 1.0491 1.0929 1.1042 1.3517 1.2582 1.3639 1.0882 1.1112 1.0979]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000152.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3813 1.1073 1.1568 1.1564 1.0852 1.1014 1.0644 1.087 1.0961 1.0906 1.0491 1.0929 1.1042 1.3517 1.2582 1.3639 1.0882 1.1112 1.0979]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000153.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3447 1.0975]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000154.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000155.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000156.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000157.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000158.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000159.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000160.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000161.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000162.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000163.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000164.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000165.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.46 1.4187]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000166.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000167.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000168.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000169.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1948 1.1834]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000170.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.032]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000171.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000172.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000173.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000174.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000175.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000176.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000177.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000178.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000179.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000180.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000181.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000182.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000183.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000184.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0118 1.3348 1.0334]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000185.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000186.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000187.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000188.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000189.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000190.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000191.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000192.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000193.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000194.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000196.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0864]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000198.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3322 1.3673]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000199.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.0291 1.2534]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000200.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.0291 1.2534]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000201.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.0291 1.2534]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000202.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000203.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000204.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000205.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000206.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000207.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000208.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000209.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000210.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000211.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000212.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000213.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000214.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000215.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000216.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.148]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000217.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2216]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000218.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2216]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000219.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0472 1.0888 1.1318 1.3638 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000220.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0472 1.0888 1.1318 1.3638 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000221.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0472 1.0888 1.1318 1.3638 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000222.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0472 1.0888 1.1318 1.3638 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000223.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0472 1.0888 1.1318 1.3638 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000224.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0472 1.0888 1.1318 1.3638 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000225.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.0657 1.0768]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000226.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.0657 1.0768]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000231.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3488]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000232.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.379 1.1509 1.2056 1.0189]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000233.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.034 1.0878]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000234.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.034 1.0878]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000235.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.034 1.0878]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000236.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.034 1.0878]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000237.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.034 1.0878]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000239.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000240.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000241.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000242.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000243.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000244.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000245.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000246.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000247.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000248.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000249.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000250.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3583]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000251.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3583]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000252.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3583]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000253.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000254.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0177 1.3068]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000257.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0377]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000259.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0872]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000260.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2216]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000261.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0819 1.0649]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000262.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0984 1.0379 1.2711]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000263.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3488]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000264.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2424 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000265.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000266.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2477 1.0966 1.0254 1.1976 1.0638 1.4171 1.1151 1.0168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000268.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0251 1.3488 1.0667]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000270.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000271.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000272.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000273.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000274.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000275.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000276.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000277.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000278.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000279.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000280.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000281.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000282.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000283.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000284.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.1607 1.1498 1.2068 1.1738 1.1713 1.177 1.4336 1.179 1.1247 1.1285 1.1228 1.1898 1.0465 1.0555 1.1407 1.162 1.1592 1.1576 1.3573 1.1937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000285.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2962 1.1061 1.2492]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000286.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2962 1.1061 1.2492]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000287.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2962 1.1061 1.2492]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000288.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2962 1.1061 1.2492]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000289.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2962 1.1061 1.2492]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000290.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0607 1.0462 1.3976 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000292.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0711 1.0652 1.0427 1.0439]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000293.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0711 1.0652 1.0427 1.0439]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000294.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0507 1.1707 1.1062 1.0498 1.4468 1.2013 1.0681]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000299.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2408 1.0816 1.0947 1.1683 1.3866 1.2441 1.3405 1.2633]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000300.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2408 1.0816 1.0947 1.1683 1.3866 1.2441 1.3405 1.2633]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000301.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3447 1.0975]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000302.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0724 1.3465]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000303.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4128]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000304.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3465 1.4145 1.0238 1.3548]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000305.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3465 1.4145 1.0238 1.3548]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000306.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0648 1.3159]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000307.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0584]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000308.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1916]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000309.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000310.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000311.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000312.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000313.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3171 1.1059 1.2]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000314.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000316.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0195 1.0134 1.2051 1.0651 1.3172 1.0999 1.3003 1.238 1.432 1.4685 1.1767 1.2398 1.4213 1.2114 1.2702 1.0145 1.0843]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000318.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2184]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000320.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.098 1.3046 1.4014]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000321.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.098 1.3046 1.4014]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000322.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.098 1.3046 1.4014]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000324.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2433]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000325.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2128 1.3769 1.337 1.2589 1.2109 1.1046 1.1607 1.3543 1.4789 1.4321 1.4126 1.298 1.3879]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000328.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3795 1.3697]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000329.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1473 1.1461]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000330.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1473 1.1461]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000331.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1473 1.1461]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000332.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1473 1.1461]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000333.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0331 1.082 1.2072 1.178]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000335.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2599 1.1365]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000336.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.3181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000337.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.3181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000338.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.3181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000339.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.3181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000341.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000342.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000344.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000345.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0919 1.0994 1.0493]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000350.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2861 1.0694 1.2839 1.3754]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000351.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2861 1.0694 1.2839 1.3754]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000352.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2861 1.0694 1.2839 1.3754]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000354.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2424 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000355.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000356.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000357.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000358.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000359.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0432 1.261]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000360.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0728]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000361.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1293 1.0559 1.0301]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000362.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1293 1.0559 1.0301]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000364.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1415]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000365.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0879 1.0447 1.0649]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000367.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1433 1.0584]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000368.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1658 1.0536 1.1723 1.0751]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000369.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0988]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000370.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0988]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000371.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0988]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000372.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0988]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000373.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1191 1.3373 1.4758 1.3819 1.3537]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000374.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.067 1.0303 1.042 1.0528 1.0443 1.0271 1.0577 1.0358 1.0675 1.0429 1.047 1.0727 1.0297]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000382.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1023 1.0417 1.2845 1.0395]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000388.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1308 1.4184]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000390.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1625 1.2811 1.0798 1.2282 1.3015]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000391.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.266]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000392.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.266]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000393.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.266]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000394.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.266]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000395.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.266]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000396.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1867 1.1554 1.1629 1.1675]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000397.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000398.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000399.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000400.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000401.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.078 1.2619]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000402.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.078 1.2619]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000403.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.078 1.2619]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000406.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.361 1.2929 1.0871 1.0424 1.0563 1.1481 1.2253 1.0351 1.2423 1.4025 1.2569 1.4244]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000407.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.361 1.2929 1.0871 1.0424 1.0563 1.1481 1.2253 1.0351 1.2423 1.4025 1.2569 1.4244]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000408.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.361 1.2929 1.0871 1.0424 1.0563 1.1481 1.2253 1.0351 1.2423 1.4025 1.2569 1.4244]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000409.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.361 1.2929 1.0871 1.0424 1.0563 1.1481 1.2253 1.0351 1.2423 1.4025 1.2569 1.4244]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000410.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.361 1.2929 1.0871 1.0424 1.0563 1.1481 1.2253 1.0351 1.2423 1.4025 1.2569 1.4244]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000413.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.025 1.0101 1.1705]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000414.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3054 1.2719 1.3924 1.2629 1.3638 1.1235 1.2335 1.0921 1.1293 1.0976 1.0477 1.1907 1.2481 1.2469 1.4638 1.0224 1.0726]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000418.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2735 1.3458 1.1181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000419.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2735 1.3458 1.1181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000420.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2735 1.3458 1.1181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000421.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2735 1.3458 1.1181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000422.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2735 1.3458 1.1181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000429.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000430.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000431.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3385 1.3641 1.3384 1.4065 1.3079 1.0907 1.4273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000433.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.0657 1.0768]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000434.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0242]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000436.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000437.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.05 1.3044 1.4222 1.4496 1.4635 1.4757 1.1427 1.3243 1.4071 1.079 1.3459 1.3661 1.3827 1.3939 1.439 1.4925 1.0395 1.3579 1.3657 1.2323 1.3455 1.2116 1.038 1.3302]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000438.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.05 1.3044 1.4222 1.4496 1.4635 1.4757 1.1427 1.3243 1.4071 1.079 1.3459 1.3661 1.3827 1.3939 1.439 1.4925 1.0395 1.3579 1.3657 1.2323 1.3455 1.2116 1.038 1.3302]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000439.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.05 1.3044 1.4222 1.4496 1.4635 1.4757 1.1427 1.3243 1.4071 1.079 1.3459 1.3661 1.3827 1.3939 1.439 1.4925 1.0395 1.3579 1.3657 1.2323 1.3455 1.2116 1.038 1.3302]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000440.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4233 1.0268 1.4472 1.4143]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000442.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0102 1.3403 1.041 1.0854 1.4127 1.4578 1.0778 1.2834]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000443.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3599]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000444.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3599]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000445.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4707 1.0701]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000446.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2014 1.026 1.3853 1.2427 1.1528 1.1792 1.0983 1.2052]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000447.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0469 1.0219 1.2866 1.4379 1.3544 1.4822 1.0766 1.2366 1.2719 1.1818]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000448.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0469 1.0219 1.2866 1.4379 1.3544 1.4822 1.0766 1.2366 1.2719 1.1818]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000449.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0469 1.0219 1.2866 1.4379 1.3544 1.4822 1.0766 1.2366 1.2719 1.1818]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000450.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0469 1.0219 1.2866 1.4379 1.3544 1.4822 1.0766 1.2366 1.2719 1.1818]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000451.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0469 1.0219 1.2866 1.4379 1.3544 1.4822 1.0766 1.2366 1.2719 1.1818]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000452.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276 1.0754 1.0253]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000453.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2059 1.1574]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000454.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0952 1.219 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000455.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4092]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000456.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1614 1.261 1.0286]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000458.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4164 1.268 1.1395 1.0109]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000459.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0389]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000460.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2171]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000461.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2171]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000462.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2171]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000463.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2171]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000464.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2171]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000466.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3606 1.0951]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000467.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0814 1.1501 1.3078 1.132]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000468.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3813 1.1073 1.1568 1.1564 1.0852 1.1014 1.0644 1.087 1.0961 1.0906 1.0491 1.0929 1.1042 1.3517 1.2582 1.3639 1.0882 1.1112 1.0979]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000469.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3023]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000470.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3023]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000471.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3023]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000472.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0732 1.0623]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000473.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3306 1.4458 1.4114 1.2858 1.1532 1.4603 1.4826]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000474.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2569 1.0143 1.1132 1.404 1.1943 1.0717 1.2386]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000475.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3266 1.0494 1.3057 1.2081]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000478.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000479.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000480.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000481.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000482.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000483.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000484.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000485.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000486.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000487.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000488.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000489.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000490.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000491.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.136 1.2483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000492.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000493.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000494.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0112 1.0766 1.0268 1.0716 1.0643 1.1068 1.0362 1.087 1.0399 1.0375 1.0568 1.174 1.031]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000499.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.367 1.3068 1.3222 1.3171 1.4502 1.3871 1.343]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000500.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.367 1.3068 1.3222 1.3171 1.4502 1.3871 1.343]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000501.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1948 1.1834]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000502.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1254 1.2028 1.1766 1.2052 1.2084 1.1524 1.1032 1.1977 1.2937 1.0171 1.0787 1.2017 1.2965 1.1468]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000503.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2296]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000504.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2716 1.3971]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000506.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3134 1.214 1.2461 1.2872 1.209 1.1122 1.1169 1.462 1.4809 1.3203 1.0778 1.1088 1.0905 1.0872 1.1193 1.0923 1.0567 1.0825 1.4075 1.3613]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000507.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3134 1.214 1.2461 1.2872 1.209 1.1122 1.1169 1.462 1.4809 1.3203 1.0778 1.1088 1.0905 1.0872 1.1193 1.0923 1.0567 1.0825 1.4075 1.3613]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000508.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3134 1.214 1.2461 1.2872 1.209 1.1122 1.1169 1.462 1.4809 1.3203 1.0778 1.1088 1.0905 1.0872 1.1193 1.0923 1.0567 1.0825 1.4075 1.3613]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000509.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3134 1.214 1.2461 1.2872 1.209 1.1122 1.1169 1.462 1.4809 1.3203 1.0778 1.1088 1.0905 1.0872 1.1193 1.0923 1.0567 1.0825 1.4075 1.3613]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000510.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000511.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.39]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000512.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1057 1.1904]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000516.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000517.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.03]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000518.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.22]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000519.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1256 1.1202]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000520.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0432 1.261]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000521.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4123]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000523.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4325]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000524.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4325]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000526.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000527.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0916]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000528.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.195]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000529.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000530.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000531.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000532.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000533.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000534.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000535.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000538.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1539]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000539.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1539]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000541.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2076]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000542.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2076]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000543.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2076]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000545.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1614 1.261 1.0286]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000549.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0812]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000550.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2837 1.2465 1.3159]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000551.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2852]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000552.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.459 1.3948]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000554.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0712]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000555.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0712]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000559.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0119 1.1955 1.0194 1.0647 1.0211 1.0261 1.2364 1.0347 1.314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000565.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3823 1.3545 1.4137 1.154 1.3619 1.3342 1.3551 1.3339 1.359]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000566.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3243 1.225]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000568.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0282]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000569.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0282]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000572.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1074 1.0704]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000576.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3914 1.3058 1.2712 1.2336 1.444 1.2158]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000577.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4566]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000580.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1967]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000581.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2598 1.3114 1.3175 1.0246 1.4372 1.0436 1.3609 1.191 1.3376 1.2616 1.2555 1.3 1.2475 1.2016]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000582.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2598 1.3114 1.3175 1.0246 1.4372 1.0436 1.3609 1.191 1.3376 1.2616 1.2555 1.3 1.2475 1.2016]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000583.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2113 1.2059 1.4601]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000584.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1425 1.1342 1.3027 1.2293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000586.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0571 1.327 1.2399 1.1931 1.1115 1.1608 1.0154 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000587.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0453]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000588.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3536 1.0144 1.0818 1.1754 1.2144 1.2513 1.3003 1.3568 1.315 1.1019 1.2382]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000589.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3536 1.0144 1.0818 1.1754 1.2144 1.2513 1.3003 1.3568 1.315 1.1019 1.2382]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000592.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2468 1.0382 1.0773]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000593.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2468 1.0382 1.0773]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000594.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2468 1.0382 1.0773]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000595.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.032]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000596.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000597.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000598.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0118 1.3348 1.0334]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000599.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000600.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000601.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000602.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000603.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000604.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000605.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000606.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000608.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1339]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000609.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1256 1.1202]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000610.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000611.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0194 1.2706 1.0633]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000612.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1784 1.3131]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000613.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4367 1.0919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000614.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4233 1.0268 1.4472 1.4143]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000615.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0414 1.2557 1.1841 1.3403 1.4323]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000617.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1415]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000618.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2477 1.0966 1.0254 1.1976 1.0638 1.4171 1.1151 1.0168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000621.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2014 1.026 1.3853 1.2427 1.1528 1.1792 1.0983 1.2052]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000622.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000623.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000624.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0879 1.0447 1.0649]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000625.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1897 1.1388]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000626.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000627.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000628.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.1607 1.1498 1.2068 1.1738 1.1713 1.177 1.4336 1.179 1.1247 1.1285 1.1228 1.1898 1.0465 1.0555 1.1407 1.162 1.1592 1.1576 1.3573 1.1937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000630.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3847 1.0703 1.4425 1.1328 1.3777 1.3455]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000631.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4322 1.4157 1.036]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000632.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0516 1.3319 1.1111 1.0484 1.0286 1.1049]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000633.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2916 1.275 1.4302 1.4422 1.4361 1.1156 1.4189 1.4698 1.4517 1.4587 1.4151 1.3592 1.2751 1.4392 1.4403 1.1641 1.4179 1.4494 1.3842 1.451 1.3966 1.4557 1.4013 1.3754 1.3953 1.3896\n", " 1.4612 1.299 1.2979 1.4233 1.2894]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000634.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1658 1.0536 1.1723 1.0751]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000635.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1658 1.0536 1.1723 1.0751]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000636.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1658 1.0536 1.1723 1.0751]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000637.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1173 1.168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000638.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1173 1.168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000639.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0952 1.219 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000640.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1152 1.1614 1.0889 1.138]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000641.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3154 1.3377 1.0388 1.0693 1.0537 1.275 1.3388 1.2903 1.2488 1.2648]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000642.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0607 1.0462 1.3976 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000643.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1614 1.261 1.0286]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000644.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0288 1.0281 1.0117 1.0186 1.2035 1.0144 1.0121]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000645.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0389]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000647.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0454]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000648.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0203]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000652.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0507 1.1707 1.1062 1.0498 1.4468 1.2013 1.0681]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000653.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2408 1.0816 1.0947 1.1683 1.3866 1.2441 1.3405 1.2633]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000654.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1599 1.015]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000656.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2265 1.3069]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000657.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3736 1.3781 1.358 1.2635 1.3725 1.0657 1.4004 1.3866 1.352 1.3398 1.3075 1.3549 1.375 1.4535 1.3392 1.364 1.3276 1.3996 1.3412 1.3606 1.354]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000658.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0732 1.0623]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000659.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1023 1.0417 1.2845 1.0395]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000660.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1023 1.0417 1.2845 1.0395]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000661.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1023 1.0417 1.2845 1.0395]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000662.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.46 1.4187]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000664.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3465 1.4145 1.0238 1.3548]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000665.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1916]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000666.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.068 1.2033 1.0507 1.0876 1.3228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000667.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.068 1.2033 1.0507 1.0876 1.3228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000668.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2417 1.3584 1.1288]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000669.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2417 1.3584 1.1288]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000670.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2852]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000671.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1625 1.2811 1.0798 1.2282 1.3015]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000672.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1625 1.2811 1.0798 1.2282 1.3015]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000673.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1625 1.2811 1.0798 1.2282 1.3015]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000674.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1625 1.2811 1.0798 1.2282 1.3015]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000675.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.077 1.1251 1.0913 1.3971 1.0682 1.1161 1.1624 1.1036 1.3631 1.1464 1.2978 1.1313 1.1067 1.2159 1.1498 1.0697 1.1656 1.026]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000676.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000677.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.272 1.4368]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000678.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.272 1.4368]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000679.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1254 1.2028 1.1766 1.2052 1.2084 1.1524 1.1032 1.1977 1.2937 1.0171 1.0787 1.2017 1.2965 1.1468]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000680.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000681.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000682.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000683.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000684.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000685.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000686.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000687.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000688.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000689.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3322 1.3673]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000690.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3322 1.3673]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000691.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3322 1.3673]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000693.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000694.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0946]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000695.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0946]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000696.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0946]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000697.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000698.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000699.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000700.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.22]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000701.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.22]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000702.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4367 1.0919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000703.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4367 1.0919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000704.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4233 1.0268 1.4472 1.4143]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000705.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4233 1.0268 1.4472 1.4143]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000706.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3754 1.4037]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000707.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3754 1.4037]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000708.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0251 1.3488 1.0667]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000709.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0916]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000710.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0916]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000711.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0916]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000712.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.0953 1.3587]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000713.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.0953 1.3587]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000714.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.0953 1.3587]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000715.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.1607 1.1498 1.2068 1.1738 1.1713 1.177 1.4336 1.179 1.1247 1.1285 1.1228 1.1898 1.0465 1.0555 1.1407 1.162 1.1592 1.1576 1.3573 1.1937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000716.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.1607 1.1498 1.2068 1.1738 1.1713 1.177 1.4336 1.179 1.1247 1.1285 1.1228 1.1898 1.0465 1.0555 1.1407 1.162 1.1592 1.1576 1.3573 1.1937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000717.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.046 1.2007 1.3681]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000718.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.046 1.2007 1.3681]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000719.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2916 1.275 1.4302 1.4422 1.4361 1.1156 1.4189 1.4698 1.4517 1.4587 1.4151 1.3592 1.2751 1.4392 1.4403 1.1641 1.4179 1.4494 1.3842 1.451 1.3966 1.4557 1.4013 1.3754 1.3953 1.3896\n", " 1.4612 1.299 1.2979 1.4233 1.2894]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000720.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2916 1.275 1.4302 1.4422 1.4361 1.1156 1.4189 1.4698 1.4517 1.4587 1.4151 1.3592 1.2751 1.4392 1.4403 1.1641 1.4179 1.4494 1.3842 1.451 1.3966 1.4557 1.4013 1.3754 1.3953 1.3896\n", " 1.4612 1.299 1.2979 1.4233 1.2894]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000721.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2916 1.275 1.4302 1.4422 1.4361 1.1156 1.4189 1.4698 1.4517 1.4587 1.4151 1.3592 1.2751 1.4392 1.4403 1.1641 1.4179 1.4494 1.3842 1.451 1.3966 1.4557 1.4013 1.3754 1.3953 1.3896\n", " 1.4612 1.299 1.2979 1.4233 1.2894]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000722.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2916 1.275 1.4302 1.4422 1.4361 1.1156 1.4189 1.4698 1.4517 1.4587 1.4151 1.3592 1.2751 1.4392 1.4403 1.1641 1.4179 1.4494 1.3842 1.451 1.3966 1.4557 1.4013 1.3754 1.3953 1.3896\n", " 1.4612 1.299 1.2979 1.4233 1.2894]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000723.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2916 1.275 1.4302 1.4422 1.4361 1.1156 1.4189 1.4698 1.4517 1.4587 1.4151 1.3592 1.2751 1.4392 1.4403 1.1641 1.4179 1.4494 1.3842 1.451 1.3966 1.4557 1.4013 1.3754 1.3953 1.3896\n", " 1.4612 1.299 1.2979 1.4233 1.2894]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000724.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0952 1.219 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000725.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0952 1.219 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000726.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0952 1.219 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000727.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3154 1.3377 1.0388 1.0693 1.0537 1.275 1.3388 1.2903 1.2488 1.2648]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000728.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3154 1.3377 1.0388 1.0693 1.0537 1.275 1.3388 1.2903 1.2488 1.2648]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000729.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000730.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000731.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000732.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000733.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0389]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000734.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481 1.2492 1.4525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000735.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481 1.2492 1.4525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000736.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481 1.2492 1.4525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000737.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481 1.2492 1.4525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000738.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481 1.2492 1.4525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000743.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3606 1.0951]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000744.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.0137 1.1085]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000745.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1202 1.2178 1.0562 1.0855 1.0295 1.4284 1.0975 1.1485 1.1605 1.0695 1.0893 1.1028 1.0967 1.31 1.1245]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000748.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.136 1.2483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000749.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.459 1.3948]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000750.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.459 1.3948]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000751.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000752.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000753.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000754.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000755.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000756.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000757.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000758.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000759.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000760.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000761.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000762.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000763.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000764.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.367 1.3068 1.3222 1.3171 1.4502 1.3871 1.343]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000765.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.032]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000766.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.39]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000767.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2059 1.1574]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000768.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3986 1.4119 1.3449 1.4049 1.163 1.408 1.0589 1.4209 1.3837 1.3754 1.246 1.4011 1.1946 1.1903 1.3871 1.252 1.3917]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000769.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3986 1.4119 1.3449 1.4049 1.163 1.408 1.0589 1.4209 1.3837 1.3754 1.246 1.4011 1.1946 1.1903 1.3871 1.252 1.3917]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000772.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.314 1.1316 1.061]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000775.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0668]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000777.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2649 1.2485 1.2453 1.2704 1.2382 1.2625 1.2872 1.234 1.2177 1.1553]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000778.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0492 1.0264 1.0191 1.0769 1.4468 1.1342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000779.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0546 1.2324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000780.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1284]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000784.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1327 1.0996]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000785.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1097]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000789.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0772]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000791.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3269 1.2657 1.2163 1.329 1.331 1.3371 1.0877 1.3574 1.2046 1.36 1.1283 1.2561]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000792.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.091]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000793.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.13]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000795.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0724 1.1465 1.1757 1.114 1.1201 1.1337 1.0671 1.0832 1.0644 1.0992 1.1717 1.1067 1.2286 1.0985 1.1826 1.0288 1.1958 1.085 1.1086 1.0146 1.1079 1.107 1.0316 1.0853 1.132 1.0802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000800.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.05]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000801.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3756 1.395 1.3772]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000802.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1524 1.0273 1.1426 1.1536 1.077 1.3664 1.0455 1.1725 1.0692 1.3674 1.0485 1.1537 1.0777]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000803.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3229 1.464 1.2557 1.1493]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000804.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3229 1.464 1.2557 1.1493]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000808.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2761 1.1774 1.1593 1.2493 1.16 1.1589 1.3185 1.1951 1.1904 1.3984 1.1755 1.1715 1.167 1.184 1.4111 1.2075]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000810.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4552 1.4155 1.3837 1.2465]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000811.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0632 1.0241 1.3898 1.2013]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000812.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.32 1.2879 1.0713 1.1626 1.2899]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000814.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2653 1.0717 1.0413 1.0219 1.1907 1.3297 1.3178 1.131 1.0974 1.033 1.0292 1.0212 1.0834 1.127]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000816.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2162]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000818.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000820.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4657 1.4056 1.4249 1.3656 1.3089 1.363]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000821.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0191 1.4111 1.0328]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000822.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.054 1.4505 1.2707 1.2308 1.1827 1.343 1.2302 1.0518 1.253 1.2448 1.3444]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000823.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.054 1.4505 1.2707 1.2308 1.1827 1.343 1.2302 1.0518 1.253 1.2448 1.3444]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000824.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4292]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000825.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2141 1.2382 1.2764 1.2476 1.3899 1.1673 1.2986 1.2267 1.2142 1.436 1.2327]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000826.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2642 1.3742 1.2885 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000827.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4312 1.3573 1.3779 1.3922 1.3743 1.3659 1.4256 1.4039 1.3227 1.3479 1.3559 1.308 1.3917 1.3706 1.3534]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000829.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2115]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000830.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3753]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000831.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0505 1.0278]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000832.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2782]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000833.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2433 1.0302 1.0205 1.1711 1.2713]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000834.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4256 1.3302]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000835.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2771 1.3713]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000836.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2771 1.3713]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000837.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3939]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000838.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3643 1.0459 1.4085]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000839.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0589 1.3825]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000840.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2816 1.0492 1.2663 1.2933 1.3978 1.273 1.2444 1.296 1.2382 1.2494 1.2247 1.1607 1.1884 1.238 1.2492 1.0777 1.2691 1.152 1.1418 1.2344]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000841.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3301 1.3912 1.4421 1.4759 1.3544 1.2422 1.0575 1.4611]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000844.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.171 1.2591 1.2601]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000848.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.297 1.1302 1.2356 1.3275 1.4435 1.0936 1.224]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000850.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000851.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000852.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000853.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000854.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000855.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000858.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2234 1.2273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000860.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000861.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1706 1.0647 1.0452]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000862.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1706 1.0647 1.0452]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000863.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000864.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000866.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4376 1.1876 1.3234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000868.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1174]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000869.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000870.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1076 1.4457 1.4495 1.2955 1.4506]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000871.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0963 1.2388]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000872.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0963 1.2388]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000873.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2136]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000875.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3206 1.1809]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000876.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2079 1.3944 1.2641 1.2551 1.3111 1.3779]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000877.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2079 1.3944 1.2641 1.2551 1.3111 1.3779]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000878.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2079 1.3944 1.2641 1.2551 1.3111 1.3779]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000879.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0815]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000880.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0815]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000881.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000882.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000883.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000884.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000885.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000886.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000887.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3847 1.0703 1.4425 1.1328 1.3777 1.3455]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000890.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.115 1.0837]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000891.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4092]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000892.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276 1.3572 1.0554 1.4834 1.2971 1.2622 1.0802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000893.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276 1.3572 1.0554 1.4834 1.2971 1.2622 1.0802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000894.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276 1.3572 1.0554 1.4834 1.2971 1.2622 1.0802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000898.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3622 1.2944 1.4458 1.237 1.3262 1.1019 1.3491 1.2381 1.2309 1.1671]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000903.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.309]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000904.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2384 1.2567 1.2147 1.3016 1.2493]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000905.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1377]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000906.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1461]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000907.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1314 1.0269 1.1178 1.055 1.0339]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000908.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1314 1.0269 1.1178 1.055 1.0339]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000909.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1314 1.0269 1.1178 1.055 1.0339]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000910.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1314 1.0269 1.1178 1.055 1.0339]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000911.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000912.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000913.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3306 1.4458 1.4114 1.2858 1.1532 1.4603 1.4826]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000914.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3702 1.242]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000915.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3702 1.242]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000916.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.46 1.4187]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000917.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.182 1.241]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000918.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.182 1.241]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000919.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1633 1.2284 1.2117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000920.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1633 1.2284 1.2117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000921.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3266 1.0494 1.3057 1.2081]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000922.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4101]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000923.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.068 1.2033 1.0507 1.0876 1.3228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000924.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.068 1.2033 1.0507 1.0876 1.3228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000925.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.068 1.2033 1.0507 1.0876 1.3228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000927.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0237 1.2134 1.3665 1.4545 1.4121 1.0725 1.0888 1.1255 1.328 1.283]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000928.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0237 1.2134 1.3665 1.4545 1.4121 1.0725 1.0888 1.1255 1.328 1.283]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000929.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0237 1.2134 1.3665 1.4545 1.4121 1.0725 1.0888 1.1255 1.328 1.283]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000930.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1969 1.3824 1.4004 1.4641 1.493 1.2]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000931.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1969 1.3824 1.4004 1.4641 1.493 1.2]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000932.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1969 1.3824 1.4004 1.4641 1.493 1.2]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000933.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.13]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000935.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000936.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000937.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000938.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000939.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000940.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000941.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000942.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000943.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000944.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000945.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000946.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000947.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000948.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000949.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000950.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000951.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000952.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000953.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000966.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0987]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000967.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000968.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000969.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000970.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000971.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000972.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000973.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000974.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000975.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000976.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000977.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000978.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000979.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000981.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000982.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000983.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000984.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000985.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000986.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000987.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000988.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000989.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000990.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000991.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000992.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000993.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000994.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000995.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000996.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000997.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000998.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000999.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001000.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001001.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001002.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001003.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001004.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001005.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001006.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001007.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001020.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2635]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001022.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3253]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001023.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.148]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001024.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0146]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001025.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001026.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001033.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.047 1.1259]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001034.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.047 1.1259]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001035.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1188]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001037.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2507 1.1559 1.2561 1.2419]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001041.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0282 1.0832]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001046.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1965]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001049.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0412]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001051.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.286]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001056.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1377]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001057.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2694]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001059.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0286 1.186]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001060.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.414 1.2287 1.3823 1.3763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001061.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.272]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001062.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0785]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001066.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1359]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001067.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1359]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001069.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1544]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001071.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4237 1.254]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001072.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001073.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0367]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001074.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.352 1.2153]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001075.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.352 1.2153]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001076.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001083.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0443 1.2404 1.461 1.3867]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001087.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4346]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001089.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3943 1.386]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001090.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1709 1.0168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001092.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1457]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001093.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2852]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001094.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2852]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001097.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1161 1.0308 1.1174]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001100.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0379 1.2789 1.1818]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001103.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0116 1.114 1.4075 1.1921 1.2107 1.428]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001106.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001107.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001108.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0864]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001109.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1277]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001111.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0581]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001114.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001117.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.047 1.1259]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001118.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2789 1.0191 1.1662]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001119.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0194 1.2706 1.0633]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001122.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3156 1.1853]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001123.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0906]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001124.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0906]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001125.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001126.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001127.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001128.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001129.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001130.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001131.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001132.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001133.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001134.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0251 1.3488 1.0667]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001138.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2136]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001139.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2136]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001140.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0949 1.206]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001143.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0162 1.4574 1.433]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001146.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0417 1.3097 1.1494]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001148.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2092 1.2012 1.2004 1.0987 1.2662 1.1445 1.2515 1.0877 1.2308 1.0146 1.0347]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001151.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.065]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001152.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1314 1.0269 1.1178 1.055 1.0339]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001153.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1633 1.2284 1.2117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001154.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0275 1.2791]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001157.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2069]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001159.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4348]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001160.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4348]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001161.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0207]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001162.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0207]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001163.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1002 1.4521 1.4133 1.4874]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001164.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1178 1.2011]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001165.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1794]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001176.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0209 1.1373 1.3803 1.47 1.4105 1.0566]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001178.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1725 1.178]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001179.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2747 1.039]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001180.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2859]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001181.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1762]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001182.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0737 1.3978 1.0118 1.1678 1.3534 1.1618]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001185.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.052]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001186.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1193]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001187.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1193]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001188.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2193]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001190.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001191.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001192.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0105]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001193.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1188]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001195.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1305 1.3196 1.3168 1.2685 1.2089 1.2903 1.3913 1.2551 1.3263 1.2114 1.2164 1.2267 1.3943 1.1789 1.1463 1.2441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001196.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1305 1.3196 1.3168 1.2685 1.2089 1.2903 1.3913 1.2551 1.3263 1.2114 1.2164 1.2267 1.3943 1.1789 1.1463 1.2441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001197.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1305 1.3196 1.3168 1.2685 1.2089 1.2903 1.3913 1.2551 1.3263 1.2114 1.2164 1.2267 1.3943 1.1789 1.1463 1.2441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001198.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1305 1.3196 1.3168 1.2685 1.2089 1.2903 1.3913 1.2551 1.3263 1.2114 1.2164 1.2267 1.3943 1.1789 1.1463 1.2441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001199.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001200.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001201.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001202.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001203.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001205.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001206.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001207.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001208.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001209.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001215.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3449]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001216.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3449]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001217.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4627 1.2884 1.452 1.3891 1.4209]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001218.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4627 1.2884 1.452 1.3891 1.4209]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001221.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.081 1.0702 1.3911 1.2859 1.467 1.3419 1.332 1.0593 1.1911 1.0545 1.472]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001222.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.081 1.0702 1.3911 1.2859 1.467 1.3419 1.332 1.0593 1.1911 1.0545 1.472]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001223.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.081 1.0702 1.3911 1.2859 1.467 1.3419 1.332 1.0593 1.1911 1.0545 1.472]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001224.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.081 1.0702 1.3911 1.2859 1.467 1.3419 1.332 1.0593 1.1911 1.0545 1.472]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001227.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.02 1.1304 1.2213 1.1192 1.2571 1.3272 1.1754]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001228.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2942 1.3317 1.1404 1.0905 1.4771 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001230.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276 1.3572 1.0554 1.4834 1.2971 1.2622 1.0802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001231.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276 1.3572 1.0554 1.4834 1.2971 1.2622 1.0802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001232.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0454]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001233.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0454]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001234.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0454]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001235.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1551 1.3875 1.1524 1.4426 1.352 1.2997 1.3483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001236.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1551 1.3875 1.1524 1.4426 1.352 1.2997 1.3483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001237.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1191 1.3373 1.4758 1.3819 1.3537]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001238.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2265 1.3069]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001239.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2265 1.3069]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001240.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1666 1.0802 1.0646 1.0511 1.0813]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001241.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0458]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001245.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0637]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001246.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0637]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001248.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001249.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001250.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001251.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001252.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001253.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001255.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001256.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001257.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1824 1.2572 1.4396]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001259.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3802 1.0485]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001260.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3802 1.0485]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001261.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3802 1.0485]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001262.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001263.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001264.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001265.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001266.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001267.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001268.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001271.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3586 1.3574 1.2148 1.4258 1.4257 1.2811 1.3354 1.2343 1.3349 1.3575 1.185 1.3623 1.3514 1.1002 1.3435 1.1284 1.411 1.2426 1.4178 1.289 1.1359 1.4374 1.3325 1.2147]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001272.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3586 1.3574 1.2148 1.4258 1.4257 1.2811 1.3354 1.2343 1.3349 1.3575 1.185 1.3623 1.3514 1.1002 1.3435 1.1284 1.411 1.2426 1.4178 1.289 1.1359 1.4374 1.3325 1.2147]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001273.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3586 1.3574 1.2148 1.4258 1.4257 1.2811 1.3354 1.2343 1.3349 1.3575 1.185 1.3623 1.3514 1.1002 1.3435 1.1284 1.411 1.2426 1.4178 1.289 1.1359 1.4374 1.3325 1.2147]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001274.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3586 1.3574 1.2148 1.4258 1.4257 1.2811 1.3354 1.2343 1.3349 1.3575 1.185 1.3623 1.3514 1.1002 1.3435 1.1284 1.411 1.2426 1.4178 1.289 1.1359 1.4374 1.3325 1.2147]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001279.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1784 1.3131]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001280.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1784 1.3131]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001281.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1784 1.3131]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001282.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1784 1.3131]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001283.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1784 1.3131]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001285.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001286.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001287.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1448]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001288.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1926]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001289.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1926]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001296.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3447 1.0975]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001298.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0637]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001299.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0637]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001300.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0637]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001304.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.098 1.3046 1.4014]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001305.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001306.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001307.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001308.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001309.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001310.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001311.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001312.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001313.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1906 1.3987]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001314.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1906 1.3987]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001315.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1906 1.3987]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001316.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2455 1.4706 1.3972 1.3619 1.1353 1.3134 1.3298]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001317.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2455 1.4706 1.3972 1.3619 1.1353 1.3134 1.3298]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001318.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2455 1.4706 1.3972 1.3619 1.1353 1.3134 1.3298]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001319.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0571]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001320.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0571]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001323.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3003 1.0663 1.1168 1.3624 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001324.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3003 1.0663 1.1168 1.3624 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001325.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3003 1.0663 1.1168 1.3624 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001326.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3003 1.0663 1.1168 1.3624 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001327.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3003 1.0663 1.1168 1.3624 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001328.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3515 1.0166 1.3976 1.259]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001329.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.236 1.1255]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001330.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.236 1.1255]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001331.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.236 1.1255]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001332.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.236 1.1255]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001333.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.236 1.1255]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001334.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.123 1.1801 1.1669]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001335.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.123 1.1801 1.1669]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001336.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.341 1.3945 1.459 1.0195]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001337.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.341 1.3945 1.459 1.0195]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001338.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.341 1.3945 1.459 1.0195]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001339.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.341 1.3945 1.459 1.0195]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001340.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001341.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001342.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001343.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001344.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001345.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001346.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001347.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2041]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001349.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1011]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001353.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1143 1.1581]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001354.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1143 1.1581]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001355.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1143 1.1581]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001356.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0817 1.2004 1.2661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001357.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0817 1.2004 1.2661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001358.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0817 1.2004 1.2661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001359.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0817 1.2004 1.2661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001364.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1298 1.2678 1.1358 1.0982 1.0199]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001365.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1298 1.2678 1.1358 1.0982 1.0199]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001366.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1298 1.2678 1.1358 1.0982 1.0199]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001367.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1298 1.2678 1.1358 1.0982 1.0199]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001368.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1298 1.2678 1.1358 1.0982 1.0199]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001369.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1298 1.2678 1.1358 1.0982 1.0199]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001370.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001371.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001372.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001373.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001374.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001375.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001376.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001377.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2061]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001378.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2061]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001379.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2061]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001380.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0133]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001381.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0133]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001382.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2565 1.0135 1.3584 1.3441 1.2876 1.2935 1.2937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001383.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2565 1.0135 1.3584 1.3441 1.2876 1.2935 1.2937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001384.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2565 1.0135 1.3584 1.3441 1.2876 1.2935 1.2937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001387.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001388.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001389.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001390.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001391.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001392.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0892]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001393.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0892]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001394.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0892]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001395.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0892]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001397.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001398.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001399.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001400.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001401.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001402.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001403.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001404.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001405.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001406.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001407.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001408.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001409.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001411.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0516 1.3319 1.1111 1.0484 1.0286 1.1049]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001417.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001418.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001419.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001420.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001421.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001422.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001423.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001424.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001425.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001426.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001427.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1904]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001428.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1904]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001431.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3948]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001432.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3948]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001442.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.303 1.1787 1.2042 1.1864 1.2294 1.2534 1.4164 1.1914 1.2802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001443.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1684]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001444.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1684]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001446.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001447.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001448.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001449.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001450.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001451.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001452.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001453.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001454.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001455.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001456.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001458.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4748 1.1871 1.1566]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001459.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4748 1.1871 1.1566]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001460.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4748 1.1871 1.1566]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001461.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001462.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001463.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0146]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001464.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0416]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001465.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0416]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001466.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2379]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001467.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001468.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001469.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001470.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001471.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001472.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001473.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0294]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001474.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.263]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001475.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001476.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001477.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001478.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001479.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001480.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001481.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001482.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001483.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001484.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001485.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001486.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001487.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001488.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001495.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1706 1.0647 1.0452]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001496.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1706 1.0647 1.0452]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001497.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2211 1.0764 1.1816]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001498.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2617]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001499.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001500.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001501.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001502.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001503.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001504.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001509.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001510.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001511.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001512.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001513.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001514.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001515.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001516.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001517.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001518.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001519.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001520.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001521.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001522.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001523.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001524.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001525.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001526.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.201 1.4511]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001527.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.201 1.4511]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001528.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.201 1.4511]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001529.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.201 1.4511]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001530.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001531.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001532.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001533.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001534.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001535.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001536.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001537.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001538.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001539.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001540.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001541.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001542.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001543.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001544.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001545.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001546.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001547.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001548.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001549.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001550.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001551.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001552.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001553.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001554.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001555.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001556.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001557.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4178 1.0167]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001558.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4178 1.0167]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001559.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4178 1.0167]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001560.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2323 1.2429 1.1507]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001561.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2323 1.2429 1.1507]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001562.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3782]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001564.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0719]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001565.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0719]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001566.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0146]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001567.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0146]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001568.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001569.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001570.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001571.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001572.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001573.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001574.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001575.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001576.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001577.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001578.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001579.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001580.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1324 1.2246 1.1323 1.0348 1.1518 1.4062 1.3396]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001581.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001582.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001583.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001584.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001585.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0294]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001586.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1731 1.1905]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001587.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1731 1.1905]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001588.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4255 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001589.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4255 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001590.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4255 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001591.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3529]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001592.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001593.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001594.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001595.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0325 1.3337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001597.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.047 1.1259]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001599.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0119 1.0116 1.0144]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001600.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2789 1.0191 1.1662]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001601.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2135]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001602.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2135]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001606.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001607.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001608.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001609.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2194]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001610.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2194]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001611.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2194]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001612.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001613.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001614.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0936 1.0384]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001618.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001619.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001620.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001621.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001622.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001623.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001624.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001625.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001626.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001627.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001628.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001629.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001630.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001631.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001632.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001633.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001634.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001635.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001636.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001637.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001638.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4178 1.0167]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001639.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4748 1.1871 1.1566]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001642.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001643.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0137 1.3517 1.4374 1.424]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001644.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001646.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0149]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001647.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2205]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001648.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4255 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001649.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001651.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001654.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0119 1.0116 1.0144]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001655.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0119 1.0116 1.0144]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001656.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0119 1.0116 1.0144]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001657.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2789 1.0191 1.1662]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001662.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0906]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001663.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2637]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001664.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0936 1.0384]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001665.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0936 1.0384]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001668.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0652 1.3586 1.242 1.1107 1.1944]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001669.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0652 1.3586 1.242 1.1107 1.1944]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001670.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0652 1.3586 1.242 1.1107 1.1944]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001671.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1295 1.2927 1.409 1.1628 1.1432 1.2169]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001672.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1295 1.2927 1.409 1.1628 1.1432 1.2169]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001673.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0338 1.1838 1.2401 1.1805 1.4813 1.1295 1.2744 1.2119]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001675.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1776 1.3347 1.0123 1.0676]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001676.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1776 1.3347 1.0123 1.0676]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001678.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0415]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001679.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0282 1.0832]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001680.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0282 1.0832]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001681.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2175 1.0905 1.1523 1.3564 1.0653 1.3505 1.3475 1.0368 1.261]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001682.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2088 1.1474 1.3309 1.0401 1.1921]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001683.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2095 1.0306]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001686.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1964]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001687.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1964]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001690.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3841 1.1723 1.287 1.4304 1.3563 1.469 1.0281 1.0876 1.2985 1.2839 1.1523 1.232 1.2772 1.1218 1.4149]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001691.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3841 1.1723 1.287 1.4304 1.3563 1.469 1.0281 1.0876 1.2985 1.2839 1.1523 1.232 1.2772 1.1218 1.4149]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001692.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3841 1.1723 1.287 1.4304 1.3563 1.469 1.0281 1.0876 1.2985 1.2839 1.1523 1.232 1.2772 1.1218 1.4149]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001693.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.34 1.074]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001694.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.191 1.3208 1.2147 1.1359]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001695.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0659]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001696.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0659]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001697.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2783 1.3093 1.2017 1.1884]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001699.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0939]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001700.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001702.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2323 1.2429 1.1507]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001703.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3782]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001709.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2095 1.0306]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001710.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2095 1.0306]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001711.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2095 1.0306]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001712.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2095 1.0306]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001713.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2095 1.0306]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001714.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001715.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001721.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2067]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001723.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2521 1.0888 1.2359 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001729.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001730.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1254 1.198 1.1768 1.2427 1.0678 1.1613 1.1801]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001731.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3457]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001732.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.335]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001733.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001735.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2816 1.0743 1.2387 1.131 1.239 1.0616]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001736.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2816 1.0743 1.2387 1.131 1.239 1.0616]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001737.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1487 1.3627 1.1307 1.3669 1.2338 1.1603 1.3146 1.0324 1.087 1.3173 1.1007 1.07 1.3735 1.4539]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001738.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2523]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001739.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2523]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001740.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2523]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001741.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4172]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001742.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4172]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001743.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4172]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001744.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0697 1.1177 1.0422 1.0649 1.1312 1.0904 1.0346 1.2556 1.3053 1.0807 1.0768]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001745.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0697 1.1177 1.0422 1.0649 1.1312 1.0904 1.0346 1.2556 1.3053 1.0807 1.0768]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001746.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0697 1.1177 1.0422 1.0649 1.1312 1.0904 1.0346 1.2556 1.3053 1.0807 1.0768]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001748.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1111 1.0718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001749.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1111 1.0718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001750.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1111 1.0718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001752.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3562 1.1125 1.1873 1.3461 1.4313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001753.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3562 1.1125 1.1873 1.3461 1.4313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001754.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0355]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001757.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3943 1.386]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001758.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001759.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001760.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001761.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001762.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001763.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001764.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001765.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001766.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001767.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001768.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.261 1.042 1.2357 1.2181 1.3573 1.1194 1.0664 1.1048 1.2896 1.4323 1.2687]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001773.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.0695]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001774.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.0695]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001775.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3226]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001779.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1686 1.0799]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001780.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3066 1.1678 1.1537 1.0116 1.2324 1.3885]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001781.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3066 1.1678 1.1537 1.0116 1.2324 1.3885]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001782.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.0919 1.2404 1.3115 1.4192 1.4631 1.3047 1.3574 1.1441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001783.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.0919 1.2404 1.3115 1.4192 1.4631 1.3047 1.3574 1.1441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001784.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.0919 1.2404 1.3115 1.4192 1.4631 1.3047 1.3574 1.1441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001786.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001787.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001788.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.087 1.147]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001789.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0137 1.3517 1.4374 1.424]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001790.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0137 1.3517 1.4374 1.424]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001791.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0137 1.3517 1.4374 1.424]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001792.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001794.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001796.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0325 1.3337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001797.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.169 1.4086 1.2435 1.0387 1.4234 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001798.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3001 1.3775]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001799.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4376 1.1876 1.3234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001800.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4376 1.1876 1.3234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001801.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4376 1.1876 1.3234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001802.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3156 1.1853]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001803.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3156 1.1853]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001805.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0133]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001806.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0133]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001807.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001808.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001809.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001810.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001811.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001812.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001813.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001814.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001815.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001816.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001817.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001818.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001819.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001820.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001821.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2146]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001822.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3023]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001823.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001824.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001825.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001826.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001827.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001828.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001829.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001830.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001831.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001832.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001833.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001834.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001835.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001836.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001837.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0659]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001838.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0913]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001839.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2092 1.2012 1.2004 1.0987 1.2662 1.1445 1.2515 1.0877 1.2308 1.0146 1.0347]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001840.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001841.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001842.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001843.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001844.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001845.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001846.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001847.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001848.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001849.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001850.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001851.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1191 1.3373 1.4758 1.3819 1.3537]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001852.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001853.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001854.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001855.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001856.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001857.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001858.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001859.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001860.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001861.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001862.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001863.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001864.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001865.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001868.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.34 1.3341 1.1962 1.1168 1.2329 1.0755 1.1467 1.0857 1.0195 1.1167 1.413]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001869.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.34 1.3341 1.1962 1.1168 1.2329 1.0755 1.1467 1.0857 1.0195 1.1167 1.413]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001870.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001871.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001872.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001873.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001874.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001875.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001876.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001879.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001880.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001881.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001882.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001883.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001884.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001885.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001886.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001887.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001888.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2323 1.2429 1.1507]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001889.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1401 1.1537]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001890.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1401 1.1537]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001891.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001892.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001893.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001894.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001895.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001896.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001897.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001898.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001899.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001900.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001901.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001902.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001903.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001904.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001905.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2234 1.2273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001906.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001907.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1324 1.2246 1.1323 1.0348 1.1518 1.4062 1.3396]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001908.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1324 1.2246 1.1323 1.0348 1.1518 1.4062 1.3396]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001909.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1324 1.2246 1.1323 1.0348 1.1518 1.4062 1.3396]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001910.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0984 1.0379 1.2711]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001911.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3144 1.0185 1.0115 1.0481 1.4546 1.0459 1.4496 1.293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001912.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3144 1.0185 1.0115 1.0481 1.4546 1.0459 1.4496 1.293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001913.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3144 1.0185 1.0115 1.0481 1.4546 1.0459 1.4496 1.293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001914.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3144 1.0185 1.0115 1.0481 1.4546 1.0459 1.4496 1.293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001915.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001916.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001917.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001918.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001919.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.379 1.1509 1.2056 1.0189]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001920.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.379 1.1509 1.2056 1.0189]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001921.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.379 1.1509 1.2056 1.0189]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001922.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.379 1.1509 1.2056 1.0189]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001923.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.379 1.1509 1.2056 1.0189]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001924.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0242]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001925.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0242]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001926.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001927.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001928.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0177 1.3068]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001929.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0177 1.3068]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001930.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0177 1.3068]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001931.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0177 1.3068]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001932.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0177 1.3068]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001933.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001934.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001935.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001936.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001937.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001938.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001939.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001940.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001941.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001942.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001943.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001944.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001945.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001946.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001947.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001948.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001949.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001950.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001951.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0358 1.3669 1.2754 1.0494 1.2859]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001952.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1778 1.1851 1.1566 1.1064]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001953.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0325 1.3337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001955.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001956.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001957.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1542]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001958.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2635]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001959.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2673 1.3585 1.2351 1.3888]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001960.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3253]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001961.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001962.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001963.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3144 1.0185 1.0115 1.0481 1.4546 1.0459 1.4496 1.293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001964.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3144 1.0185 1.0115 1.0481 1.4546 1.0459 1.4496 1.293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001966.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001967.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001968.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001969.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001973.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2559 1.0411 1.2715 1.3184]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001974.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2559 1.0411 1.2715 1.3184]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001975.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.201 1.4511]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001976.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4361 1.3512 1.424]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001977.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0652 1.3586 1.242 1.1107 1.1944]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001978.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0652 1.3586 1.242 1.1107 1.1944]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001979.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1306 1.1646 1.1511 1.057 1.1493]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001980.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1306 1.1646 1.1511 1.057 1.1493]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001983.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1776 1.3347 1.0123 1.0676]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001984.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0415]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001985.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0949 1.206]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001986.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.137 1.1783]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001987.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3336 1.1284 1.227 1.1123 1.1955 1.3635 1.0413]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001988.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3336 1.1284 1.227 1.1123 1.1955 1.3635 1.0413]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001989.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0723 1.2011 1.3391 1.1489 1.1474 1.0335]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001990.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1256 1.0654]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001991.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1256 1.0654]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001992.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3841 1.1723 1.287 1.4304 1.3563 1.469 1.0281 1.0876 1.2985 1.2839 1.1523 1.232 1.2772 1.1218 1.4149]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001993.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4017 1.2871 1.0449 1.2869 1.3957 1.246 1.3421 1.0901 1.049 1.146 1.3777 1.3273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001995.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.029 1.102 1.3175]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001996.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0417 1.3097 1.1494]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001997.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.348 1.1043 1.0665 1.4448 1.4122]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001998.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.286]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002001.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2694]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002002.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0286 1.186]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002003.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3012 1.0477]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002004.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.414 1.2287 1.3823 1.3763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002006.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1527 1.2312 1.2637 1.2645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002007.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1527 1.2312 1.2637 1.2645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002008.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2798 1.321 1.3091 1.0725 1.1324 1.1356 1.3548 1.1002 1.0155 1.0806]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002009.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1933 1.0171 1.1628 1.2245 1.0234 1.0389 1.0834]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002010.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1933 1.0171 1.1628 1.2245 1.0234 1.0389 1.0834]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002011.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1105 1.0516]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002014.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0207]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002015.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3714]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002019.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002020.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002024.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2691]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002025.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2859]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002026.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3948 1.0915]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002027.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0542 1.3148 1.2589 1.4424]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002028.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0716 1.0402 1.2713 1.1127 1.4734 1.0883 1.2271]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002029.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1485 1.0747 1.0474 1.3841 1.0347 1.2545 1.4052]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002030.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2492 1.1554 1.0553]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002031.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2492 1.1554 1.0553]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002033.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3047 1.1845]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002034.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0551 1.2978]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002035.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1208 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002036.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0986]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002037.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2468 1.0382 1.0773]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002038.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.23 1.1966]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002039.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0617 1.3322 1.1368 1.1124 1.1722]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002041.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002042.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002043.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002044.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002045.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002046.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1057 1.1904]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002047.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1057 1.1904]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002048.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1057 1.1904]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002049.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2673 1.3585 1.2351 1.3888]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002050.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2673 1.3585 1.2351 1.3888]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002051.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2673 1.3585 1.2351 1.3888]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002053.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3253]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002054.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002055.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002056.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002057.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002058.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002059.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002060.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002061.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002062.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002063.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002064.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002065.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002066.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002067.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2789 1.0191 1.1662]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002070.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.169 1.4086 1.2435 1.0387 1.4234 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002071.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2559 1.0411 1.2715 1.3184]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002072.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2559 1.0411 1.2715 1.3184]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002073.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0338 1.1838 1.2401 1.1805 1.4813 1.1295 1.2744 1.2119]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002074.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1776 1.3347 1.0123 1.0676]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002075.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1604 1.2529 1.2553]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002076.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1604 1.2529 1.2553]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002077.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1583 1.1798 1.373]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002078.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1583 1.1798 1.373]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002079.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1583 1.1798 1.373]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002080.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1583 1.1798 1.373]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002081.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1583 1.1798 1.373]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002082.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2088 1.1474 1.3309 1.0401 1.1921]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002084.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.137 1.1783]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002086.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0607 1.0462 1.3976 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002087.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3453]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002088.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1545 1.1026 1.3005]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002089.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1545 1.1026 1.3005]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002090.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.286]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002093.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.025 1.2632 1.0793]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002094.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.025 1.2632 1.0793]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002095.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.025 1.2632 1.0793]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002096.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.025 1.2632 1.0793]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002097.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.025 1.2632 1.0793]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002098.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1934]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002099.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.109 1.0539 1.3559 1.4501]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002100.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.109 1.0539 1.3559 1.4501]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002101.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1429]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002102.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002103.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2144 1.4619]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002104.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1527 1.2312 1.2637 1.2645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002107.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2798 1.321 1.3091 1.0725 1.1324 1.1356 1.3548 1.1002 1.0155 1.0806]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002108.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2798 1.321 1.3091 1.0725 1.1324 1.1356 1.3548 1.1002 1.0155 1.0806]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002109.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1933 1.0171 1.1628 1.2245 1.0234 1.0389 1.0834]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002110.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1105 1.0516]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002111.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0941 1.0995 1.3236 1.1913 1.1285]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002112.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002113.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002114.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1544]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002115.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002116.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002117.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002118.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002119.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002120.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002128.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0146]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002131.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2699 1.0158]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002134.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0167]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002135.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1463]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002137.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4017 1.2871 1.0449 1.2869 1.3957 1.246 1.3421 1.0901 1.049 1.146 1.3777 1.3273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002138.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.029 1.102 1.3175]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002139.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0412]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002141.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2047]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002144.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.065]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002147.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0185]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002150.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1429]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002151.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.341 1.0231 1.2322 1.3017]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002152.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0753]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002153.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.272]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002155.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0292]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002156.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0785]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002157.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0275 1.2791]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002158.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0275 1.2791]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002162.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1359]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002165.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0173 1.1067 1.2355]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002166.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0792]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002168.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0492 1.0264 1.0191 1.0769 1.4468 1.1342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002170.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2947]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002171.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3715]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002177.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002180.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1327 1.1733]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002181.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3798]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002182.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0642]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002183.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0986]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002185.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1302 1.1838 1.1564 1.0757 1.0377]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002188.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1102 1.0269]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002189.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1102 1.0269]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002192.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0118 1.0491]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002194.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1929 1.2485]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002197.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4629]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002203.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3197]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002204.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1056 1.12 1.2937 1.1116 1.0538]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002205.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.302]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002209.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0222]\n", "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n", "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.000333, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n", "Plotting labels to /content/runs/detect/smartvision_yolov83/labels.jpg... \n", "Image sizes 640 train, 640 val\n", "Using 0 dataloader workers\n", "Logging results to \u001b[1m/content/runs/detect/smartvision_yolov83\u001b[0m\n", "Starting training for 10 epochs...\n", "Closing dataloader mosaic\n", "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 1/10 0G 2.083 4.561 2.616 53 640: 100% ━━━━━━━━━━━━ 33/33 13.9s/it 7:37\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 17/17 6.9s/it 1:57\n", " all 523 1930 0 0 0 0\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 2/10 0G 1.689 4.235 2.131 23 640: 100% ━━━━━━━━━━━━ 33/33 13.5s/it 7:26\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 17/17 7.1s/it 2:01\n", " all 523 1930 0.0786 0.274 0.146 0.0775\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 3/10 0G 1.563 3.833 2.003 47 640: 100% ━━━━━━━━━━━━ 33/33 13.5s/it 7:26\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 17/17 7.1s/it 2:01\n", " all 523 1930 0.615 0.142 0.19 0.0881\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 4/10 0G 1.544 3.457 2.03 22 640: 100% ━━━━━━━━━━━━ 33/33 13.4s/it 7:24\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 17/17 7.2s/it 2:03\n", " all 523 1930 0.661 0.248 0.257 0.106\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 5/10 0G 1.472 3.188 1.95 36 640: 100% ━━━━━━━━━━━━ 33/33 13.5s/it 7:26\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 17/17 7.3s/it 2:04\n", " all 523 1930 0.628 0.277 0.309 0.129\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 6/10 0G 1.443 2.944 1.916 36 640: 100% ━━━━━━━━━━━━ 33/33 13.5s/it 7:25\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 17/17 7.2s/it 2:03\n", " all 523 1930 0.664 0.369 0.413 0.177\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 7/10 0G 1.398 2.758 1.865 41 640: 100% ━━━━━━━━━━━━ 33/33 13.6s/it 7:28\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 17/17 7.3s/it 2:04\n", " all 523 1930 0.668 0.429 0.481 0.211\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 8/10 0G 1.347 2.619 1.803 33 640: 100% ━━━━━━━━━━━━ 33/33 13.7s/it 7:32\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 17/17 7.4s/it 2:06\n", " all 523 1930 0.715 0.44 0.513 0.242\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 9/10 0G 1.287 2.496 1.766 27 640: 100% ━━━━━━━━━━━━ 33/33 13.5s/it 7:26\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 17/17 7.1s/it 2:02\n", " all 523 1930 0.716 0.46 0.551 0.26\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 10/10 0G 1.264 2.452 1.747 30 640: 100% ━━━━━━━━━━━━ 33/33 13.4s/it 7:22\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 17/17 7.3s/it 2:04\n", " all 523 1930 0.699 0.505 0.558 0.272\n", "\n", "10 epochs completed in 1.583 hours.\n", "Optimizer stripped from /content/runs/detect/smartvision_yolov83/weights/last.pt, 6.2MB\n", "Optimizer stripped from /content/runs/detect/smartvision_yolov83/weights/best.pt, 6.2MB\n", "\n", "Validating /content/runs/detect/smartvision_yolov83/weights/best.pt...\n", "Ultralytics 8.4.14 πŸš€ Python-3.12.12 torch-2.10.0+cpu CPU (Intel Xeon CPU @ 2.20GHz)\n", "Model summary (fused): 73 layers, 3,010,718 parameters, 0 gradients, 8.1 GFLOPs\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 17/17 6.2s/it 1:45\n", " all 523 1930 0.699 0.505 0.558 0.271\n", " person 153 446 0.552 0.509 0.513 0.205\n", " bicycle 23 136 0.626 0.684 0.679 0.389\n", " car 55 139 0.523 0.439 0.402 0.232\n", " motorcycle 21 28 0.451 0.607 0.627 0.275\n", " airplane 32 88 0.561 0.705 0.655 0.378\n", " bus 28 42 0.555 0.548 0.531 0.359\n", " train 32 42 0.581 0.726 0.712 0.257\n", " truck 27 36 1 0.356 0.583 0.299\n", " traffic light 15 39 1 0 0.481 0.304\n", " stop sign 36 38 0.429 0.632 0.507 0.219\n", " bench 28 47 0.517 0.456 0.511 0.163\n", " bird 26 112 0.539 0.393 0.385 0.281\n", " cat 60 73 0.727 0.712 0.744 0.357\n", " dog 48 152 0.66 0.612 0.689 0.327\n", " horse 20 48 0.729 0.688 0.676 0.291\n", " cow 22 52 0.762 0.558 0.631 0.21\n", " elephant 28 43 0.536 0.86 0.819 0.298\n", " bottle 25 44 0.973 0.0909 0.234 0.168\n", " cup 23 28 1 0 0.197 0.169\n", " bowl 34 49 0.845 0.735 0.793 0.209\n", " pizza 31 39 0.914 0.718 0.81 0.454\n", " cake 10 10 0.939 0.3 0.427 0.25\n", " chair 27 40 1 0 0.0202 0.0102\n", " couch 31 31 0.637 0.548 0.649 0.349\n", " potted plant 18 65 0.475 0.646 0.567 0.259\n", " bed 55 63 0.639 0.618 0.658 0.333\n", "Speed: 3.3ms preprocess, 185.7ms inference, 0.0ms loss, 2.7ms postprocess per image\n", "Results saved to \u001b[1m/content/runs/detect/smartvision_yolov83\u001b[0m\n" ] } ], "source": [ "from ultralytics import YOLO\n", "\n", "# Load pretrained YOLOv8 nano\n", "model = YOLO(\"yolov8n.pt\")\n", "\n", "# Train\n", "results = model.train(\n", " data=\"smartvision_dataset/detection/data.yaml\",\n", " epochs=10,\n", " imgsz=640,\n", " batch=16,\n", " name=\"smartvision_yolov8\"\n", ")" ] }, { "cell_type": "markdown", "id": "YAx2jSdM80ej", "metadata": { "id": "YAx2jSdM80ej" }, "source": [ "Evaluate Model" ] }, { "cell_type": "code", "execution_count": 2, "id": "MpVWFbEn9AI7", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MpVWFbEn9AI7", "outputId": "c42ce667-de44-4114-983a-f5058c794900" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ultralytics 8.4.14 πŸš€ Python-3.12.12 torch-2.10.0+cpu CPU (Intel Xeon CPU @ 2.20GHz)\n", "Model summary (fused): 73 layers, 3,010,718 parameters, 0 gradients, 8.1 GFLOPs\n", "\u001b[34m\u001b[1mval: \u001b[0mFast image access βœ… (ping: 0.1Β±0.0 ms, read: 220.6Β±417.1 MB/s, size: 178.8 KB)\n", "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /content/smartvision_dataset/detection/labels.cache... 2210 images, 0 backgrounds, 1687 corrupt: 100% ━━━━━━━━━━━━ 2210/2210 386.2Mit/s 0.0s\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000001.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000002.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000003.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000004.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000005.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000006.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000010.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000011.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000012.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000013.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000014.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000015.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000016.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0568]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000017.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0568]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000018.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0568]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000019.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0568]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000020.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0568]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000021.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0377]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000022.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000023.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000024.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000025.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000026.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000027.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000028.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000029.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000030.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000031.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000032.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000033.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000034.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000035.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000036.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000037.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000038.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000039.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000040.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000043.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000044.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000045.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000046.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000047.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000048.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000049.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000050.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000051.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000052.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000053.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000054.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000055.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000056.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.39]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000059.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0213 1.086 1.071]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000060.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0213 1.086 1.071]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000061.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0213 1.086 1.071]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000062.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0213 1.086 1.071]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000063.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4517 1.3824]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000064.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4517 1.3824]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000065.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000066.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000067.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000068.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000069.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000070.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000071.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000072.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000073.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000074.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000075.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000076.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000077.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000078.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000079.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000080.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000081.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000082.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000083.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000085.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000086.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0573 1.1913 1.4816]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000087.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0573 1.1913 1.4816]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000088.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0573 1.1913 1.4816]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000089.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3385 1.3641 1.3384 1.4065 1.3079 1.0907 1.4273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000090.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3385 1.3641 1.3384 1.4065 1.3079 1.0907 1.4273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000091.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3488]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000092.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3492 1.1307]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000093.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3492 1.1307]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000094.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000095.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000096.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000097.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000098.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000099.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000100.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000101.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000102.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000103.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000104.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000106.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2477 1.0966 1.0254 1.1976 1.0638 1.4171 1.1151 1.0168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000107.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2477 1.0966 1.0254 1.1976 1.0638 1.4171 1.1151 1.0168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000108.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000109.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000110.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575 1.1136 1.3602 1.1078 1.3157 1.1806]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000111.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575 1.1136 1.3602 1.1078 1.3157 1.1806]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000113.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000114.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000115.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000116.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000117.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000118.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000119.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000120.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000121.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.2781 1.2829 1.1132 1.1248 1.257]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000122.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0167 1.3468 1.0565 1.3635 1.1786]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000125.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2942 1.3317 1.1404 1.0905 1.4771 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000126.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000127.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000128.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0389]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000129.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0389]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000131.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0217 1.0899]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000132.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0217 1.0899]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000133.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0857 1.2957 1.4019 1.4657 1.3516 1.2175]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000134.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0814 1.1501 1.3078 1.132]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000135.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0814 1.1501 1.3078 1.132]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000136.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0814 1.1501 1.3078 1.132]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000137.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2408 1.0816 1.0947 1.1683 1.3866 1.2441 1.3405 1.2633]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000149.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3813 1.1073 1.1568 1.1564 1.0852 1.1014 1.0644 1.087 1.0961 1.0906 1.0491 1.0929 1.1042 1.3517 1.2582 1.3639 1.0882 1.1112 1.0979]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000150.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3813 1.1073 1.1568 1.1564 1.0852 1.1014 1.0644 1.087 1.0961 1.0906 1.0491 1.0929 1.1042 1.3517 1.2582 1.3639 1.0882 1.1112 1.0979]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000151.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3813 1.1073 1.1568 1.1564 1.0852 1.1014 1.0644 1.087 1.0961 1.0906 1.0491 1.0929 1.1042 1.3517 1.2582 1.3639 1.0882 1.1112 1.0979]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000152.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3813 1.1073 1.1568 1.1564 1.0852 1.1014 1.0644 1.087 1.0961 1.0906 1.0491 1.0929 1.1042 1.3517 1.2582 1.3639 1.0882 1.1112 1.0979]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000153.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3447 1.0975]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000154.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000155.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000156.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000157.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000158.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000159.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000160.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000161.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000162.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000163.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000164.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1246 1.1661 1.0299 1.2214]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000165.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.46 1.4187]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000166.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000167.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000168.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000169.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1948 1.1834]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000170.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.032]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000171.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000172.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000173.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000174.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000175.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000176.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000177.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000178.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000179.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000180.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000181.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000182.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000183.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000184.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0118 1.3348 1.0334]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000185.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000186.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000187.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000188.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1534 1.1256 1.1228 1.1566 1.317 1.1345 1.266 1.1246 1.0987 1.116 1.1141 1.1348 1.1596 1.2801 1.1323 1.378 1.1375 1.1353 1.1334 1.1303 1.1262]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000189.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000190.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000191.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000192.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000193.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000194.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000196.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0864]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000198.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3322 1.3673]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000199.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.0291 1.2534]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000200.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.0291 1.2534]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000201.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.0291 1.2534]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000202.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000203.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000204.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000205.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000206.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000207.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000208.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000209.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000210.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000211.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000212.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000213.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000214.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000215.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000216.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.148]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000217.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2216]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000218.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2216]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000219.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0472 1.0888 1.1318 1.3638 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000220.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0472 1.0888 1.1318 1.3638 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000221.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0472 1.0888 1.1318 1.3638 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000222.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0472 1.0888 1.1318 1.3638 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000223.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0472 1.0888 1.1318 1.3638 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000224.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0472 1.0888 1.1318 1.3638 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000225.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.0657 1.0768]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000226.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.0657 1.0768]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000231.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3488]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000232.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.379 1.1509 1.2056 1.0189]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000233.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.034 1.0878]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000234.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.034 1.0878]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000235.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.034 1.0878]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000236.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.034 1.0878]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000237.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.034 1.0878]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000239.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000240.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000241.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000242.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000243.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000244.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000245.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000246.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000247.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000248.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000249.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000250.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3583]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000251.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3583]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000252.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3583]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000253.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000254.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0177 1.3068]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000257.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0377]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000259.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0872]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000260.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2216]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000261.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0819 1.0649]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000262.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0984 1.0379 1.2711]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000263.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3488]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000264.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2424 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000265.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000266.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2477 1.0966 1.0254 1.1976 1.0638 1.4171 1.1151 1.0168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000268.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0251 1.3488 1.0667]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000270.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000271.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000272.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000273.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000274.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000275.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000276.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000277.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000278.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000279.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000280.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000281.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000282.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000283.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000284.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.1607 1.1498 1.2068 1.1738 1.1713 1.177 1.4336 1.179 1.1247 1.1285 1.1228 1.1898 1.0465 1.0555 1.1407 1.162 1.1592 1.1576 1.3573 1.1937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000285.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2962 1.1061 1.2492]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000286.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2962 1.1061 1.2492]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000287.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2962 1.1061 1.2492]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000288.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2962 1.1061 1.2492]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000289.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2962 1.1061 1.2492]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000290.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0607 1.0462 1.3976 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000292.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0711 1.0652 1.0427 1.0439]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000293.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0711 1.0652 1.0427 1.0439]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000294.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0507 1.1707 1.1062 1.0498 1.4468 1.2013 1.0681]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000299.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2408 1.0816 1.0947 1.1683 1.3866 1.2441 1.3405 1.2633]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000300.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2408 1.0816 1.0947 1.1683 1.3866 1.2441 1.3405 1.2633]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000301.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3447 1.0975]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000302.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0724 1.3465]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000303.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4128]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000304.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3465 1.4145 1.0238 1.3548]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000305.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3465 1.4145 1.0238 1.3548]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000306.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0648 1.3159]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000307.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0584]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000308.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1916]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000309.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000310.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000311.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000312.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000313.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3171 1.1059 1.2]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000314.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000316.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0195 1.0134 1.2051 1.0651 1.3172 1.0999 1.3003 1.238 1.432 1.4685 1.1767 1.2398 1.4213 1.2114 1.2702 1.0145 1.0843]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000318.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2184]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000320.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.098 1.3046 1.4014]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000321.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.098 1.3046 1.4014]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000322.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.098 1.3046 1.4014]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000324.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2433]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000325.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2128 1.3769 1.337 1.2589 1.2109 1.1046 1.1607 1.3543 1.4789 1.4321 1.4126 1.298 1.3879]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000328.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3795 1.3697]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000329.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1473 1.1461]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000330.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1473 1.1461]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000331.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1473 1.1461]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000332.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1473 1.1461]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000333.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0331 1.082 1.2072 1.178]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000335.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2599 1.1365]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000336.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.3181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000337.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.3181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000338.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.3181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000339.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.3181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000341.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000342.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2328 1.1457 1.2815 1.2566 1.1566 1.1928]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000344.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000345.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0919 1.0994 1.0493]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000350.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2861 1.0694 1.2839 1.3754]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000351.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2861 1.0694 1.2839 1.3754]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000352.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2861 1.0694 1.2839 1.3754]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000354.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2424 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000355.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000356.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000357.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000358.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000359.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0432 1.261]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000360.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0728]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000361.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1293 1.0559 1.0301]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000362.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1293 1.0559 1.0301]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000364.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1415]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000365.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0879 1.0447 1.0649]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000367.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1433 1.0584]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000368.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1658 1.0536 1.1723 1.0751]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000369.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0988]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000370.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0988]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000371.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0988]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000372.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0988]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000373.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1191 1.3373 1.4758 1.3819 1.3537]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000374.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.067 1.0303 1.042 1.0528 1.0443 1.0271 1.0577 1.0358 1.0675 1.0429 1.047 1.0727 1.0297]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000382.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1023 1.0417 1.2845 1.0395]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000388.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1308 1.4184]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000390.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1625 1.2811 1.0798 1.2282 1.3015]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000391.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.266]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000392.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.266]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000393.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.266]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000394.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.266]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000395.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.266]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000396.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1867 1.1554 1.1629 1.1675]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000397.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000398.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000399.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000400.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000401.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.078 1.2619]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000402.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.078 1.2619]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000403.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.078 1.2619]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000406.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.361 1.2929 1.0871 1.0424 1.0563 1.1481 1.2253 1.0351 1.2423 1.4025 1.2569 1.4244]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000407.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.361 1.2929 1.0871 1.0424 1.0563 1.1481 1.2253 1.0351 1.2423 1.4025 1.2569 1.4244]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000408.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.361 1.2929 1.0871 1.0424 1.0563 1.1481 1.2253 1.0351 1.2423 1.4025 1.2569 1.4244]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000409.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.361 1.2929 1.0871 1.0424 1.0563 1.1481 1.2253 1.0351 1.2423 1.4025 1.2569 1.4244]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000410.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.361 1.2929 1.0871 1.0424 1.0563 1.1481 1.2253 1.0351 1.2423 1.4025 1.2569 1.4244]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000413.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.025 1.0101 1.1705]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000414.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3054 1.2719 1.3924 1.2629 1.3638 1.1235 1.2335 1.0921 1.1293 1.0976 1.0477 1.1907 1.2481 1.2469 1.4638 1.0224 1.0726]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000418.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2735 1.3458 1.1181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000419.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2735 1.3458 1.1181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000420.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2735 1.3458 1.1181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000421.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2735 1.3458 1.1181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000422.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2735 1.3458 1.1181]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000429.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000430.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000431.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3385 1.3641 1.3384 1.4065 1.3079 1.0907 1.4273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000433.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.0657 1.0768]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000434.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0242]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000436.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000437.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.05 1.3044 1.4222 1.4496 1.4635 1.4757 1.1427 1.3243 1.4071 1.079 1.3459 1.3661 1.3827 1.3939 1.439 1.4925 1.0395 1.3579 1.3657 1.2323 1.3455 1.2116 1.038 1.3302]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000438.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.05 1.3044 1.4222 1.4496 1.4635 1.4757 1.1427 1.3243 1.4071 1.079 1.3459 1.3661 1.3827 1.3939 1.439 1.4925 1.0395 1.3579 1.3657 1.2323 1.3455 1.2116 1.038 1.3302]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000439.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.05 1.3044 1.4222 1.4496 1.4635 1.4757 1.1427 1.3243 1.4071 1.079 1.3459 1.3661 1.3827 1.3939 1.439 1.4925 1.0395 1.3579 1.3657 1.2323 1.3455 1.2116 1.038 1.3302]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000440.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4233 1.0268 1.4472 1.4143]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000442.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0102 1.3403 1.041 1.0854 1.4127 1.4578 1.0778 1.2834]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000443.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3599]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000444.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3599]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000445.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4707 1.0701]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000446.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2014 1.026 1.3853 1.2427 1.1528 1.1792 1.0983 1.2052]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000447.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0469 1.0219 1.2866 1.4379 1.3544 1.4822 1.0766 1.2366 1.2719 1.1818]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000448.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0469 1.0219 1.2866 1.4379 1.3544 1.4822 1.0766 1.2366 1.2719 1.1818]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000449.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0469 1.0219 1.2866 1.4379 1.3544 1.4822 1.0766 1.2366 1.2719 1.1818]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000450.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0469 1.0219 1.2866 1.4379 1.3544 1.4822 1.0766 1.2366 1.2719 1.1818]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000451.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0469 1.0219 1.2866 1.4379 1.3544 1.4822 1.0766 1.2366 1.2719 1.1818]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000452.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276 1.0754 1.0253]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000453.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2059 1.1574]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000454.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0952 1.219 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000455.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4092]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000456.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1614 1.261 1.0286]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000458.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4164 1.268 1.1395 1.0109]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000459.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0389]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000460.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2171]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000461.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2171]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000462.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2171]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000463.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2171]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000464.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2171]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000466.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3606 1.0951]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000467.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0814 1.1501 1.3078 1.132]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000468.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3813 1.1073 1.1568 1.1564 1.0852 1.1014 1.0644 1.087 1.0961 1.0906 1.0491 1.0929 1.1042 1.3517 1.2582 1.3639 1.0882 1.1112 1.0979]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000469.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3023]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000470.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3023]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000471.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3023]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000472.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0732 1.0623]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000473.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3306 1.4458 1.4114 1.2858 1.1532 1.4603 1.4826]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000474.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2569 1.0143 1.1132 1.404 1.1943 1.0717 1.2386]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000475.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3266 1.0494 1.3057 1.2081]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000478.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000479.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000480.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000481.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000482.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000483.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000484.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000485.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000486.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000487.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000488.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000489.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000490.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1388 1.3698 1.259 1.126 1.066 1.0799 1.1124 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000491.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.136 1.2483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000492.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000493.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000494.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0112 1.0766 1.0268 1.0716 1.0643 1.1068 1.0362 1.087 1.0399 1.0375 1.0568 1.174 1.031]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000499.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.367 1.3068 1.3222 1.3171 1.4502 1.3871 1.343]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000500.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.367 1.3068 1.3222 1.3171 1.4502 1.3871 1.343]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000501.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1948 1.1834]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000502.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1254 1.2028 1.1766 1.2052 1.2084 1.1524 1.1032 1.1977 1.2937 1.0171 1.0787 1.2017 1.2965 1.1468]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000503.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2296]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000504.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2716 1.3971]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000506.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3134 1.214 1.2461 1.2872 1.209 1.1122 1.1169 1.462 1.4809 1.3203 1.0778 1.1088 1.0905 1.0872 1.1193 1.0923 1.0567 1.0825 1.4075 1.3613]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000507.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3134 1.214 1.2461 1.2872 1.209 1.1122 1.1169 1.462 1.4809 1.3203 1.0778 1.1088 1.0905 1.0872 1.1193 1.0923 1.0567 1.0825 1.4075 1.3613]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000508.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3134 1.214 1.2461 1.2872 1.209 1.1122 1.1169 1.462 1.4809 1.3203 1.0778 1.1088 1.0905 1.0872 1.1193 1.0923 1.0567 1.0825 1.4075 1.3613]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000509.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3134 1.214 1.2461 1.2872 1.209 1.1122 1.1169 1.462 1.4809 1.3203 1.0778 1.1088 1.0905 1.0872 1.1193 1.0923 1.0567 1.0825 1.4075 1.3613]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000510.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000511.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.39]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000512.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1057 1.1904]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000516.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000517.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.03]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000518.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.22]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000519.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1256 1.1202]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000520.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0432 1.261]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000521.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4123]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000523.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4325]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000524.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4325]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000526.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000527.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0916]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000528.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.195]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000529.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000530.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000531.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000532.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000533.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000534.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000535.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000538.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1539]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000539.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1539]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000541.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2076]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000542.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2076]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000543.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2076]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000545.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1614 1.261 1.0286]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000549.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0812]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000550.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2837 1.2465 1.3159]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000551.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2852]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000552.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.459 1.3948]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000554.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0712]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000555.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0712]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000559.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0119 1.1955 1.0194 1.0647 1.0211 1.0261 1.2364 1.0347 1.314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000565.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3823 1.3545 1.4137 1.154 1.3619 1.3342 1.3551 1.3339 1.359]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000566.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3243 1.225]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000568.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0282]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000569.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0282]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000572.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1074 1.0704]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000576.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3914 1.3058 1.2712 1.2336 1.444 1.2158]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000577.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4566]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000580.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1967]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000581.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2598 1.3114 1.3175 1.0246 1.4372 1.0436 1.3609 1.191 1.3376 1.2616 1.2555 1.3 1.2475 1.2016]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000582.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2598 1.3114 1.3175 1.0246 1.4372 1.0436 1.3609 1.191 1.3376 1.2616 1.2555 1.3 1.2475 1.2016]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000583.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2113 1.2059 1.4601]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000584.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1425 1.1342 1.3027 1.2293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000586.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0571 1.327 1.2399 1.1931 1.1115 1.1608 1.0154 1.228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000587.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0453]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000588.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3536 1.0144 1.0818 1.1754 1.2144 1.2513 1.3003 1.3568 1.315 1.1019 1.2382]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000589.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3536 1.0144 1.0818 1.1754 1.2144 1.2513 1.3003 1.3568 1.315 1.1019 1.2382]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000592.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2468 1.0382 1.0773]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000593.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2468 1.0382 1.0773]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000594.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2468 1.0382 1.0773]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000595.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.032]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000596.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000597.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1181 1.2441 1.0422 1.3955 1.1833 1.4237 1.4651 1.1417 1.1866 1.0938]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000598.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0118 1.3348 1.0334]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000599.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000600.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000601.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000602.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000603.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000604.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000605.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000606.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000608.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1339]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000609.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1256 1.1202]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000610.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4574 1.141 1.2514 1.0366 1.408]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000611.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0194 1.2706 1.0633]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000612.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1784 1.3131]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000613.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4367 1.0919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000614.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4233 1.0268 1.4472 1.4143]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000615.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0414 1.2557 1.1841 1.3403 1.4323]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000617.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1415]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000618.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2477 1.0966 1.0254 1.1976 1.0638 1.4171 1.1151 1.0168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000621.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2014 1.026 1.3853 1.2427 1.1528 1.1792 1.0983 1.2052]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000622.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000623.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000624.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0879 1.0447 1.0649]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000625.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1897 1.1388]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000626.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000627.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1942 1.3051 1.3048 1.1821 1.3786 1.3067 1.1843 1.4565 1.3056 1.0753 1.2209 1.0855 1.0305 1.2888 1.3607]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000628.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.1607 1.1498 1.2068 1.1738 1.1713 1.177 1.4336 1.179 1.1247 1.1285 1.1228 1.1898 1.0465 1.0555 1.1407 1.162 1.1592 1.1576 1.3573 1.1937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000630.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3847 1.0703 1.4425 1.1328 1.3777 1.3455]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000631.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4322 1.4157 1.036]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000632.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0516 1.3319 1.1111 1.0484 1.0286 1.1049]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000633.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2916 1.275 1.4302 1.4422 1.4361 1.1156 1.4189 1.4698 1.4517 1.4587 1.4151 1.3592 1.2751 1.4392 1.4403 1.1641 1.4179 1.4494 1.3842 1.451 1.3966 1.4557 1.4013 1.3754 1.3953 1.3896\n", " 1.4612 1.299 1.2979 1.4233 1.2894]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000634.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1658 1.0536 1.1723 1.0751]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000635.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1658 1.0536 1.1723 1.0751]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000636.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1658 1.0536 1.1723 1.0751]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000637.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1173 1.168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000638.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1173 1.168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000639.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0952 1.219 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000640.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1152 1.1614 1.0889 1.138]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000641.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3154 1.3377 1.0388 1.0693 1.0537 1.275 1.3388 1.2903 1.2488 1.2648]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000642.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0607 1.0462 1.3976 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000643.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1614 1.261 1.0286]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000644.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0288 1.0281 1.0117 1.0186 1.2035 1.0144 1.0121]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000645.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0389]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000647.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0454]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000648.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0203]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000652.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0507 1.1707 1.1062 1.0498 1.4468 1.2013 1.0681]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000653.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2408 1.0816 1.0947 1.1683 1.3866 1.2441 1.3405 1.2633]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000654.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1599 1.015]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000656.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2265 1.3069]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000657.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3736 1.3781 1.358 1.2635 1.3725 1.0657 1.4004 1.3866 1.352 1.3398 1.3075 1.3549 1.375 1.4535 1.3392 1.364 1.3276 1.3996 1.3412 1.3606 1.354]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000658.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0732 1.0623]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000659.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1023 1.0417 1.2845 1.0395]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000660.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1023 1.0417 1.2845 1.0395]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000661.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1023 1.0417 1.2845 1.0395]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000662.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.46 1.4187]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000664.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3465 1.4145 1.0238 1.3548]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000665.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1916]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000666.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.068 1.2033 1.0507 1.0876 1.3228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000667.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.068 1.2033 1.0507 1.0876 1.3228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000668.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2417 1.3584 1.1288]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000669.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2417 1.3584 1.1288]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000670.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2852]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000671.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1625 1.2811 1.0798 1.2282 1.3015]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000672.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1625 1.2811 1.0798 1.2282 1.3015]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000673.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1625 1.2811 1.0798 1.2282 1.3015]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000674.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1625 1.2811 1.0798 1.2282 1.3015]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000675.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.077 1.1251 1.0913 1.3971 1.0682 1.1161 1.1624 1.1036 1.3631 1.1464 1.2978 1.1313 1.1067 1.2159 1.1498 1.0697 1.1656 1.026]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000676.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000677.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.272 1.4368]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000678.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.272 1.4368]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000679.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1254 1.2028 1.1766 1.2052 1.2084 1.1524 1.1032 1.1977 1.2937 1.0171 1.0787 1.2017 1.2965 1.1468]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000680.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000681.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000682.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000683.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000684.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000685.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000686.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000687.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000688.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2938 1.018 1.22 1.1032 1.102 1.1092 1.1338 1.3498 1.0683 1.0556 1.1221 1.0702 1.0575 1.0953 1.0746 1.3124 1.1746 1.1294 1.0159 1.27 1.0692 1.126]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000689.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3322 1.3673]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000690.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3322 1.3673]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000691.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3322 1.3673]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000693.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.304 1.4397 1.1057 1.4035 1.1752 1.2997 1.4379 1.3945 1.325 1.2983 1.3109 1.2467 1.0718 1.3842 1.3968]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000694.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0946]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000695.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0946]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000696.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0946]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000697.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3165 1.4403 1.223 1.1551 1.4079 1.3888 1.2563 1.2404 1.1289 1.1927]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000698.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000699.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1387 1.3106 1.0775 1.1513]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000700.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.22]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000701.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.22]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000702.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4367 1.0919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000703.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4367 1.0919]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000704.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4233 1.0268 1.4472 1.4143]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000705.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4233 1.0268 1.4472 1.4143]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000706.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3754 1.4037]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000707.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3754 1.4037]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000708.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0251 1.3488 1.0667]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000709.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0916]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000710.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0916]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000711.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0916]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000712.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.0953 1.3587]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000713.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.0953 1.3587]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000714.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.0953 1.3587]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000715.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.1607 1.1498 1.2068 1.1738 1.1713 1.177 1.4336 1.179 1.1247 1.1285 1.1228 1.1898 1.0465 1.0555 1.1407 1.162 1.1592 1.1576 1.3573 1.1937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000716.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0538 1.1607 1.1498 1.2068 1.1738 1.1713 1.177 1.4336 1.179 1.1247 1.1285 1.1228 1.1898 1.0465 1.0555 1.1407 1.162 1.1592 1.1576 1.3573 1.1937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000717.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.046 1.2007 1.3681]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000718.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.046 1.2007 1.3681]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000719.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2916 1.275 1.4302 1.4422 1.4361 1.1156 1.4189 1.4698 1.4517 1.4587 1.4151 1.3592 1.2751 1.4392 1.4403 1.1641 1.4179 1.4494 1.3842 1.451 1.3966 1.4557 1.4013 1.3754 1.3953 1.3896\n", " 1.4612 1.299 1.2979 1.4233 1.2894]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000720.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2916 1.275 1.4302 1.4422 1.4361 1.1156 1.4189 1.4698 1.4517 1.4587 1.4151 1.3592 1.2751 1.4392 1.4403 1.1641 1.4179 1.4494 1.3842 1.451 1.3966 1.4557 1.4013 1.3754 1.3953 1.3896\n", " 1.4612 1.299 1.2979 1.4233 1.2894]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000721.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2916 1.275 1.4302 1.4422 1.4361 1.1156 1.4189 1.4698 1.4517 1.4587 1.4151 1.3592 1.2751 1.4392 1.4403 1.1641 1.4179 1.4494 1.3842 1.451 1.3966 1.4557 1.4013 1.3754 1.3953 1.3896\n", " 1.4612 1.299 1.2979 1.4233 1.2894]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000722.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2916 1.275 1.4302 1.4422 1.4361 1.1156 1.4189 1.4698 1.4517 1.4587 1.4151 1.3592 1.2751 1.4392 1.4403 1.1641 1.4179 1.4494 1.3842 1.451 1.3966 1.4557 1.4013 1.3754 1.3953 1.3896\n", " 1.4612 1.299 1.2979 1.4233 1.2894]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000723.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2916 1.275 1.4302 1.4422 1.4361 1.1156 1.4189 1.4698 1.4517 1.4587 1.4151 1.3592 1.2751 1.4392 1.4403 1.1641 1.4179 1.4494 1.3842 1.451 1.3966 1.4557 1.4013 1.3754 1.3953 1.3896\n", " 1.4612 1.299 1.2979 1.4233 1.2894]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000724.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0952 1.219 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000725.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0952 1.219 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000726.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0952 1.219 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000727.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3154 1.3377 1.0388 1.0693 1.0537 1.275 1.3388 1.2903 1.2488 1.2648]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000728.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3154 1.3377 1.0388 1.0693 1.0537 1.275 1.3388 1.2903 1.2488 1.2648]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000729.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000730.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000731.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000732.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000733.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0389]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000734.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481 1.2492 1.4525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000735.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481 1.2492 1.4525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000736.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481 1.2492 1.4525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000737.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481 1.2492 1.4525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000738.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481 1.2492 1.4525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000743.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3606 1.0951]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000744.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.0137 1.1085]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000745.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1202 1.2178 1.0562 1.0855 1.0295 1.4284 1.0975 1.1485 1.1605 1.0695 1.0893 1.1028 1.0967 1.31 1.1245]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000748.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.136 1.2483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000749.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.459 1.3948]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000750.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.459 1.3948]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000751.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000752.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000753.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000754.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000755.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000756.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000757.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000758.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000759.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000760.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000761.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000762.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000763.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000764.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.367 1.3068 1.3222 1.3171 1.4502 1.3871 1.343]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000765.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.032]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000766.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.39]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000767.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2059 1.1574]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000768.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3986 1.4119 1.3449 1.4049 1.163 1.408 1.0589 1.4209 1.3837 1.3754 1.246 1.4011 1.1946 1.1903 1.3871 1.252 1.3917]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000769.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3986 1.4119 1.3449 1.4049 1.163 1.408 1.0589 1.4209 1.3837 1.3754 1.246 1.4011 1.1946 1.1903 1.3871 1.252 1.3917]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000772.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.314 1.1316 1.061]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000775.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0668]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000777.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2649 1.2485 1.2453 1.2704 1.2382 1.2625 1.2872 1.234 1.2177 1.1553]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000778.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0492 1.0264 1.0191 1.0769 1.4468 1.1342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000779.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0546 1.2324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000780.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1284]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000784.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1327 1.0996]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000785.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1097]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000789.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0772]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000791.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3269 1.2657 1.2163 1.329 1.331 1.3371 1.0877 1.3574 1.2046 1.36 1.1283 1.2561]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000792.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.091]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000793.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.13]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000795.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0724 1.1465 1.1757 1.114 1.1201 1.1337 1.0671 1.0832 1.0644 1.0992 1.1717 1.1067 1.2286 1.0985 1.1826 1.0288 1.1958 1.085 1.1086 1.0146 1.1079 1.107 1.0316 1.0853 1.132 1.0802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000800.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.05]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000801.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3756 1.395 1.3772]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000802.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1524 1.0273 1.1426 1.1536 1.077 1.3664 1.0455 1.1725 1.0692 1.3674 1.0485 1.1537 1.0777]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000803.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3229 1.464 1.2557 1.1493]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000804.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3229 1.464 1.2557 1.1493]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000808.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2761 1.1774 1.1593 1.2493 1.16 1.1589 1.3185 1.1951 1.1904 1.3984 1.1755 1.1715 1.167 1.184 1.4111 1.2075]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000810.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4552 1.4155 1.3837 1.2465]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000811.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0632 1.0241 1.3898 1.2013]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000812.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.32 1.2879 1.0713 1.1626 1.2899]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000814.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2653 1.0717 1.0413 1.0219 1.1907 1.3297 1.3178 1.131 1.0974 1.033 1.0292 1.0212 1.0834 1.127]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000816.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2162]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000818.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000820.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4657 1.4056 1.4249 1.3656 1.3089 1.363]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000821.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0191 1.4111 1.0328]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000822.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.054 1.4505 1.2707 1.2308 1.1827 1.343 1.2302 1.0518 1.253 1.2448 1.3444]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000823.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.054 1.4505 1.2707 1.2308 1.1827 1.343 1.2302 1.0518 1.253 1.2448 1.3444]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000824.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4292]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000825.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2141 1.2382 1.2764 1.2476 1.3899 1.1673 1.2986 1.2267 1.2142 1.436 1.2327]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000826.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2642 1.3742 1.2885 1.1755]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000827.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4312 1.3573 1.3779 1.3922 1.3743 1.3659 1.4256 1.4039 1.3227 1.3479 1.3559 1.308 1.3917 1.3706 1.3534]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000829.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2115]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000830.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3753]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000831.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0505 1.0278]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000832.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2782]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000833.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2433 1.0302 1.0205 1.1711 1.2713]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000834.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4256 1.3302]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000835.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2771 1.3713]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000836.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2771 1.3713]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000837.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3939]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000838.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3643 1.0459 1.4085]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000839.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0589 1.3825]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000840.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2816 1.0492 1.2663 1.2933 1.3978 1.273 1.2444 1.296 1.2382 1.2494 1.2247 1.1607 1.1884 1.238 1.2492 1.0777 1.2691 1.152 1.1418 1.2344]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000841.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3301 1.3912 1.4421 1.4759 1.3544 1.2422 1.0575 1.4611]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000844.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.171 1.2591 1.2601]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000848.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.297 1.1302 1.2356 1.3275 1.4435 1.0936 1.224]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000850.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000851.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000852.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000853.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000854.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000855.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000858.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2234 1.2273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000860.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000861.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1706 1.0647 1.0452]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000862.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1706 1.0647 1.0452]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000863.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000864.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000866.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4376 1.1876 1.3234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000868.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1174]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000869.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000870.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1076 1.4457 1.4495 1.2955 1.4506]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000871.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0963 1.2388]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000872.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0963 1.2388]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000873.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2136]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000875.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3206 1.1809]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000876.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2079 1.3944 1.2641 1.2551 1.3111 1.3779]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000877.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2079 1.3944 1.2641 1.2551 1.3111 1.3779]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000878.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2079 1.3944 1.2641 1.2551 1.3111 1.3779]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000879.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0815]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000880.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0815]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000881.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000882.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000883.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000884.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000885.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000886.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3142 1.305 1.2143 1.1624 1.1243]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000887.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3847 1.0703 1.4425 1.1328 1.3777 1.3455]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000890.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.115 1.0837]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000891.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4092]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000892.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276 1.3572 1.0554 1.4834 1.2971 1.2622 1.0802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000893.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276 1.3572 1.0554 1.4834 1.2971 1.2622 1.0802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000894.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276 1.3572 1.0554 1.4834 1.2971 1.2622 1.0802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000898.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3622 1.2944 1.4458 1.237 1.3262 1.1019 1.3491 1.2381 1.2309 1.1671]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000903.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.309]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000904.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2384 1.2567 1.2147 1.3016 1.2493]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000905.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1377]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000906.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1461]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000907.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1314 1.0269 1.1178 1.055 1.0339]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000908.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1314 1.0269 1.1178 1.055 1.0339]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000909.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1314 1.0269 1.1178 1.055 1.0339]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000910.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1314 1.0269 1.1178 1.055 1.0339]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000911.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000912.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000913.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3306 1.4458 1.4114 1.2858 1.1532 1.4603 1.4826]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000914.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3702 1.242]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000915.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3702 1.242]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000916.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.46 1.4187]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000917.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.182 1.241]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000918.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.182 1.241]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000919.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1633 1.2284 1.2117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000920.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1633 1.2284 1.2117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000921.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3266 1.0494 1.3057 1.2081]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000922.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4101]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000923.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.068 1.2033 1.0507 1.0876 1.3228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000924.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.068 1.2033 1.0507 1.0876 1.3228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000925.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.068 1.2033 1.0507 1.0876 1.3228]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000927.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0237 1.2134 1.3665 1.4545 1.4121 1.0725 1.0888 1.1255 1.328 1.283]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000928.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0237 1.2134 1.3665 1.4545 1.4121 1.0725 1.0888 1.1255 1.328 1.283]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000929.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0237 1.2134 1.3665 1.4545 1.4121 1.0725 1.0888 1.1255 1.328 1.283]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000930.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1969 1.3824 1.4004 1.4641 1.493 1.2]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000931.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1969 1.3824 1.4004 1.4641 1.493 1.2]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000932.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1969 1.3824 1.4004 1.4641 1.493 1.2]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000933.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.13]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000935.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000936.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000937.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000938.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000939.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000940.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000941.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000942.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1765 1.1189 1.2503 1.117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000943.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000944.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000945.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000946.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000947.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000948.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000949.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000950.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0675 1.0587 1.4199 1.0759 1.0101 1.1971 1.0256]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000951.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000952.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000953.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1525]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000966.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0987]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000967.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000968.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000969.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000970.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000971.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000972.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000973.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000974.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000975.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000976.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000977.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000978.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000979.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.177 1.4499 1.3138 1.1645 1.0836 1.0597 1.3531]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000981.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000982.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000983.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000984.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000985.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000986.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000987.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000988.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000989.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000990.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000991.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000992.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000993.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000994.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000995.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000996.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1431 1.2012 1.2914 1.4395 1.226 1.2129 1.0221 1.2025 1.0368 1.0622 1.0985 1.0168 1.2291]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000997.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000998.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_000999.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001000.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001001.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001002.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001003.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001004.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001005.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001006.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001007.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4218 1.4156 1.3342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001020.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2635]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001022.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3253]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001023.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.148]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001024.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0146]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001025.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001026.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001033.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.047 1.1259]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001034.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.047 1.1259]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001035.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1188]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001037.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2507 1.1559 1.2561 1.2419]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001041.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0282 1.0832]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001046.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1965]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001049.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0412]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001051.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.286]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001056.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1377]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001057.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2694]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001059.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0286 1.186]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001060.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.414 1.2287 1.3823 1.3763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001061.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.272]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001062.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0785]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001066.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1359]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001067.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1359]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001069.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1544]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001071.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4237 1.254]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001072.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001073.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0367]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001074.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.352 1.2153]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001075.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.352 1.2153]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001076.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001083.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0443 1.2404 1.461 1.3867]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001087.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4346]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001089.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3943 1.386]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001090.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1709 1.0168]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001092.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1457]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001093.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2852]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001094.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2852]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001097.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1161 1.0308 1.1174]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001100.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0379 1.2789 1.1818]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001103.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0116 1.114 1.4075 1.1921 1.2107 1.428]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001106.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0938 1.1077]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001107.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2829 1.0874 1.2187 1.018 1.3874 1.288 1.0823 1.0439 1.4234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001108.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0864]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001109.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1277]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001111.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0581]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001114.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001117.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.047 1.1259]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001118.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2789 1.0191 1.1662]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001119.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0194 1.2706 1.0633]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001122.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3156 1.1853]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001123.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0906]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001124.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0906]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001125.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001126.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001127.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001128.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001129.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001130.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001131.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001132.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001133.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1567]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001134.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0251 1.3488 1.0667]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001138.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2136]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001139.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2136]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001140.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0949 1.206]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001143.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0162 1.4574 1.433]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001146.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0417 1.3097 1.1494]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001148.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2092 1.2012 1.2004 1.0987 1.2662 1.1445 1.2515 1.0877 1.2308 1.0146 1.0347]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001151.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.065]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001152.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1314 1.0269 1.1178 1.055 1.0339]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001153.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1633 1.2284 1.2117]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001154.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0275 1.2791]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001157.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2069]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001159.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4348]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001160.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4348]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001161.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0207]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001162.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0207]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001163.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1002 1.4521 1.4133 1.4874]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001164.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1178 1.2011]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001165.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1794]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001176.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0209 1.1373 1.3803 1.47 1.4105 1.0566]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001178.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1725 1.178]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001179.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2747 1.039]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001180.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2859]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001181.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1762]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001182.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0737 1.3978 1.0118 1.1678 1.3534 1.1618]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001185.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.052]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001186.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1193]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001187.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1193]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001188.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2193]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001190.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001191.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001192.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0105]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001193.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1188]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001195.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1305 1.3196 1.3168 1.2685 1.2089 1.2903 1.3913 1.2551 1.3263 1.2114 1.2164 1.2267 1.3943 1.1789 1.1463 1.2441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001196.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1305 1.3196 1.3168 1.2685 1.2089 1.2903 1.3913 1.2551 1.3263 1.2114 1.2164 1.2267 1.3943 1.1789 1.1463 1.2441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001197.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1305 1.3196 1.3168 1.2685 1.2089 1.2903 1.3913 1.2551 1.3263 1.2114 1.2164 1.2267 1.3943 1.1789 1.1463 1.2441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001198.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1305 1.3196 1.3168 1.2685 1.2089 1.2903 1.3913 1.2551 1.3263 1.2114 1.2164 1.2267 1.3943 1.1789 1.1463 1.2441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001199.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001200.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001201.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001202.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001203.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0925 1.3179 1.0125 1.0143 1.0492 1.1162 1.1077 1.0736 1.0809 1.268 1.0516 1.1785 1.0196 1.3286 1.3086 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001205.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001206.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001207.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001208.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001209.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001215.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3449]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001216.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3449]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001217.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4627 1.2884 1.452 1.3891 1.4209]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001218.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4627 1.2884 1.452 1.3891 1.4209]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001221.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.081 1.0702 1.3911 1.2859 1.467 1.3419 1.332 1.0593 1.1911 1.0545 1.472]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001222.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.081 1.0702 1.3911 1.2859 1.467 1.3419 1.332 1.0593 1.1911 1.0545 1.472]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001223.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.081 1.0702 1.3911 1.2859 1.467 1.3419 1.332 1.0593 1.1911 1.0545 1.472]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001224.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.081 1.0702 1.3911 1.2859 1.467 1.3419 1.332 1.0593 1.1911 1.0545 1.472]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001227.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.02 1.1304 1.2213 1.1192 1.2571 1.3272 1.1754]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001228.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2942 1.3317 1.1404 1.0905 1.4771 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001230.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276 1.3572 1.0554 1.4834 1.2971 1.2622 1.0802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001231.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1276 1.3572 1.0554 1.4834 1.2971 1.2622 1.0802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001232.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0454]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001233.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0454]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001234.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0454]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001235.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1551 1.3875 1.1524 1.4426 1.352 1.2997 1.3483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001236.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1551 1.3875 1.1524 1.4426 1.352 1.2997 1.3483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001237.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1191 1.3373 1.4758 1.3819 1.3537]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001238.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2265 1.3069]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001239.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2265 1.3069]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001240.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1666 1.0802 1.0646 1.0511 1.0813]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001241.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0458]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001245.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0637]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001246.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0637]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001248.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001249.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001250.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001251.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001252.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001253.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001255.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001256.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001257.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1824 1.2572 1.4396]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001259.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3802 1.0485]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001260.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3802 1.0485]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001261.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3802 1.0485]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001262.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001263.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001264.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001265.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001266.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001267.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001268.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0816 1.3929 1.4303 1.1046 1.2024 1.0534 1.1325 1.087 1.0212 1.342 1.2978 1.0764 1.0288 1.0366]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001271.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3586 1.3574 1.2148 1.4258 1.4257 1.2811 1.3354 1.2343 1.3349 1.3575 1.185 1.3623 1.3514 1.1002 1.3435 1.1284 1.411 1.2426 1.4178 1.289 1.1359 1.4374 1.3325 1.2147]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001272.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3586 1.3574 1.2148 1.4258 1.4257 1.2811 1.3354 1.2343 1.3349 1.3575 1.185 1.3623 1.3514 1.1002 1.3435 1.1284 1.411 1.2426 1.4178 1.289 1.1359 1.4374 1.3325 1.2147]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001273.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3586 1.3574 1.2148 1.4258 1.4257 1.2811 1.3354 1.2343 1.3349 1.3575 1.185 1.3623 1.3514 1.1002 1.3435 1.1284 1.411 1.2426 1.4178 1.289 1.1359 1.4374 1.3325 1.2147]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001274.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3586 1.3574 1.2148 1.4258 1.4257 1.2811 1.3354 1.2343 1.3349 1.3575 1.185 1.3623 1.3514 1.1002 1.3435 1.1284 1.411 1.2426 1.4178 1.289 1.1359 1.4374 1.3325 1.2147]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001279.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1784 1.3131]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001280.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1784 1.3131]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001281.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1784 1.3131]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001282.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1784 1.3131]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001283.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1784 1.3131]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001285.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001286.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871 1.1834 1.231 1.2807 1.0586 1.1844 1.4918 1.4567 1.4316]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001287.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1448]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001288.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1926]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001289.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1926]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001296.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3447 1.0975]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001298.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0637]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001299.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0637]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001300.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0637]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001304.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.098 1.3046 1.4014]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001305.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001306.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001307.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001308.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001309.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001310.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001311.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001312.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.041 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001313.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1906 1.3987]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001314.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1906 1.3987]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001315.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1906 1.3987]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001316.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2455 1.4706 1.3972 1.3619 1.1353 1.3134 1.3298]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001317.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2455 1.4706 1.3972 1.3619 1.1353 1.3134 1.3298]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001318.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2455 1.4706 1.3972 1.3619 1.1353 1.3134 1.3298]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001319.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0571]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001320.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0571]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001323.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3003 1.0663 1.1168 1.3624 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001324.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3003 1.0663 1.1168 1.3624 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001325.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3003 1.0663 1.1168 1.3624 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001326.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3003 1.0663 1.1168 1.3624 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001327.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3003 1.0663 1.1168 1.3624 1.308]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001328.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3515 1.0166 1.3976 1.259]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001329.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.236 1.1255]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001330.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.236 1.1255]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001331.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.236 1.1255]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001332.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.236 1.1255]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001333.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.236 1.1255]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001334.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.123 1.1801 1.1669]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001335.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.123 1.1801 1.1669]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001336.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.341 1.3945 1.459 1.0195]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001337.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.341 1.3945 1.459 1.0195]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001338.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.341 1.3945 1.459 1.0195]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001339.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.341 1.3945 1.459 1.0195]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001340.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001341.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001342.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001343.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001344.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001345.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001346.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.153 1.3689 1.096 1.478]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001347.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2041]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001349.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1011]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001353.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1143 1.1581]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001354.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1143 1.1581]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001355.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1143 1.1581]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001356.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0817 1.2004 1.2661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001357.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0817 1.2004 1.2661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001358.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0817 1.2004 1.2661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001359.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0817 1.2004 1.2661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001364.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1298 1.2678 1.1358 1.0982 1.0199]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001365.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1298 1.2678 1.1358 1.0982 1.0199]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001366.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1298 1.2678 1.1358 1.0982 1.0199]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001367.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1298 1.2678 1.1358 1.0982 1.0199]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001368.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1298 1.2678 1.1358 1.0982 1.0199]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001369.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1298 1.2678 1.1358 1.0982 1.0199]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001370.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001371.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001372.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001373.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001374.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001375.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001376.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0625 1.1536 1.0337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001377.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2061]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001378.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2061]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001379.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2061]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001380.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0133]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001381.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0133]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001382.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2565 1.0135 1.3584 1.3441 1.2876 1.2935 1.2937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001383.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2565 1.0135 1.3584 1.3441 1.2876 1.2935 1.2937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001384.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2565 1.0135 1.3584 1.3441 1.2876 1.2935 1.2937]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001387.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001388.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001389.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001390.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001391.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2575]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001392.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0892]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001393.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0892]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001394.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0892]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001395.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0892]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001397.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001398.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001399.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001400.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001401.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001402.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001403.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001404.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001405.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001406.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001407.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001408.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001409.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1544 1.1987 1.1314]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001411.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0516 1.3319 1.1111 1.0484 1.0286 1.1049]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001417.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001418.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001419.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001420.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4483]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001421.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001422.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001423.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001424.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001425.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001426.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001427.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1904]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001428.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1904]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001431.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3948]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001432.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3948]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001442.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.303 1.1787 1.2042 1.1864 1.2294 1.2534 1.4164 1.1914 1.2802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001443.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1684]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001444.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1684]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001446.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001447.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001448.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001449.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001450.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001451.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001452.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001453.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001454.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001455.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001456.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001458.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4748 1.1871 1.1566]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001459.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4748 1.1871 1.1566]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001460.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4748 1.1871 1.1566]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001461.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001462.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001463.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0146]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001464.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0416]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001465.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0416]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001466.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2379]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001467.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001468.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001469.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001470.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001471.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001472.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001473.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0294]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001474.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.263]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001475.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001476.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001477.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001478.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001479.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001480.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001481.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001482.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001483.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001484.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001485.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001486.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001487.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001488.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001495.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1706 1.0647 1.0452]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001496.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1706 1.0647 1.0452]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001497.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2211 1.0764 1.1816]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001498.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2617]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001499.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001500.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001501.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001502.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001503.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001504.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2689 1.38 1.2666 1.4621 1.073]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001509.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001510.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001511.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001512.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001513.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001514.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001515.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001516.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001517.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001518.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001519.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001520.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001521.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001522.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001523.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001524.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001525.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3011 1.0271 1.0541]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001526.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.201 1.4511]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001527.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.201 1.4511]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001528.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.201 1.4511]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001529.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.201 1.4511]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001530.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001531.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001532.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001533.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001534.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001535.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001536.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001537.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001538.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001539.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001540.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001541.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001542.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001543.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001544.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001545.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001546.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001547.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001548.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001549.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001550.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001551.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001552.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001553.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001554.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001555.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001556.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001557.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4178 1.0167]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001558.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4178 1.0167]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001559.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4178 1.0167]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001560.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2323 1.2429 1.1507]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001561.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2323 1.2429 1.1507]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001562.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3782]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001564.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0719]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001565.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0719]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001566.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0146]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001567.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0146]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001568.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001569.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001570.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001571.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001572.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001573.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001574.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001575.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001576.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001577.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001578.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001579.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.234 1.0519]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001580.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1324 1.2246 1.1323 1.0348 1.1518 1.4062 1.3396]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001581.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001582.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001583.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001584.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001585.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0294]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001586.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1731 1.1905]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001587.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1731 1.1905]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001588.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4255 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001589.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4255 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001590.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4255 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001591.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3529]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001592.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001593.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001594.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001595.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0325 1.3337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001597.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.047 1.1259]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001599.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0119 1.0116 1.0144]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001600.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2789 1.0191 1.1662]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001601.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2135]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001602.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2135]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001606.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001607.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001608.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001609.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2194]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001610.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2194]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001611.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2194]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001612.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001613.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1746 1.3096 1.2304 1.3089 1.1849 1.0635 1.1685 1.2952 1.4732 1.1838 1.017 1.0692 1.032 1.3774]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001614.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0936 1.0384]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001618.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001619.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001620.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001621.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001622.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001623.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001624.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001625.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001626.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001627.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001628.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001629.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001630.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001631.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001632.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001633.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001634.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001635.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001636.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001637.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1069 1.3028 1.1342 1.1989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001638.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4178 1.0167]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001639.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4748 1.1871 1.1566]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001642.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001643.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0137 1.3517 1.4374 1.424]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001644.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001646.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0149]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001647.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2205]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001648.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4255 1.2481]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001649.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001651.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001654.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0119 1.0116 1.0144]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001655.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0119 1.0116 1.0144]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001656.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0119 1.0116 1.0144]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001657.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2789 1.0191 1.1662]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001662.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0906]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001663.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2637]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001664.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0936 1.0384]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001665.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0936 1.0384]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001668.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0652 1.3586 1.242 1.1107 1.1944]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001669.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0652 1.3586 1.242 1.1107 1.1944]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001670.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0652 1.3586 1.242 1.1107 1.1944]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001671.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1295 1.2927 1.409 1.1628 1.1432 1.2169]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001672.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1295 1.2927 1.409 1.1628 1.1432 1.2169]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001673.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0338 1.1838 1.2401 1.1805 1.4813 1.1295 1.2744 1.2119]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001675.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1776 1.3347 1.0123 1.0676]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001676.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1776 1.3347 1.0123 1.0676]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001678.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0415]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001679.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0282 1.0832]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001680.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0282 1.0832]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001681.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2175 1.0905 1.1523 1.3564 1.0653 1.3505 1.3475 1.0368 1.261]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001682.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2088 1.1474 1.3309 1.0401 1.1921]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001683.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2095 1.0306]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001686.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1964]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001687.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1964]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001690.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3841 1.1723 1.287 1.4304 1.3563 1.469 1.0281 1.0876 1.2985 1.2839 1.1523 1.232 1.2772 1.1218 1.4149]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001691.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3841 1.1723 1.287 1.4304 1.3563 1.469 1.0281 1.0876 1.2985 1.2839 1.1523 1.232 1.2772 1.1218 1.4149]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001692.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3841 1.1723 1.287 1.4304 1.3563 1.469 1.0281 1.0876 1.2985 1.2839 1.1523 1.232 1.2772 1.1218 1.4149]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001693.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.34 1.074]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001694.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.191 1.3208 1.2147 1.1359]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001695.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0659]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001696.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0659]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001697.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2783 1.3093 1.2017 1.1884]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001699.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0939]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001700.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001702.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2323 1.2429 1.1507]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001703.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3782]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001709.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2095 1.0306]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001710.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2095 1.0306]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001711.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2095 1.0306]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001712.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2095 1.0306]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001713.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2095 1.0306]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001714.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001715.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.053]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001721.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2067]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001723.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2521 1.0888 1.2359 1.1438]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001729.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0871]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001730.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1254 1.198 1.1768 1.2427 1.0678 1.1613 1.1801]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001731.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3457]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001732.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.335]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001733.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001735.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2816 1.0743 1.2387 1.131 1.239 1.0616]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001736.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2816 1.0743 1.2387 1.131 1.239 1.0616]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001737.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1487 1.3627 1.1307 1.3669 1.2338 1.1603 1.3146 1.0324 1.087 1.3173 1.1007 1.07 1.3735 1.4539]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001738.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2523]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001739.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2523]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001740.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2523]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001741.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4172]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001742.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4172]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001743.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4172]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001744.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0697 1.1177 1.0422 1.0649 1.1312 1.0904 1.0346 1.2556 1.3053 1.0807 1.0768]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001745.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0697 1.1177 1.0422 1.0649 1.1312 1.0904 1.0346 1.2556 1.3053 1.0807 1.0768]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001746.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0697 1.1177 1.0422 1.0649 1.1312 1.0904 1.0346 1.2556 1.3053 1.0807 1.0768]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001748.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1111 1.0718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001749.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1111 1.0718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001750.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1111 1.0718]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001752.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3562 1.1125 1.1873 1.3461 1.4313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001753.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3562 1.1125 1.1873 1.3461 1.4313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001754.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0355]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001757.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3943 1.386]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001758.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001759.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001760.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001761.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001762.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001763.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001764.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001765.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001766.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001767.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1018 1.1393 1.2303]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001768.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.261 1.042 1.2357 1.2181 1.3573 1.1194 1.0664 1.1048 1.2896 1.4323 1.2687]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001773.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.0695]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001774.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2178 1.0695]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001775.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3226]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001779.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1686 1.0799]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001780.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3066 1.1678 1.1537 1.0116 1.2324 1.3885]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001781.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3066 1.1678 1.1537 1.0116 1.2324 1.3885]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001782.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.0919 1.2404 1.3115 1.4192 1.4631 1.3047 1.3574 1.1441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001783.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.0919 1.2404 1.3115 1.4192 1.4631 1.3047 1.3574 1.1441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001784.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1367 1.0919 1.2404 1.3115 1.4192 1.4631 1.3047 1.3574 1.1441]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001786.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001787.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001788.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.087 1.147]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001789.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0137 1.3517 1.4374 1.424]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001790.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0137 1.3517 1.4374 1.424]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001791.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0137 1.3517 1.4374 1.424]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001792.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001794.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001796.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0325 1.3337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001797.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.169 1.4086 1.2435 1.0387 1.4234 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001798.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3001 1.3775]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001799.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4376 1.1876 1.3234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001800.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4376 1.1876 1.3234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001801.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4376 1.1876 1.3234]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001802.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3156 1.1853]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001803.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3156 1.1853]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001805.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0133]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001806.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0133]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001807.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001808.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001809.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001810.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001811.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001812.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001813.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001814.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001815.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001816.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001817.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001818.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001819.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001820.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0565 1.1241 1.3373 1.322 1.0681 1.0291 1.0589 1.3282 1.2884 1.1272 1.2933 1.2403 1.3223 1.2763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001821.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2146]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001822.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3023]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001823.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001824.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001825.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001826.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001827.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001828.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001829.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001830.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001831.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001832.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001833.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001834.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001835.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001836.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4021 1.0449 1.0787 1.0732 1.1567 1.2314 1.0704 1.3354 1.071 1.1141 1.0731 1.2484 1.1081 1.0551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001837.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0659]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001838.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0913]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001839.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2092 1.2012 1.2004 1.0987 1.2662 1.1445 1.2515 1.0877 1.2308 1.0146 1.0347]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001840.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001841.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001842.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001843.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001844.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001845.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001846.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001847.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001848.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001849.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001850.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2352 1.2737 1.361 1.4252 1.3834 1.3636 1.4238 1.4277 1.401 1.0273 1.222]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001851.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1191 1.3373 1.4758 1.3819 1.3537]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001852.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001853.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001854.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001855.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001856.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001857.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001858.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001859.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001860.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001861.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001862.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001863.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001864.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001865.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.051 1.0583 1.0265 1.0552]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001868.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.34 1.3341 1.1962 1.1168 1.2329 1.0755 1.1467 1.0857 1.0195 1.1167 1.413]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001869.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.34 1.3341 1.1962 1.1168 1.2329 1.0755 1.1467 1.0857 1.0195 1.1167 1.413]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001870.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001871.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001872.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001873.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3106]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001874.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001875.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.402 1.1218 1.2781 1.1112 1.3206 1.0495 1.2776 1.2673 1.3911]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001876.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.031 1.0931 1.3524 1.2304 1.214 1.2602 1.1911 1.2086 1.2284 1.2342 1.2313]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001879.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001880.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001881.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001882.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001883.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001884.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001885.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001886.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001887.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1949 1.2836 1.2897 1.3878 1.2928 1.1202 1.381 1.3644 1.4338 1.2342 1.0438 1.4661]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001888.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2323 1.2429 1.1507]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001889.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1401 1.1537]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001890.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1401 1.1537]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001891.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001892.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001893.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001894.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001895.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001896.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001897.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001898.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001899.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001900.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001901.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001902.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001903.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001904.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3284 1.2484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001905.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2234 1.2273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001906.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001907.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1324 1.2246 1.1323 1.0348 1.1518 1.4062 1.3396]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001908.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1324 1.2246 1.1323 1.0348 1.1518 1.4062 1.3396]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001909.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1324 1.2246 1.1323 1.0348 1.1518 1.4062 1.3396]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001910.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0984 1.0379 1.2711]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001911.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3144 1.0185 1.0115 1.0481 1.4546 1.0459 1.4496 1.293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001912.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3144 1.0185 1.0115 1.0481 1.4546 1.0459 1.4496 1.293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001913.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3144 1.0185 1.0115 1.0481 1.4546 1.0459 1.4496 1.293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001914.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3144 1.0185 1.0115 1.0481 1.4546 1.0459 1.4496 1.293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001915.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001916.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001917.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001918.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001919.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.379 1.1509 1.2056 1.0189]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001920.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.379 1.1509 1.2056 1.0189]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001921.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.379 1.1509 1.2056 1.0189]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001922.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.379 1.1509 1.2056 1.0189]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001923.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.379 1.1509 1.2056 1.0189]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001924.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0242]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001925.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0242]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001926.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001927.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001928.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0177 1.3068]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001929.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0177 1.3068]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001930.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0177 1.3068]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001931.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0177 1.3068]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001932.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0177 1.3068]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001933.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001934.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001935.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001936.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001937.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001938.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001939.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001940.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001941.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001942.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001943.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001944.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001945.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001946.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001947.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001948.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001949.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001950.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2311 1.0994 1.1787 1.0172 1.3713 1.435]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001951.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0358 1.3669 1.2754 1.0494 1.2859]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001952.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1778 1.1851 1.1566 1.1064]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001953.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0325 1.3337]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001955.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001956.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001957.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1542]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001958.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2635]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001959.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2673 1.3585 1.2351 1.3888]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001960.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3253]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001961.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001962.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001963.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3144 1.0185 1.0115 1.0481 1.4546 1.0459 1.4496 1.293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001964.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3144 1.0185 1.0115 1.0481 1.4546 1.0459 1.4496 1.293]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001966.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001967.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001968.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001969.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001973.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2559 1.0411 1.2715 1.3184]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001974.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2559 1.0411 1.2715 1.3184]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001975.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.201 1.4511]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001976.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4361 1.3512 1.424]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001977.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0652 1.3586 1.242 1.1107 1.1944]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001978.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0652 1.3586 1.242 1.1107 1.1944]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001979.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1306 1.1646 1.1511 1.057 1.1493]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001980.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1306 1.1646 1.1511 1.057 1.1493]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001983.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1776 1.3347 1.0123 1.0676]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001984.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0415]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001985.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0949 1.206]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001986.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.137 1.1783]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001987.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3336 1.1284 1.227 1.1123 1.1955 1.3635 1.0413]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001988.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3336 1.1284 1.227 1.1123 1.1955 1.3635 1.0413]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001989.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0723 1.2011 1.3391 1.1489 1.1474 1.0335]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001990.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1256 1.0654]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001991.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1256 1.0654]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001992.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3841 1.1723 1.287 1.4304 1.3563 1.469 1.0281 1.0876 1.2985 1.2839 1.1523 1.232 1.2772 1.1218 1.4149]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001993.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4017 1.2871 1.0449 1.2869 1.3957 1.246 1.3421 1.0901 1.049 1.146 1.3777 1.3273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001995.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.029 1.102 1.3175]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001996.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0417 1.3097 1.1494]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001997.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.348 1.1043 1.0665 1.4448 1.4122]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_001998.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.286]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002001.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2694]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002002.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0286 1.186]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002003.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3012 1.0477]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002004.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.414 1.2287 1.3823 1.3763]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002006.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1527 1.2312 1.2637 1.2645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002007.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1527 1.2312 1.2637 1.2645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002008.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2798 1.321 1.3091 1.0725 1.1324 1.1356 1.3548 1.1002 1.0155 1.0806]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002009.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1933 1.0171 1.1628 1.2245 1.0234 1.0389 1.0834]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002010.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1933 1.0171 1.1628 1.2245 1.0234 1.0389 1.0834]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002011.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1105 1.0516]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002014.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0207]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002015.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3714]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002019.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002020.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2802]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002024.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2691]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002025.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2859]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002026.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3948 1.0915]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002027.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0542 1.3148 1.2589 1.4424]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002028.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0716 1.0402 1.2713 1.1127 1.4734 1.0883 1.2271]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002029.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1485 1.0747 1.0474 1.3841 1.0347 1.2545 1.4052]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002030.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2492 1.1554 1.0553]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002031.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2492 1.1554 1.0553]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002033.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3047 1.1845]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002034.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0551 1.2978]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002035.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1208 1.0526]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002036.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0986]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002037.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2468 1.0382 1.0773]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002038.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.23 1.1966]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002039.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0617 1.3322 1.1368 1.1124 1.1722]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002041.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0362 1.3258 1.1337 1.0276 1.0265 1.3846]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002042.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0399 1.0588]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002043.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3484]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002044.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002045.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3653 1.1506 1.0357 1.4301 1.432 1.0325 1.324]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002046.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1057 1.1904]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002047.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1057 1.1904]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002048.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1057 1.1904]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002049.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2673 1.3585 1.2351 1.3888]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002050.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2673 1.3585 1.2351 1.3888]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002051.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2673 1.3585 1.2351 1.3888]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002053.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3253]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002054.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002055.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002056.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002057.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002058.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0795 1.3223 1.1994 1.1939 1.3237]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002059.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002060.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2742 1.3133 1.0119 1.2534 1.1223 1.1029 1.1688 1.0677 1.1172 1.1364 1.1448 1.1693 1.099]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002061.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002062.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002063.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2011 1.0315 1.1263 1.0683]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002064.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002065.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0263 1.1972 1.0909 1.0155 1.0445 1.045]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002066.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2854 1.0326 1.2708 1.0163]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002067.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2789 1.0191 1.1662]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002070.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.169 1.4086 1.2435 1.0387 1.4234 1.3161]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002071.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2559 1.0411 1.2715 1.3184]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002072.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2559 1.0411 1.2715 1.3184]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002073.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0338 1.1838 1.2401 1.1805 1.4813 1.1295 1.2744 1.2119]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002074.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1776 1.3347 1.0123 1.0676]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002075.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1604 1.2529 1.2553]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002076.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1604 1.2529 1.2553]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002077.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1583 1.1798 1.373]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002078.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1583 1.1798 1.373]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002079.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1583 1.1798 1.373]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002080.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1583 1.1798 1.373]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002081.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1583 1.1798 1.373]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002082.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2088 1.1474 1.3309 1.0401 1.1921]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002084.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.137 1.1783]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002086.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0607 1.0462 1.3976 1.2249]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002087.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3453]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002088.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1545 1.1026 1.3005]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002089.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1545 1.1026 1.3005]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002090.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.286]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002093.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.025 1.2632 1.0793]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002094.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.025 1.2632 1.0793]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002095.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.025 1.2632 1.0793]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002096.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.025 1.2632 1.0793]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002097.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.025 1.2632 1.0793]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002098.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1934]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002099.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.109 1.0539 1.3559 1.4501]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002100.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.109 1.0539 1.3559 1.4501]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002101.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1429]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002102.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1551]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002103.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2144 1.4619]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002104.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1527 1.2312 1.2637 1.2645]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002107.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2798 1.321 1.3091 1.0725 1.1324 1.1356 1.3548 1.1002 1.0155 1.0806]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002108.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2798 1.321 1.3091 1.0725 1.1324 1.1356 1.3548 1.1002 1.0155 1.0806]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002109.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1933 1.0171 1.1628 1.2245 1.0234 1.0389 1.0834]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002110.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1105 1.0516]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002111.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0941 1.0995 1.3236 1.1913 1.1285]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002112.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002113.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0485 1.1685 1.244 1.1992 1.0701 1.0759 1.1103 1.0548 1.1521 1.0497 1.2563 1.014 1.3082 1.3827 1.408 1.3457 1.4221 1.0157]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002114.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1544]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002115.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002116.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002117.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002118.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002119.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002120.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1004 1.2732 1.0353 1.0978 1.1376]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002128.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0146]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002131.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2699 1.0158]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002134.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0167]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002135.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1463]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002137.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4017 1.2871 1.0449 1.2869 1.3957 1.246 1.3421 1.0901 1.049 1.146 1.3777 1.3273]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002138.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.029 1.102 1.3175]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002139.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0412]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002141.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2047]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002144.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.065]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002147.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0185]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002150.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1429]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002151.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.341 1.0231 1.2322 1.3017]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002152.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0753]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002153.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.272]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002155.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0292]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002156.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0785]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002157.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0275 1.2791]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002158.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0275 1.2791]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002162.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1359]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002165.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0173 1.1067 1.2355]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002166.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0792]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002168.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0492 1.0264 1.0191 1.0769 1.4468 1.1342]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002170.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.2947]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002171.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3715]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002177.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3989]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002180.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0596 1.1327 1.1733]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002181.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3798]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002182.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0642]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002183.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0986]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002185.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1302 1.1838 1.1564 1.0757 1.0377]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002188.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1102 1.0269]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002189.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1102 1.0269]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002192.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0118 1.0491]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002194.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1929 1.2485]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002197.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.4629]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002203.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.3197]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002204.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.1056 1.12 1.2937 1.1116 1.0538]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002205.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.302]\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/content/smartvision_dataset/detection/images/image_002209.jpg: ignoring corrupt image/label: non-normalized or out of bounds coordinates [ 1.0222]\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 33/33 2.9s/it 1:36\n", " all 523 1930 0.69 0.498 0.55 0.269\n", " person 153 446 0.547 0.511 0.508 0.202\n", " bicycle 23 136 0.626 0.684 0.677 0.389\n", " car 55 139 0.533 0.424 0.391 0.217\n", " motorcycle 21 28 0.451 0.571 0.584 0.282\n", " airplane 32 88 0.563 0.693 0.657 0.367\n", " bus 28 42 0.542 0.548 0.54 0.366\n", " train 32 42 0.635 0.705 0.76 0.275\n", " truck 27 36 0.865 0.356 0.537 0.271\n", " traffic light 15 39 1 0 0.4 0.249\n", " stop sign 36 38 0.418 0.605 0.523 0.227\n", " bench 28 47 0.517 0.456 0.513 0.167\n", " bird 26 112 0.538 0.385 0.383 0.278\n", " cat 60 73 0.748 0.712 0.748 0.363\n", " dog 48 152 0.657 0.605 0.689 0.326\n", " horse 20 48 0.548 0.688 0.674 0.281\n", " cow 22 52 0.762 0.558 0.626 0.211\n", " elephant 28 43 0.535 0.791 0.719 0.305\n", " bottle 25 44 1 0.0782 0.242 0.179\n", " cup 23 28 1 0 0.197 0.169\n", " bowl 34 49 0.845 0.735 0.791 0.209\n", " pizza 31 39 0.914 0.718 0.81 0.455\n", " cake 10 10 0.939 0.3 0.426 0.25\n", " chair 27 40 1 0 0.0205 0.0103\n", " couch 31 31 0.662 0.548 0.653 0.35\n", " potted plant 18 65 0.475 0.646 0.566 0.258\n", " bed 55 63 0.618 0.617 0.66 0.337\n", "Speed: 1.4ms preprocess, 171.0ms inference, 0.0ms loss, 2.8ms postprocess per image\n", "Results saved to \u001b[1m/content/runs/detect/val\u001b[0m\n", "ultralytics.utils.metrics.DetMetrics object with attributes:\n", "\n", "ap_class_index: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25])\n", "box: ultralytics.utils.metrics.Metric object\n", "confusion_matrix: \n", "curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']\n", "curves_results: [[array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,\n", " 0.024024, 0.025025, 0.026026, 0.027027, 0.028028, 0.029029, 0.03003, 0.031031, 0.032032, 0.033033, 0.034034, 0.035035, 0.036036, 0.037037, 0.038038, 0.039039, 0.04004, 0.041041, 0.042042, 0.043043, 0.044044, 0.045045, 0.046046, 0.047047,\n", " 0.048048, 0.049049, 0.05005, 0.051051, 0.052052, 0.053053, 0.054054, 0.055055, 0.056056, 0.057057, 0.058058, 0.059059, 0.06006, 0.061061, 0.062062, 0.063063, 0.064064, 0.065065, 0.066066, 0.067067, 0.068068, 0.069069, 0.07007, 0.071071,\n", " 0.072072, 0.073073, 0.074074, 0.075075, 0.076076, 0.077077, 0.078078, 0.079079, 0.08008, 0.081081, 0.082082, 0.083083, 0.084084, 0.085085, 0.086086, 0.087087, 0.088088, 0.089089, 0.09009, 0.091091, 0.092092, 0.093093, 0.094094, 0.095095,\n", " 0.096096, 0.097097, 0.098098, 0.099099, 0.1001, 0.1011, 0.1021, 0.1031, 0.1041, 0.10511, 0.10611, 0.10711, 0.10811, 0.10911, 0.11011, 0.11111, 0.11211, 0.11311, 0.11411, 0.11512, 0.11612, 0.11712, 0.11812, 0.11912,\n", " 0.12012, 0.12112, 0.12212, 0.12312, 0.12412, 0.12513, 0.12613, 0.12713, 0.12813, 0.12913, 0.13013, 0.13113, 0.13213, 0.13313, 0.13413, 0.13514, 0.13614, 0.13714, 0.13814, 0.13914, 0.14014, 0.14114, 0.14214, 0.14314,\n", " 0.14414, 0.14515, 0.14615, 0.14715, 0.14815, 0.14915, 0.15015, 0.15115, 0.15215, 0.15315, 0.15415, 0.15516, 0.15616, 0.15716, 0.15816, 0.15916, 0.16016, 0.16116, 0.16216, 0.16316, 0.16416, 0.16517, 0.16617, 0.16717,\n", " 0.16817, 0.16917, 0.17017, 0.17117, 0.17217, 0.17317, 0.17417, 0.17518, 0.17618, 0.17718, 0.17818, 0.17918, 0.18018, 0.18118, 0.18218, 0.18318, 0.18418, 0.18519, 0.18619, 0.18719, 0.18819, 0.18919, 0.19019, 0.19119,\n", " 0.19219, 0.19319, 0.19419, 0.1952, 0.1962, 0.1972, 0.1982, 0.1992, 0.2002, 0.2012, 0.2022, 0.2032, 0.2042, 0.20521, 0.20621, 0.20721, 0.20821, 0.20921, 0.21021, 0.21121, 0.21221, 0.21321, 0.21421, 0.21522,\n", " 0.21622, 0.21722, 0.21822, 0.21922, 0.22022, 0.22122, 0.22222, 0.22322, 0.22422, 0.22523, 0.22623, 0.22723, 0.22823, 0.22923, 0.23023, 0.23123, 0.23223, 0.23323, 0.23423, 0.23524, 0.23624, 0.23724, 0.23824, 0.23924,\n", " 0.24024, 0.24124, 0.24224, 0.24324, 0.24424, 0.24525, 0.24625, 0.24725, 0.24825, 0.24925, 0.25025, 0.25125, 0.25225, 0.25325, 0.25425, 0.25526, 0.25626, 0.25726, 0.25826, 0.25926, 0.26026, 0.26126, 0.26226, 0.26326,\n", " 0.26426, 0.26527, 0.26627, 0.26727, 0.26827, 0.26927, 0.27027, 0.27127, 0.27227, 0.27327, 0.27427, 0.27528, 0.27628, 0.27728, 0.27828, 0.27928, 0.28028, 0.28128, 0.28228, 0.28328, 0.28428, 0.28529, 0.28629, 0.28729,\n", " 0.28829, 0.28929, 0.29029, 0.29129, 0.29229, 0.29329, 0.29429, 0.2953, 0.2963, 0.2973, 0.2983, 0.2993, 0.3003, 0.3013, 0.3023, 0.3033, 0.3043, 0.30531, 0.30631, 0.30731, 0.30831, 0.30931, 0.31031, 0.31131,\n", " 0.31231, 0.31331, 0.31431, 0.31532, 0.31632, 0.31732, 0.31832, 0.31932, 0.32032, 0.32132, 0.32232, 0.32332, 0.32432, 0.32533, 0.32633, 0.32733, 0.32833, 0.32933, 0.33033, 0.33133, 0.33233, 0.33333, 0.33433, 0.33534,\n", " 0.33634, 0.33734, 0.33834, 0.33934, 0.34034, 0.34134, 0.34234, 0.34334, 0.34434, 0.34535, 0.34635, 0.34735, 0.34835, 0.34935, 0.35035, 0.35135, 0.35235, 0.35335, 0.35435, 0.35536, 0.35636, 0.35736, 0.35836, 0.35936,\n", " 0.36036, 0.36136, 0.36236, 0.36336, 0.36436, 0.36537, 0.36637, 0.36737, 0.36837, 0.36937, 0.37037, 0.37137, 0.37237, 0.37337, 0.37437, 0.37538, 0.37638, 0.37738, 0.37838, 0.37938, 0.38038, 0.38138, 0.38238, 0.38338,\n", " 0.38438, 0.38539, 0.38639, 0.38739, 0.38839, 0.38939, 0.39039, 0.39139, 0.39239, 0.39339, 0.39439, 0.3954, 0.3964, 0.3974, 0.3984, 0.3994, 0.4004, 0.4014, 0.4024, 0.4034, 0.4044, 0.40541, 0.40641, 0.40741,\n", " 0.40841, 0.40941, 0.41041, 0.41141, 0.41241, 0.41341, 0.41441, 0.41542, 0.41642, 0.41742, 0.41842, 0.41942, 0.42042, 0.42142, 0.42242, 0.42342, 0.42442, 0.42543, 0.42643, 0.42743, 0.42843, 0.42943, 0.43043, 0.43143,\n", " 0.43243, 0.43343, 0.43443, 0.43544, 0.43644, 0.43744, 0.43844, 0.43944, 0.44044, 0.44144, 0.44244, 0.44344, 0.44444, 0.44545, 0.44645, 0.44745, 0.44845, 0.44945, 0.45045, 0.45145, 0.45245, 0.45345, 0.45445, 0.45546,\n", " 0.45646, 0.45746, 0.45846, 0.45946, 0.46046, 0.46146, 0.46246, 0.46346, 0.46446, 0.46547, 0.46647, 0.46747, 0.46847, 0.46947, 0.47047, 0.47147, 0.47247, 0.47347, 0.47447, 0.47548, 0.47648, 0.47748, 0.47848, 0.47948,\n", " 0.48048, 0.48148, 0.48248, 0.48348, 0.48448, 0.48549, 0.48649, 0.48749, 0.48849, 0.48949, 0.49049, 0.49149, 0.49249, 0.49349, 0.49449, 0.4955, 0.4965, 0.4975, 0.4985, 0.4995, 0.5005, 0.5015, 0.5025, 0.5035,\n", " 0.5045, 0.50551, 0.50651, 0.50751, 0.50851, 0.50951, 0.51051, 0.51151, 0.51251, 0.51351, 0.51451, 0.51552, 0.51652, 0.51752, 0.51852, 0.51952, 0.52052, 0.52152, 0.52252, 0.52352, 0.52452, 0.52553, 0.52653, 0.52753,\n", " 0.52853, 0.52953, 0.53053, 0.53153, 0.53253, 0.53353, 0.53453, 0.53554, 0.53654, 0.53754, 0.53854, 0.53954, 0.54054, 0.54154, 0.54254, 0.54354, 0.54454, 0.54555, 0.54655, 0.54755, 0.54855, 0.54955, 0.55055, 0.55155,\n", " 0.55255, 0.55355, 0.55455, 0.55556, 0.55656, 0.55756, 0.55856, 0.55956, 0.56056, 0.56156, 0.56256, 0.56356, 0.56456, 0.56557, 0.56657, 0.56757, 0.56857, 0.56957, 0.57057, 0.57157, 0.57257, 0.57357, 0.57457, 0.57558,\n", " 0.57658, 0.57758, 0.57858, 0.57958, 0.58058, 0.58158, 0.58258, 0.58358, 0.58458, 0.58559, 0.58659, 0.58759, 0.58859, 0.58959, 0.59059, 0.59159, 0.59259, 0.59359, 0.59459, 0.5956, 0.5966, 0.5976, 0.5986, 0.5996,\n", " 0.6006, 0.6016, 0.6026, 0.6036, 0.6046, 0.60561, 0.60661, 0.60761, 0.60861, 0.60961, 0.61061, 0.61161, 0.61261, 0.61361, 0.61461, 0.61562, 0.61662, 0.61762, 0.61862, 0.61962, 0.62062, 0.62162, 0.62262, 0.62362,\n", " 0.62462, 0.62563, 0.62663, 0.62763, 0.62863, 0.62963, 0.63063, 0.63163, 0.63263, 0.63363, 0.63463, 0.63564, 0.63664, 0.63764, 0.63864, 0.63964, 0.64064, 0.64164, 0.64264, 0.64364, 0.64464, 0.64565, 0.64665, 0.64765,\n", " 0.64865, 0.64965, 0.65065, 0.65165, 0.65265, 0.65365, 0.65465, 0.65566, 0.65666, 0.65766, 0.65866, 0.65966, 0.66066, 0.66166, 0.66266, 0.66366, 0.66466, 0.66567, 0.66667, 0.66767, 0.66867, 0.66967, 0.67067, 0.67167,\n", " 0.67267, 0.67367, 0.67467, 0.67568, 0.67668, 0.67768, 0.67868, 0.67968, 0.68068, 0.68168, 0.68268, 0.68368, 0.68468, 0.68569, 0.68669, 0.68769, 0.68869, 0.68969, 0.69069, 0.69169, 0.69269, 0.69369, 0.69469, 0.6957,\n", " 0.6967, 0.6977, 0.6987, 0.6997, 0.7007, 0.7017, 0.7027, 0.7037, 0.7047, 0.70571, 0.70671, 0.70771, 0.70871, 0.70971, 0.71071, 0.71171, 0.71271, 0.71371, 0.71471, 0.71572, 0.71672, 0.71772, 0.71872, 0.71972,\n", " 0.72072, 0.72172, 0.72272, 0.72372, 0.72472, 0.72573, 0.72673, 0.72773, 0.72873, 0.72973, 0.73073, 0.73173, 0.73273, 0.73373, 0.73473, 0.73574, 0.73674, 0.73774, 0.73874, 0.73974, 0.74074, 0.74174, 0.74274, 0.74374,\n", " 0.74474, 0.74575, 0.74675, 0.74775, 0.74875, 0.74975, 0.75075, 0.75175, 0.75275, 0.75375, 0.75475, 0.75576, 0.75676, 0.75776, 0.75876, 0.75976, 0.76076, 0.76176, 0.76276, 0.76376, 0.76476, 0.76577, 0.76677, 0.76777,\n", " 0.76877, 0.76977, 0.77077, 0.77177, 0.77277, 0.77377, 0.77477, 0.77578, 0.77678, 0.77778, 0.77878, 0.77978, 0.78078, 0.78178, 0.78278, 0.78378, 0.78478, 0.78579, 0.78679, 0.78779, 0.78879, 0.78979, 0.79079, 0.79179,\n", " 0.79279, 0.79379, 0.79479, 0.7958, 0.7968, 0.7978, 0.7988, 0.7998, 0.8008, 0.8018, 0.8028, 0.8038, 0.8048, 0.80581, 0.80681, 0.80781, 0.80881, 0.80981, 0.81081, 0.81181, 0.81281, 0.81381, 0.81481, 0.81582,\n", " 0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.82583, 0.82683, 0.82783, 0.82883, 0.82983, 0.83083, 0.83183, 0.83283, 0.83383, 0.83483, 0.83584, 0.83684, 0.83784, 0.83884, 0.83984,\n", " 0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,\n", " 0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,\n", " 0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,\n", " 0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,\n", " 0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,\n", " 0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,\n", " 0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 1, 1, 1, ..., 0.00033483, 0.00016741, 0],\n", " [ 1, 1, 1, ..., 0.00052595, 0.00026298, 0],\n", " [ 1, 1, 1, ..., 8.1757e-05, 4.0879e-05, 0],\n", " ...,\n", " [ 1, 1, 1, ..., 0.0001582, 7.9101e-05, 0],\n", " [ 1, 1, 1, ..., 0.00019837, 9.9185e-05, 0],\n", " [ 1, 1, 1, ..., 0.00032012, 0.00016006, 0]]), 'Recall', 'Precision'], [array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,\n", " 0.024024, 0.025025, 0.026026, 0.027027, 0.028028, 0.029029, 0.03003, 0.031031, 0.032032, 0.033033, 0.034034, 0.035035, 0.036036, 0.037037, 0.038038, 0.039039, 0.04004, 0.041041, 0.042042, 0.043043, 0.044044, 0.045045, 0.046046, 0.047047,\n", " 0.048048, 0.049049, 0.05005, 0.051051, 0.052052, 0.053053, 0.054054, 0.055055, 0.056056, 0.057057, 0.058058, 0.059059, 0.06006, 0.061061, 0.062062, 0.063063, 0.064064, 0.065065, 0.066066, 0.067067, 0.068068, 0.069069, 0.07007, 0.071071,\n", " 0.072072, 0.073073, 0.074074, 0.075075, 0.076076, 0.077077, 0.078078, 0.079079, 0.08008, 0.081081, 0.082082, 0.083083, 0.084084, 0.085085, 0.086086, 0.087087, 0.088088, 0.089089, 0.09009, 0.091091, 0.092092, 0.093093, 0.094094, 0.095095,\n", " 0.096096, 0.097097, 0.098098, 0.099099, 0.1001, 0.1011, 0.1021, 0.1031, 0.1041, 0.10511, 0.10611, 0.10711, 0.10811, 0.10911, 0.11011, 0.11111, 0.11211, 0.11311, 0.11411, 0.11512, 0.11612, 0.11712, 0.11812, 0.11912,\n", " 0.12012, 0.12112, 0.12212, 0.12312, 0.12412, 0.12513, 0.12613, 0.12713, 0.12813, 0.12913, 0.13013, 0.13113, 0.13213, 0.13313, 0.13413, 0.13514, 0.13614, 0.13714, 0.13814, 0.13914, 0.14014, 0.14114, 0.14214, 0.14314,\n", " 0.14414, 0.14515, 0.14615, 0.14715, 0.14815, 0.14915, 0.15015, 0.15115, 0.15215, 0.15315, 0.15415, 0.15516, 0.15616, 0.15716, 0.15816, 0.15916, 0.16016, 0.16116, 0.16216, 0.16316, 0.16416, 0.16517, 0.16617, 0.16717,\n", " 0.16817, 0.16917, 0.17017, 0.17117, 0.17217, 0.17317, 0.17417, 0.17518, 0.17618, 0.17718, 0.17818, 0.17918, 0.18018, 0.18118, 0.18218, 0.18318, 0.18418, 0.18519, 0.18619, 0.18719, 0.18819, 0.18919, 0.19019, 0.19119,\n", " 0.19219, 0.19319, 0.19419, 0.1952, 0.1962, 0.1972, 0.1982, 0.1992, 0.2002, 0.2012, 0.2022, 0.2032, 0.2042, 0.20521, 0.20621, 0.20721, 0.20821, 0.20921, 0.21021, 0.21121, 0.21221, 0.21321, 0.21421, 0.21522,\n", " 0.21622, 0.21722, 0.21822, 0.21922, 0.22022, 0.22122, 0.22222, 0.22322, 0.22422, 0.22523, 0.22623, 0.22723, 0.22823, 0.22923, 0.23023, 0.23123, 0.23223, 0.23323, 0.23423, 0.23524, 0.23624, 0.23724, 0.23824, 0.23924,\n", " 0.24024, 0.24124, 0.24224, 0.24324, 0.24424, 0.24525, 0.24625, 0.24725, 0.24825, 0.24925, 0.25025, 0.25125, 0.25225, 0.25325, 0.25425, 0.25526, 0.25626, 0.25726, 0.25826, 0.25926, 0.26026, 0.26126, 0.26226, 0.26326,\n", " 0.26426, 0.26527, 0.26627, 0.26727, 0.26827, 0.26927, 0.27027, 0.27127, 0.27227, 0.27327, 0.27427, 0.27528, 0.27628, 0.27728, 0.27828, 0.27928, 0.28028, 0.28128, 0.28228, 0.28328, 0.28428, 0.28529, 0.28629, 0.28729,\n", " 0.28829, 0.28929, 0.29029, 0.29129, 0.29229, 0.29329, 0.29429, 0.2953, 0.2963, 0.2973, 0.2983, 0.2993, 0.3003, 0.3013, 0.3023, 0.3033, 0.3043, 0.30531, 0.30631, 0.30731, 0.30831, 0.30931, 0.31031, 0.31131,\n", " 0.31231, 0.31331, 0.31431, 0.31532, 0.31632, 0.31732, 0.31832, 0.31932, 0.32032, 0.32132, 0.32232, 0.32332, 0.32432, 0.32533, 0.32633, 0.32733, 0.32833, 0.32933, 0.33033, 0.33133, 0.33233, 0.33333, 0.33433, 0.33534,\n", " 0.33634, 0.33734, 0.33834, 0.33934, 0.34034, 0.34134, 0.34234, 0.34334, 0.34434, 0.34535, 0.34635, 0.34735, 0.34835, 0.34935, 0.35035, 0.35135, 0.35235, 0.35335, 0.35435, 0.35536, 0.35636, 0.35736, 0.35836, 0.35936,\n", " 0.36036, 0.36136, 0.36236, 0.36336, 0.36436, 0.36537, 0.36637, 0.36737, 0.36837, 0.36937, 0.37037, 0.37137, 0.37237, 0.37337, 0.37437, 0.37538, 0.37638, 0.37738, 0.37838, 0.37938, 0.38038, 0.38138, 0.38238, 0.38338,\n", " 0.38438, 0.38539, 0.38639, 0.38739, 0.38839, 0.38939, 0.39039, 0.39139, 0.39239, 0.39339, 0.39439, 0.3954, 0.3964, 0.3974, 0.3984, 0.3994, 0.4004, 0.4014, 0.4024, 0.4034, 0.4044, 0.40541, 0.40641, 0.40741,\n", " 0.40841, 0.40941, 0.41041, 0.41141, 0.41241, 0.41341, 0.41441, 0.41542, 0.41642, 0.41742, 0.41842, 0.41942, 0.42042, 0.42142, 0.42242, 0.42342, 0.42442, 0.42543, 0.42643, 0.42743, 0.42843, 0.42943, 0.43043, 0.43143,\n", " 0.43243, 0.43343, 0.43443, 0.43544, 0.43644, 0.43744, 0.43844, 0.43944, 0.44044, 0.44144, 0.44244, 0.44344, 0.44444, 0.44545, 0.44645, 0.44745, 0.44845, 0.44945, 0.45045, 0.45145, 0.45245, 0.45345, 0.45445, 0.45546,\n", " 0.45646, 0.45746, 0.45846, 0.45946, 0.46046, 0.46146, 0.46246, 0.46346, 0.46446, 0.46547, 0.46647, 0.46747, 0.46847, 0.46947, 0.47047, 0.47147, 0.47247, 0.47347, 0.47447, 0.47548, 0.47648, 0.47748, 0.47848, 0.47948,\n", " 0.48048, 0.48148, 0.48248, 0.48348, 0.48448, 0.48549, 0.48649, 0.48749, 0.48849, 0.48949, 0.49049, 0.49149, 0.49249, 0.49349, 0.49449, 0.4955, 0.4965, 0.4975, 0.4985, 0.4995, 0.5005, 0.5015, 0.5025, 0.5035,\n", " 0.5045, 0.50551, 0.50651, 0.50751, 0.50851, 0.50951, 0.51051, 0.51151, 0.51251, 0.51351, 0.51451, 0.51552, 0.51652, 0.51752, 0.51852, 0.51952, 0.52052, 0.52152, 0.52252, 0.52352, 0.52452, 0.52553, 0.52653, 0.52753,\n", " 0.52853, 0.52953, 0.53053, 0.53153, 0.53253, 0.53353, 0.53453, 0.53554, 0.53654, 0.53754, 0.53854, 0.53954, 0.54054, 0.54154, 0.54254, 0.54354, 0.54454, 0.54555, 0.54655, 0.54755, 0.54855, 0.54955, 0.55055, 0.55155,\n", " 0.55255, 0.55355, 0.55455, 0.55556, 0.55656, 0.55756, 0.55856, 0.55956, 0.56056, 0.56156, 0.56256, 0.56356, 0.56456, 0.56557, 0.56657, 0.56757, 0.56857, 0.56957, 0.57057, 0.57157, 0.57257, 0.57357, 0.57457, 0.57558,\n", " 0.57658, 0.57758, 0.57858, 0.57958, 0.58058, 0.58158, 0.58258, 0.58358, 0.58458, 0.58559, 0.58659, 0.58759, 0.58859, 0.58959, 0.59059, 0.59159, 0.59259, 0.59359, 0.59459, 0.5956, 0.5966, 0.5976, 0.5986, 0.5996,\n", " 0.6006, 0.6016, 0.6026, 0.6036, 0.6046, 0.60561, 0.60661, 0.60761, 0.60861, 0.60961, 0.61061, 0.61161, 0.61261, 0.61361, 0.61461, 0.61562, 0.61662, 0.61762, 0.61862, 0.61962, 0.62062, 0.62162, 0.62262, 0.62362,\n", " 0.62462, 0.62563, 0.62663, 0.62763, 0.62863, 0.62963, 0.63063, 0.63163, 0.63263, 0.63363, 0.63463, 0.63564, 0.63664, 0.63764, 0.63864, 0.63964, 0.64064, 0.64164, 0.64264, 0.64364, 0.64464, 0.64565, 0.64665, 0.64765,\n", " 0.64865, 0.64965, 0.65065, 0.65165, 0.65265, 0.65365, 0.65465, 0.65566, 0.65666, 0.65766, 0.65866, 0.65966, 0.66066, 0.66166, 0.66266, 0.66366, 0.66466, 0.66567, 0.66667, 0.66767, 0.66867, 0.66967, 0.67067, 0.67167,\n", " 0.67267, 0.67367, 0.67467, 0.67568, 0.67668, 0.67768, 0.67868, 0.67968, 0.68068, 0.68168, 0.68268, 0.68368, 0.68468, 0.68569, 0.68669, 0.68769, 0.68869, 0.68969, 0.69069, 0.69169, 0.69269, 0.69369, 0.69469, 0.6957,\n", " 0.6967, 0.6977, 0.6987, 0.6997, 0.7007, 0.7017, 0.7027, 0.7037, 0.7047, 0.70571, 0.70671, 0.70771, 0.70871, 0.70971, 0.71071, 0.71171, 0.71271, 0.71371, 0.71471, 0.71572, 0.71672, 0.71772, 0.71872, 0.71972,\n", " 0.72072, 0.72172, 0.72272, 0.72372, 0.72472, 0.72573, 0.72673, 0.72773, 0.72873, 0.72973, 0.73073, 0.73173, 0.73273, 0.73373, 0.73473, 0.73574, 0.73674, 0.73774, 0.73874, 0.73974, 0.74074, 0.74174, 0.74274, 0.74374,\n", " 0.74474, 0.74575, 0.74675, 0.74775, 0.74875, 0.74975, 0.75075, 0.75175, 0.75275, 0.75375, 0.75475, 0.75576, 0.75676, 0.75776, 0.75876, 0.75976, 0.76076, 0.76176, 0.76276, 0.76376, 0.76476, 0.76577, 0.76677, 0.76777,\n", " 0.76877, 0.76977, 0.77077, 0.77177, 0.77277, 0.77377, 0.77477, 0.77578, 0.77678, 0.77778, 0.77878, 0.77978, 0.78078, 0.78178, 0.78278, 0.78378, 0.78478, 0.78579, 0.78679, 0.78779, 0.78879, 0.78979, 0.79079, 0.79179,\n", " 0.79279, 0.79379, 0.79479, 0.7958, 0.7968, 0.7978, 0.7988, 0.7998, 0.8008, 0.8018, 0.8028, 0.8038, 0.8048, 0.80581, 0.80681, 0.80781, 0.80881, 0.80981, 0.81081, 0.81181, 0.81281, 0.81381, 0.81481, 0.81582,\n", " 0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.82583, 0.82683, 0.82783, 0.82883, 0.82983, 0.83083, 0.83183, 0.83283, 0.83383, 0.83483, 0.83584, 0.83684, 0.83784, 0.83884, 0.83984,\n", " 0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,\n", " 0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,\n", " 0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,\n", " 0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,\n", " 0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,\n", " 0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,\n", " 0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 0.045984, 0.046018, 0.070846, ..., 0, 0, 0],\n", " [ 0.048868, 0.048868, 0.075985, ..., 0, 0, 0],\n", " [ 0.024813, 0.024818, 0.041434, ..., 0, 0, 0],\n", " ...,\n", " [ 0.020157, 0.020174, 0.040265, ..., 0, 0, 0],\n", " [ 0.024056, 0.024066, 0.044068, ..., 0, 0, 0],\n", " [ 0.044499, 0.044509, 0.0908, ..., 0, 0, 0]]), 'Confidence', 'F1'], [array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,\n", " 0.024024, 0.025025, 0.026026, 0.027027, 0.028028, 0.029029, 0.03003, 0.031031, 0.032032, 0.033033, 0.034034, 0.035035, 0.036036, 0.037037, 0.038038, 0.039039, 0.04004, 0.041041, 0.042042, 0.043043, 0.044044, 0.045045, 0.046046, 0.047047,\n", " 0.048048, 0.049049, 0.05005, 0.051051, 0.052052, 0.053053, 0.054054, 0.055055, 0.056056, 0.057057, 0.058058, 0.059059, 0.06006, 0.061061, 0.062062, 0.063063, 0.064064, 0.065065, 0.066066, 0.067067, 0.068068, 0.069069, 0.07007, 0.071071,\n", " 0.072072, 0.073073, 0.074074, 0.075075, 0.076076, 0.077077, 0.078078, 0.079079, 0.08008, 0.081081, 0.082082, 0.083083, 0.084084, 0.085085, 0.086086, 0.087087, 0.088088, 0.089089, 0.09009, 0.091091, 0.092092, 0.093093, 0.094094, 0.095095,\n", " 0.096096, 0.097097, 0.098098, 0.099099, 0.1001, 0.1011, 0.1021, 0.1031, 0.1041, 0.10511, 0.10611, 0.10711, 0.10811, 0.10911, 0.11011, 0.11111, 0.11211, 0.11311, 0.11411, 0.11512, 0.11612, 0.11712, 0.11812, 0.11912,\n", " 0.12012, 0.12112, 0.12212, 0.12312, 0.12412, 0.12513, 0.12613, 0.12713, 0.12813, 0.12913, 0.13013, 0.13113, 0.13213, 0.13313, 0.13413, 0.13514, 0.13614, 0.13714, 0.13814, 0.13914, 0.14014, 0.14114, 0.14214, 0.14314,\n", " 0.14414, 0.14515, 0.14615, 0.14715, 0.14815, 0.14915, 0.15015, 0.15115, 0.15215, 0.15315, 0.15415, 0.15516, 0.15616, 0.15716, 0.15816, 0.15916, 0.16016, 0.16116, 0.16216, 0.16316, 0.16416, 0.16517, 0.16617, 0.16717,\n", " 0.16817, 0.16917, 0.17017, 0.17117, 0.17217, 0.17317, 0.17417, 0.17518, 0.17618, 0.17718, 0.17818, 0.17918, 0.18018, 0.18118, 0.18218, 0.18318, 0.18418, 0.18519, 0.18619, 0.18719, 0.18819, 0.18919, 0.19019, 0.19119,\n", " 0.19219, 0.19319, 0.19419, 0.1952, 0.1962, 0.1972, 0.1982, 0.1992, 0.2002, 0.2012, 0.2022, 0.2032, 0.2042, 0.20521, 0.20621, 0.20721, 0.20821, 0.20921, 0.21021, 0.21121, 0.21221, 0.21321, 0.21421, 0.21522,\n", " 0.21622, 0.21722, 0.21822, 0.21922, 0.22022, 0.22122, 0.22222, 0.22322, 0.22422, 0.22523, 0.22623, 0.22723, 0.22823, 0.22923, 0.23023, 0.23123, 0.23223, 0.23323, 0.23423, 0.23524, 0.23624, 0.23724, 0.23824, 0.23924,\n", " 0.24024, 0.24124, 0.24224, 0.24324, 0.24424, 0.24525, 0.24625, 0.24725, 0.24825, 0.24925, 0.25025, 0.25125, 0.25225, 0.25325, 0.25425, 0.25526, 0.25626, 0.25726, 0.25826, 0.25926, 0.26026, 0.26126, 0.26226, 0.26326,\n", " 0.26426, 0.26527, 0.26627, 0.26727, 0.26827, 0.26927, 0.27027, 0.27127, 0.27227, 0.27327, 0.27427, 0.27528, 0.27628, 0.27728, 0.27828, 0.27928, 0.28028, 0.28128, 0.28228, 0.28328, 0.28428, 0.28529, 0.28629, 0.28729,\n", " 0.28829, 0.28929, 0.29029, 0.29129, 0.29229, 0.29329, 0.29429, 0.2953, 0.2963, 0.2973, 0.2983, 0.2993, 0.3003, 0.3013, 0.3023, 0.3033, 0.3043, 0.30531, 0.30631, 0.30731, 0.30831, 0.30931, 0.31031, 0.31131,\n", " 0.31231, 0.31331, 0.31431, 0.31532, 0.31632, 0.31732, 0.31832, 0.31932, 0.32032, 0.32132, 0.32232, 0.32332, 0.32432, 0.32533, 0.32633, 0.32733, 0.32833, 0.32933, 0.33033, 0.33133, 0.33233, 0.33333, 0.33433, 0.33534,\n", " 0.33634, 0.33734, 0.33834, 0.33934, 0.34034, 0.34134, 0.34234, 0.34334, 0.34434, 0.34535, 0.34635, 0.34735, 0.34835, 0.34935, 0.35035, 0.35135, 0.35235, 0.35335, 0.35435, 0.35536, 0.35636, 0.35736, 0.35836, 0.35936,\n", " 0.36036, 0.36136, 0.36236, 0.36336, 0.36436, 0.36537, 0.36637, 0.36737, 0.36837, 0.36937, 0.37037, 0.37137, 0.37237, 0.37337, 0.37437, 0.37538, 0.37638, 0.37738, 0.37838, 0.37938, 0.38038, 0.38138, 0.38238, 0.38338,\n", " 0.38438, 0.38539, 0.38639, 0.38739, 0.38839, 0.38939, 0.39039, 0.39139, 0.39239, 0.39339, 0.39439, 0.3954, 0.3964, 0.3974, 0.3984, 0.3994, 0.4004, 0.4014, 0.4024, 0.4034, 0.4044, 0.40541, 0.40641, 0.40741,\n", " 0.40841, 0.40941, 0.41041, 0.41141, 0.41241, 0.41341, 0.41441, 0.41542, 0.41642, 0.41742, 0.41842, 0.41942, 0.42042, 0.42142, 0.42242, 0.42342, 0.42442, 0.42543, 0.42643, 0.42743, 0.42843, 0.42943, 0.43043, 0.43143,\n", " 0.43243, 0.43343, 0.43443, 0.43544, 0.43644, 0.43744, 0.43844, 0.43944, 0.44044, 0.44144, 0.44244, 0.44344, 0.44444, 0.44545, 0.44645, 0.44745, 0.44845, 0.44945, 0.45045, 0.45145, 0.45245, 0.45345, 0.45445, 0.45546,\n", " 0.45646, 0.45746, 0.45846, 0.45946, 0.46046, 0.46146, 0.46246, 0.46346, 0.46446, 0.46547, 0.46647, 0.46747, 0.46847, 0.46947, 0.47047, 0.47147, 0.47247, 0.47347, 0.47447, 0.47548, 0.47648, 0.47748, 0.47848, 0.47948,\n", " 0.48048, 0.48148, 0.48248, 0.48348, 0.48448, 0.48549, 0.48649, 0.48749, 0.48849, 0.48949, 0.49049, 0.49149, 0.49249, 0.49349, 0.49449, 0.4955, 0.4965, 0.4975, 0.4985, 0.4995, 0.5005, 0.5015, 0.5025, 0.5035,\n", " 0.5045, 0.50551, 0.50651, 0.50751, 0.50851, 0.50951, 0.51051, 0.51151, 0.51251, 0.51351, 0.51451, 0.51552, 0.51652, 0.51752, 0.51852, 0.51952, 0.52052, 0.52152, 0.52252, 0.52352, 0.52452, 0.52553, 0.52653, 0.52753,\n", " 0.52853, 0.52953, 0.53053, 0.53153, 0.53253, 0.53353, 0.53453, 0.53554, 0.53654, 0.53754, 0.53854, 0.53954, 0.54054, 0.54154, 0.54254, 0.54354, 0.54454, 0.54555, 0.54655, 0.54755, 0.54855, 0.54955, 0.55055, 0.55155,\n", " 0.55255, 0.55355, 0.55455, 0.55556, 0.55656, 0.55756, 0.55856, 0.55956, 0.56056, 0.56156, 0.56256, 0.56356, 0.56456, 0.56557, 0.56657, 0.56757, 0.56857, 0.56957, 0.57057, 0.57157, 0.57257, 0.57357, 0.57457, 0.57558,\n", " 0.57658, 0.57758, 0.57858, 0.57958, 0.58058, 0.58158, 0.58258, 0.58358, 0.58458, 0.58559, 0.58659, 0.58759, 0.58859, 0.58959, 0.59059, 0.59159, 0.59259, 0.59359, 0.59459, 0.5956, 0.5966, 0.5976, 0.5986, 0.5996,\n", " 0.6006, 0.6016, 0.6026, 0.6036, 0.6046, 0.60561, 0.60661, 0.60761, 0.60861, 0.60961, 0.61061, 0.61161, 0.61261, 0.61361, 0.61461, 0.61562, 0.61662, 0.61762, 0.61862, 0.61962, 0.62062, 0.62162, 0.62262, 0.62362,\n", " 0.62462, 0.62563, 0.62663, 0.62763, 0.62863, 0.62963, 0.63063, 0.63163, 0.63263, 0.63363, 0.63463, 0.63564, 0.63664, 0.63764, 0.63864, 0.63964, 0.64064, 0.64164, 0.64264, 0.64364, 0.64464, 0.64565, 0.64665, 0.64765,\n", " 0.64865, 0.64965, 0.65065, 0.65165, 0.65265, 0.65365, 0.65465, 0.65566, 0.65666, 0.65766, 0.65866, 0.65966, 0.66066, 0.66166, 0.66266, 0.66366, 0.66466, 0.66567, 0.66667, 0.66767, 0.66867, 0.66967, 0.67067, 0.67167,\n", " 0.67267, 0.67367, 0.67467, 0.67568, 0.67668, 0.67768, 0.67868, 0.67968, 0.68068, 0.68168, 0.68268, 0.68368, 0.68468, 0.68569, 0.68669, 0.68769, 0.68869, 0.68969, 0.69069, 0.69169, 0.69269, 0.69369, 0.69469, 0.6957,\n", " 0.6967, 0.6977, 0.6987, 0.6997, 0.7007, 0.7017, 0.7027, 0.7037, 0.7047, 0.70571, 0.70671, 0.70771, 0.70871, 0.70971, 0.71071, 0.71171, 0.71271, 0.71371, 0.71471, 0.71572, 0.71672, 0.71772, 0.71872, 0.71972,\n", " 0.72072, 0.72172, 0.72272, 0.72372, 0.72472, 0.72573, 0.72673, 0.72773, 0.72873, 0.72973, 0.73073, 0.73173, 0.73273, 0.73373, 0.73473, 0.73574, 0.73674, 0.73774, 0.73874, 0.73974, 0.74074, 0.74174, 0.74274, 0.74374,\n", " 0.74474, 0.74575, 0.74675, 0.74775, 0.74875, 0.74975, 0.75075, 0.75175, 0.75275, 0.75375, 0.75475, 0.75576, 0.75676, 0.75776, 0.75876, 0.75976, 0.76076, 0.76176, 0.76276, 0.76376, 0.76476, 0.76577, 0.76677, 0.76777,\n", " 0.76877, 0.76977, 0.77077, 0.77177, 0.77277, 0.77377, 0.77477, 0.77578, 0.77678, 0.77778, 0.77878, 0.77978, 0.78078, 0.78178, 0.78278, 0.78378, 0.78478, 0.78579, 0.78679, 0.78779, 0.78879, 0.78979, 0.79079, 0.79179,\n", " 0.79279, 0.79379, 0.79479, 0.7958, 0.7968, 0.7978, 0.7988, 0.7998, 0.8008, 0.8018, 0.8028, 0.8038, 0.8048, 0.80581, 0.80681, 0.80781, 0.80881, 0.80981, 0.81081, 0.81181, 0.81281, 0.81381, 0.81481, 0.81582,\n", " 0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.82583, 0.82683, 0.82783, 0.82883, 0.82983, 0.83083, 0.83183, 0.83283, 0.83383, 0.83483, 0.83584, 0.83684, 0.83784, 0.83884, 0.83984,\n", " 0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,\n", " 0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,\n", " 0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,\n", " 0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,\n", " 0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,\n", " 0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,\n", " 0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 0.023624, 0.023642, 0.036947, ..., 1, 1, 1],\n", " [ 0.025112, 0.025112, 0.039717, ..., 1, 1, 1],\n", " [ 0.012633, 0.012636, 0.021358, ..., 1, 1, 1],\n", " ...,\n", " [ 0.010196, 0.010205, 0.020609, ..., 1, 1, 1],\n", " [ 0.012195, 0.0122, 0.022602, ..., 1, 1, 1],\n", " [ 0.022843, 0.022848, 0.04799, ..., 1, 1, 1]]), 'Confidence', 'Precision'], [array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,\n", " 0.024024, 0.025025, 0.026026, 0.027027, 0.028028, 0.029029, 0.03003, 0.031031, 0.032032, 0.033033, 0.034034, 0.035035, 0.036036, 0.037037, 0.038038, 0.039039, 0.04004, 0.041041, 0.042042, 0.043043, 0.044044, 0.045045, 0.046046, 0.047047,\n", " 0.048048, 0.049049, 0.05005, 0.051051, 0.052052, 0.053053, 0.054054, 0.055055, 0.056056, 0.057057, 0.058058, 0.059059, 0.06006, 0.061061, 0.062062, 0.063063, 0.064064, 0.065065, 0.066066, 0.067067, 0.068068, 0.069069, 0.07007, 0.071071,\n", " 0.072072, 0.073073, 0.074074, 0.075075, 0.076076, 0.077077, 0.078078, 0.079079, 0.08008, 0.081081, 0.082082, 0.083083, 0.084084, 0.085085, 0.086086, 0.087087, 0.088088, 0.089089, 0.09009, 0.091091, 0.092092, 0.093093, 0.094094, 0.095095,\n", " 0.096096, 0.097097, 0.098098, 0.099099, 0.1001, 0.1011, 0.1021, 0.1031, 0.1041, 0.10511, 0.10611, 0.10711, 0.10811, 0.10911, 0.11011, 0.11111, 0.11211, 0.11311, 0.11411, 0.11512, 0.11612, 0.11712, 0.11812, 0.11912,\n", " 0.12012, 0.12112, 0.12212, 0.12312, 0.12412, 0.12513, 0.12613, 0.12713, 0.12813, 0.12913, 0.13013, 0.13113, 0.13213, 0.13313, 0.13413, 0.13514, 0.13614, 0.13714, 0.13814, 0.13914, 0.14014, 0.14114, 0.14214, 0.14314,\n", " 0.14414, 0.14515, 0.14615, 0.14715, 0.14815, 0.14915, 0.15015, 0.15115, 0.15215, 0.15315, 0.15415, 0.15516, 0.15616, 0.15716, 0.15816, 0.15916, 0.16016, 0.16116, 0.16216, 0.16316, 0.16416, 0.16517, 0.16617, 0.16717,\n", " 0.16817, 0.16917, 0.17017, 0.17117, 0.17217, 0.17317, 0.17417, 0.17518, 0.17618, 0.17718, 0.17818, 0.17918, 0.18018, 0.18118, 0.18218, 0.18318, 0.18418, 0.18519, 0.18619, 0.18719, 0.18819, 0.18919, 0.19019, 0.19119,\n", " 0.19219, 0.19319, 0.19419, 0.1952, 0.1962, 0.1972, 0.1982, 0.1992, 0.2002, 0.2012, 0.2022, 0.2032, 0.2042, 0.20521, 0.20621, 0.20721, 0.20821, 0.20921, 0.21021, 0.21121, 0.21221, 0.21321, 0.21421, 0.21522,\n", " 0.21622, 0.21722, 0.21822, 0.21922, 0.22022, 0.22122, 0.22222, 0.22322, 0.22422, 0.22523, 0.22623, 0.22723, 0.22823, 0.22923, 0.23023, 0.23123, 0.23223, 0.23323, 0.23423, 0.23524, 0.23624, 0.23724, 0.23824, 0.23924,\n", " 0.24024, 0.24124, 0.24224, 0.24324, 0.24424, 0.24525, 0.24625, 0.24725, 0.24825, 0.24925, 0.25025, 0.25125, 0.25225, 0.25325, 0.25425, 0.25526, 0.25626, 0.25726, 0.25826, 0.25926, 0.26026, 0.26126, 0.26226, 0.26326,\n", " 0.26426, 0.26527, 0.26627, 0.26727, 0.26827, 0.26927, 0.27027, 0.27127, 0.27227, 0.27327, 0.27427, 0.27528, 0.27628, 0.27728, 0.27828, 0.27928, 0.28028, 0.28128, 0.28228, 0.28328, 0.28428, 0.28529, 0.28629, 0.28729,\n", " 0.28829, 0.28929, 0.29029, 0.29129, 0.29229, 0.29329, 0.29429, 0.2953, 0.2963, 0.2973, 0.2983, 0.2993, 0.3003, 0.3013, 0.3023, 0.3033, 0.3043, 0.30531, 0.30631, 0.30731, 0.30831, 0.30931, 0.31031, 0.31131,\n", " 0.31231, 0.31331, 0.31431, 0.31532, 0.31632, 0.31732, 0.31832, 0.31932, 0.32032, 0.32132, 0.32232, 0.32332, 0.32432, 0.32533, 0.32633, 0.32733, 0.32833, 0.32933, 0.33033, 0.33133, 0.33233, 0.33333, 0.33433, 0.33534,\n", " 0.33634, 0.33734, 0.33834, 0.33934, 0.34034, 0.34134, 0.34234, 0.34334, 0.34434, 0.34535, 0.34635, 0.34735, 0.34835, 0.34935, 0.35035, 0.35135, 0.35235, 0.35335, 0.35435, 0.35536, 0.35636, 0.35736, 0.35836, 0.35936,\n", " 0.36036, 0.36136, 0.36236, 0.36336, 0.36436, 0.36537, 0.36637, 0.36737, 0.36837, 0.36937, 0.37037, 0.37137, 0.37237, 0.37337, 0.37437, 0.37538, 0.37638, 0.37738, 0.37838, 0.37938, 0.38038, 0.38138, 0.38238, 0.38338,\n", " 0.38438, 0.38539, 0.38639, 0.38739, 0.38839, 0.38939, 0.39039, 0.39139, 0.39239, 0.39339, 0.39439, 0.3954, 0.3964, 0.3974, 0.3984, 0.3994, 0.4004, 0.4014, 0.4024, 0.4034, 0.4044, 0.40541, 0.40641, 0.40741,\n", " 0.40841, 0.40941, 0.41041, 0.41141, 0.41241, 0.41341, 0.41441, 0.41542, 0.41642, 0.41742, 0.41842, 0.41942, 0.42042, 0.42142, 0.42242, 0.42342, 0.42442, 0.42543, 0.42643, 0.42743, 0.42843, 0.42943, 0.43043, 0.43143,\n", " 0.43243, 0.43343, 0.43443, 0.43544, 0.43644, 0.43744, 0.43844, 0.43944, 0.44044, 0.44144, 0.44244, 0.44344, 0.44444, 0.44545, 0.44645, 0.44745, 0.44845, 0.44945, 0.45045, 0.45145, 0.45245, 0.45345, 0.45445, 0.45546,\n", " 0.45646, 0.45746, 0.45846, 0.45946, 0.46046, 0.46146, 0.46246, 0.46346, 0.46446, 0.46547, 0.46647, 0.46747, 0.46847, 0.46947, 0.47047, 0.47147, 0.47247, 0.47347, 0.47447, 0.47548, 0.47648, 0.47748, 0.47848, 0.47948,\n", " 0.48048, 0.48148, 0.48248, 0.48348, 0.48448, 0.48549, 0.48649, 0.48749, 0.48849, 0.48949, 0.49049, 0.49149, 0.49249, 0.49349, 0.49449, 0.4955, 0.4965, 0.4975, 0.4985, 0.4995, 0.5005, 0.5015, 0.5025, 0.5035,\n", " 0.5045, 0.50551, 0.50651, 0.50751, 0.50851, 0.50951, 0.51051, 0.51151, 0.51251, 0.51351, 0.51451, 0.51552, 0.51652, 0.51752, 0.51852, 0.51952, 0.52052, 0.52152, 0.52252, 0.52352, 0.52452, 0.52553, 0.52653, 0.52753,\n", " 0.52853, 0.52953, 0.53053, 0.53153, 0.53253, 0.53353, 0.53453, 0.53554, 0.53654, 0.53754, 0.53854, 0.53954, 0.54054, 0.54154, 0.54254, 0.54354, 0.54454, 0.54555, 0.54655, 0.54755, 0.54855, 0.54955, 0.55055, 0.55155,\n", " 0.55255, 0.55355, 0.55455, 0.55556, 0.55656, 0.55756, 0.55856, 0.55956, 0.56056, 0.56156, 0.56256, 0.56356, 0.56456, 0.56557, 0.56657, 0.56757, 0.56857, 0.56957, 0.57057, 0.57157, 0.57257, 0.57357, 0.57457, 0.57558,\n", " 0.57658, 0.57758, 0.57858, 0.57958, 0.58058, 0.58158, 0.58258, 0.58358, 0.58458, 0.58559, 0.58659, 0.58759, 0.58859, 0.58959, 0.59059, 0.59159, 0.59259, 0.59359, 0.59459, 0.5956, 0.5966, 0.5976, 0.5986, 0.5996,\n", " 0.6006, 0.6016, 0.6026, 0.6036, 0.6046, 0.60561, 0.60661, 0.60761, 0.60861, 0.60961, 0.61061, 0.61161, 0.61261, 0.61361, 0.61461, 0.61562, 0.61662, 0.61762, 0.61862, 0.61962, 0.62062, 0.62162, 0.62262, 0.62362,\n", " 0.62462, 0.62563, 0.62663, 0.62763, 0.62863, 0.62963, 0.63063, 0.63163, 0.63263, 0.63363, 0.63463, 0.63564, 0.63664, 0.63764, 0.63864, 0.63964, 0.64064, 0.64164, 0.64264, 0.64364, 0.64464, 0.64565, 0.64665, 0.64765,\n", " 0.64865, 0.64965, 0.65065, 0.65165, 0.65265, 0.65365, 0.65465, 0.65566, 0.65666, 0.65766, 0.65866, 0.65966, 0.66066, 0.66166, 0.66266, 0.66366, 0.66466, 0.66567, 0.66667, 0.66767, 0.66867, 0.66967, 0.67067, 0.67167,\n", " 0.67267, 0.67367, 0.67467, 0.67568, 0.67668, 0.67768, 0.67868, 0.67968, 0.68068, 0.68168, 0.68268, 0.68368, 0.68468, 0.68569, 0.68669, 0.68769, 0.68869, 0.68969, 0.69069, 0.69169, 0.69269, 0.69369, 0.69469, 0.6957,\n", " 0.6967, 0.6977, 0.6987, 0.6997, 0.7007, 0.7017, 0.7027, 0.7037, 0.7047, 0.70571, 0.70671, 0.70771, 0.70871, 0.70971, 0.71071, 0.71171, 0.71271, 0.71371, 0.71471, 0.71572, 0.71672, 0.71772, 0.71872, 0.71972,\n", " 0.72072, 0.72172, 0.72272, 0.72372, 0.72472, 0.72573, 0.72673, 0.72773, 0.72873, 0.72973, 0.73073, 0.73173, 0.73273, 0.73373, 0.73473, 0.73574, 0.73674, 0.73774, 0.73874, 0.73974, 0.74074, 0.74174, 0.74274, 0.74374,\n", " 0.74474, 0.74575, 0.74675, 0.74775, 0.74875, 0.74975, 0.75075, 0.75175, 0.75275, 0.75375, 0.75475, 0.75576, 0.75676, 0.75776, 0.75876, 0.75976, 0.76076, 0.76176, 0.76276, 0.76376, 0.76476, 0.76577, 0.76677, 0.76777,\n", " 0.76877, 0.76977, 0.77077, 0.77177, 0.77277, 0.77377, 0.77477, 0.77578, 0.77678, 0.77778, 0.77878, 0.77978, 0.78078, 0.78178, 0.78278, 0.78378, 0.78478, 0.78579, 0.78679, 0.78779, 0.78879, 0.78979, 0.79079, 0.79179,\n", " 0.79279, 0.79379, 0.79479, 0.7958, 0.7968, 0.7978, 0.7988, 0.7998, 0.8008, 0.8018, 0.8028, 0.8038, 0.8048, 0.80581, 0.80681, 0.80781, 0.80881, 0.80981, 0.81081, 0.81181, 0.81281, 0.81381, 0.81481, 0.81582,\n", " 0.81682, 0.81782, 0.81882, 0.81982, 0.82082, 0.82182, 0.82282, 0.82382, 0.82482, 0.82583, 0.82683, 0.82783, 0.82883, 0.82983, 0.83083, 0.83183, 0.83283, 0.83383, 0.83483, 0.83584, 0.83684, 0.83784, 0.83884, 0.83984,\n", " 0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,\n", " 0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,\n", " 0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,\n", " 0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,\n", " 0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,\n", " 0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,\n", " 0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 0.85874, 0.85874, 0.85874, ..., 0, 0, 0],\n", " [ 0.90441, 0.90441, 0.875, ..., 0, 0, 0],\n", " [ 0.69065, 0.69065, 0.69065, ..., 0, 0, 0],\n", " ...,\n", " [ 0.87097, 0.87097, 0.87097, ..., 0, 0, 0],\n", " [ 0.87692, 0.87692, 0.87692, ..., 0, 0, 0],\n", " [ 0.85714, 0.85714, 0.84127, ..., 0, 0, 0]]), 'Confidence', 'Recall']]\n", "fitness: np.float64(0.26894067250585574)\n", "keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']\n", "maps: array([ 0.20204, 0.38865, 0.21698, 0.28202, 0.36656, 0.3661, 0.27487, 0.27089, 0.24926, 0.22657, 0.16712, 0.27784, 0.3627, 0.32636, 0.28093, 0.21054, 0.30468, 0.17938, 0.16914, 0.2089, 0.45514, 0.24997, 0.010293, 0.35029,\n", " 0.25787, 0.33736])\n", "names: {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'traffic light', 9: 'stop sign', 10: 'bench', 11: 'bird', 12: 'cat', 13: 'dog', 14: 'horse', 15: 'cow', 16: 'elephant', 17: 'bottle', 18: 'cup', 19: 'bowl', 20: 'pizza', 21: 'cake', 22: 'chair', 23: 'couch', 24: 'potted plant', 25: 'bed'}\n", "nt_per_class: array([446, 136, 139, 28, 88, 42, 42, 36, 39, 38, 47, 112, 73, 152, 48, 52, 43, 44, 28, 49, 39, 10, 40, 31, 65, 63])\n", "nt_per_image: array([153, 23, 55, 21, 32, 28, 32, 27, 15, 36, 28, 26, 60, 48, 20, 22, 28, 25, 23, 34, 31, 10, 27, 31, 18, 55])\n", "results_dict: {'metrics/precision(B)': 0.6900182718968334, 'metrics/recall(B)': 0.4975548496689023, 'metrics/mAP50(B)': 0.5497688331337283, 'metrics/mAP50-95(B)': 0.26894067250585574, 'fitness': 0.26894067250585574}\n", "save_dir: PosixPath('/content/runs/detect/val')\n", "speed: {'preprocess': 1.3960578011389022, 'inference': 170.99571628296832, 'loss': 9.133463683600466e-05, 'postprocess': 2.7864586845193684}\n", "stats: {'tp': [], 'conf': [], 'pred_cls': [], 'target_cls': [], 'target_img': []}\n", "task: 'detect'\n" ] } ], "source": [ "metrics = model.val()\n", "print(metrics)" ] }, { "cell_type": "markdown", "id": "NOqibmwk9FEr", "metadata": { "id": "NOqibmwk9FEr" }, "source": [ "Test on Sample Image" ] }, { "cell_type": "code", "execution_count": 3, "id": "pzrqWHpo9CJL", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pzrqWHpo9CJL", "outputId": "fc0ba8cf-60a0-4a4c-de0c-34a0bc52ac3e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "WARNING ⚠️ \n", "Inference results will accumulate in RAM unless `stream=True` is passed, which can cause out-of-memory errors for large\n", "sources or long-running streams and videos. See https://docs.ultralytics.com/modes/predict/ for help.\n", "\n", "Example:\n", " results = model(source=..., stream=True) # generator of Results objects\n", " for r in results:\n", " boxes = r.boxes # Boxes object for bbox outputs\n", " masks = r.masks # Masks object for segment masks outputs\n", " probs = r.probs # Class probabilities for classification outputs\n", "\n", "image 1/2210 /content/smartvision_dataset/detection/images/image_000000.jpg: 640x512 1 person, 190.7ms\n", "image 2/2210 /content/smartvision_dataset/detection/images/image_000001.jpg: 640x512 (no detections), 170.6ms\n", "image 3/2210 /content/smartvision_dataset/detection/images/image_000002.jpg: 640x512 (no detections), 172.7ms\n", "image 4/2210 /content/smartvision_dataset/detection/images/image_000003.jpg: 640x512 (no detections), 168.5ms\n", "image 5/2210 /content/smartvision_dataset/detection/images/image_000004.jpg: 640x512 (no detections), 172.1ms\n", "image 6/2210 /content/smartvision_dataset/detection/images/image_000005.jpg: 640x512 (no detections), 174.6ms\n", "image 7/2210 /content/smartvision_dataset/detection/images/image_000006.jpg: 640x512 (no detections), 194.1ms\n", "image 8/2210 /content/smartvision_dataset/detection/images/image_000007.jpg: 512x640 2 persons, 2 elephants, 191.1ms\n", "image 9/2210 /content/smartvision_dataset/detection/images/image_000008.jpg: 512x640 2 persons, 2 elephants, 175.1ms\n", "image 10/2210 /content/smartvision_dataset/detection/images/image_000009.jpg: 512x640 2 persons, 2 elephants, 193.4ms\n", "image 11/2210 /content/smartvision_dataset/detection/images/image_000010.jpg: 448x640 2 persons, 169.1ms\n", "image 12/2210 /content/smartvision_dataset/detection/images/image_000011.jpg: 448x640 2 persons, 169.5ms\n", "image 13/2210 /content/smartvision_dataset/detection/images/image_000012.jpg: 448x640 2 persons, 145.7ms\n", "image 14/2210 /content/smartvision_dataset/detection/images/image_000013.jpg: 448x640 2 persons, 147.7ms\n", "image 15/2210 /content/smartvision_dataset/detection/images/image_000014.jpg: 448x640 2 persons, 147.0ms\n", "image 16/2210 /content/smartvision_dataset/detection/images/image_000015.jpg: 448x640 2 persons, 148.2ms\n", "image 17/2210 /content/smartvision_dataset/detection/images/image_000016.jpg: 480x640 3 persons, 175.9ms\n", "image 18/2210 /content/smartvision_dataset/detection/images/image_000017.jpg: 480x640 3 persons, 175.4ms\n", "image 19/2210 /content/smartvision_dataset/detection/images/image_000018.jpg: 480x640 3 persons, 163.6ms\n", "image 20/2210 /content/smartvision_dataset/detection/images/image_000019.jpg: 480x640 3 persons, 155.0ms\n", "image 21/2210 /content/smartvision_dataset/detection/images/image_000020.jpg: 480x640 3 persons, 160.2ms\n", "image 22/2210 /content/smartvision_dataset/detection/images/image_000021.jpg: 640x512 (no detections), 180.3ms\n", "image 23/2210 /content/smartvision_dataset/detection/images/image_000022.jpg: 416x640 1 train, 159.7ms\n", "image 24/2210 /content/smartvision_dataset/detection/images/image_000023.jpg: 416x640 1 train, 154.8ms\n", "image 25/2210 /content/smartvision_dataset/detection/images/image_000024.jpg: 416x640 1 train, 140.0ms\n", "image 26/2210 /content/smartvision_dataset/detection/images/image_000025.jpg: 416x640 1 train, 138.0ms\n", "image 27/2210 /content/smartvision_dataset/detection/images/image_000026.jpg: 416x640 1 train, 141.8ms\n", "image 28/2210 /content/smartvision_dataset/detection/images/image_000027.jpg: 480x640 2 persons, 160.0ms\n", "image 29/2210 /content/smartvision_dataset/detection/images/image_000028.jpg: 480x640 2 persons, 155.7ms\n", "image 30/2210 /content/smartvision_dataset/detection/images/image_000029.jpg: 480x640 2 persons, 159.7ms\n", "image 31/2210 /content/smartvision_dataset/detection/images/image_000030.jpg: 480x640 2 persons, 173.5ms\n", "image 32/2210 /content/smartvision_dataset/detection/images/image_000031.jpg: 480x640 2 persons, 159.2ms\n", "image 33/2210 /content/smartvision_dataset/detection/images/image_000032.jpg: 480x640 2 persons, 158.0ms\n", "image 34/2210 /content/smartvision_dataset/detection/images/image_000033.jpg: 480x640 2 persons, 160.1ms\n", "image 35/2210 /content/smartvision_dataset/detection/images/image_000034.jpg: 480x640 2 persons, 158.7ms\n", "image 36/2210 /content/smartvision_dataset/detection/images/image_000035.jpg: 480x640 2 persons, 155.7ms\n", "image 37/2210 /content/smartvision_dataset/detection/images/image_000036.jpg: 480x640 2 persons, 170.9ms\n", "image 38/2210 /content/smartvision_dataset/detection/images/image_000037.jpg: 640x416 4 persons, 165.8ms\n", "image 39/2210 /content/smartvision_dataset/detection/images/image_000038.jpg: 640x416 4 persons, 145.0ms\n", "image 40/2210 /content/smartvision_dataset/detection/images/image_000039.jpg: 640x416 4 persons, 147.6ms\n", "image 41/2210 /content/smartvision_dataset/detection/images/image_000040.jpg: 512x640 (no detections), 177.0ms\n", "image 42/2210 /content/smartvision_dataset/detection/images/image_000041.jpg: 480x640 2 persons, 156.6ms\n", "image 43/2210 /content/smartvision_dataset/detection/images/image_000042.jpg: 480x640 2 persons, 165.0ms\n", "image 44/2210 /content/smartvision_dataset/detection/images/image_000043.jpg: 448x640 5 airplanes, 155.5ms\n", "image 45/2210 /content/smartvision_dataset/detection/images/image_000044.jpg: 448x640 5 airplanes, 151.8ms\n", "image 46/2210 /content/smartvision_dataset/detection/images/image_000045.jpg: 448x640 5 airplanes, 156.2ms\n", "image 47/2210 /content/smartvision_dataset/detection/images/image_000046.jpg: 448x640 5 airplanes, 155.8ms\n", "image 48/2210 /content/smartvision_dataset/detection/images/image_000047.jpg: 448x640 5 airplanes, 153.8ms\n", "image 49/2210 /content/smartvision_dataset/detection/images/image_000048.jpg: 448x640 5 airplanes, 162.1ms\n", "image 50/2210 /content/smartvision_dataset/detection/images/image_000049.jpg: 448x640 5 airplanes, 144.1ms\n", "image 51/2210 /content/smartvision_dataset/detection/images/image_000050.jpg: 448x640 5 airplanes, 149.1ms\n", "image 52/2210 /content/smartvision_dataset/detection/images/image_000051.jpg: 448x640 5 airplanes, 152.5ms\n", "image 53/2210 /content/smartvision_dataset/detection/images/image_000052.jpg: 448x640 5 airplanes, 145.4ms\n", "image 54/2210 /content/smartvision_dataset/detection/images/image_000053.jpg: 448x640 5 airplanes, 146.3ms\n", "image 55/2210 /content/smartvision_dataset/detection/images/image_000054.jpg: 448x640 5 airplanes, 157.9ms\n", "image 56/2210 /content/smartvision_dataset/detection/images/image_000055.jpg: 448x640 5 airplanes, 177.7ms\n", "image 57/2210 /content/smartvision_dataset/detection/images/image_000056.jpg: 640x480 (no detections), 256.7ms\n", "image 58/2210 /content/smartvision_dataset/detection/images/image_000057.jpg: 544x640 2 persons, 282.0ms\n", "image 59/2210 /content/smartvision_dataset/detection/images/image_000058.jpg: 544x640 2 persons, 274.9ms\n", "image 60/2210 /content/smartvision_dataset/detection/images/image_000059.jpg: 480x640 11 persons, 232.0ms\n", "image 61/2210 /content/smartvision_dataset/detection/images/image_000060.jpg: 480x640 11 persons, 228.2ms\n", "image 62/2210 /content/smartvision_dataset/detection/images/image_000061.jpg: 480x640 11 persons, 240.1ms\n", "image 63/2210 /content/smartvision_dataset/detection/images/image_000062.jpg: 480x640 11 persons, 232.6ms\n", "image 64/2210 /content/smartvision_dataset/detection/images/image_000063.jpg: 448x640 (no detections), 218.2ms\n", "image 65/2210 /content/smartvision_dataset/detection/images/image_000064.jpg: 448x640 (no detections), 220.9ms\n", "image 66/2210 /content/smartvision_dataset/detection/images/image_000065.jpg: 640x480 6 persons, 236.5ms\n", "image 67/2210 /content/smartvision_dataset/detection/images/image_000066.jpg: 640x480 6 persons, 241.9ms\n", "image 68/2210 /content/smartvision_dataset/detection/images/image_000067.jpg: 640x480 6 persons, 255.7ms\n", "image 69/2210 /content/smartvision_dataset/detection/images/image_000068.jpg: 640x480 6 persons, 246.2ms\n", "image 70/2210 /content/smartvision_dataset/detection/images/image_000069.jpg: 640x480 6 persons, 236.9ms\n", "image 71/2210 /content/smartvision_dataset/detection/images/image_000070.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 235.4ms\n", "image 72/2210 /content/smartvision_dataset/detection/images/image_000071.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 178.6ms\n", "image 73/2210 /content/smartvision_dataset/detection/images/image_000072.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 152.3ms\n", "image 74/2210 /content/smartvision_dataset/detection/images/image_000073.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 158.1ms\n", "image 75/2210 /content/smartvision_dataset/detection/images/image_000074.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 151.8ms\n", "image 76/2210 /content/smartvision_dataset/detection/images/image_000075.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 154.4ms\n", "image 77/2210 /content/smartvision_dataset/detection/images/image_000076.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 154.1ms\n", "image 78/2210 /content/smartvision_dataset/detection/images/image_000077.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 162.6ms\n", "image 79/2210 /content/smartvision_dataset/detection/images/image_000078.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 154.5ms\n", "image 80/2210 /content/smartvision_dataset/detection/images/image_000079.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 157.2ms\n", "image 81/2210 /content/smartvision_dataset/detection/images/image_000080.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 153.8ms\n", "image 82/2210 /content/smartvision_dataset/detection/images/image_000081.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 151.1ms\n", "image 83/2210 /content/smartvision_dataset/detection/images/image_000082.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 152.9ms\n", "image 84/2210 /content/smartvision_dataset/detection/images/image_000083.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 154.7ms\n", "image 85/2210 /content/smartvision_dataset/detection/images/image_000084.jpg: 448x640 2 persons, 1 bus, 149.0ms\n", "image 86/2210 /content/smartvision_dataset/detection/images/image_000085.jpg: 448x640 2 persons, 150.3ms\n", "image 87/2210 /content/smartvision_dataset/detection/images/image_000086.jpg: 480x640 3 persons, 159.2ms\n", "image 88/2210 /content/smartvision_dataset/detection/images/image_000087.jpg: 480x640 3 persons, 156.2ms\n", "image 89/2210 /content/smartvision_dataset/detection/images/image_000088.jpg: 480x640 3 persons, 158.7ms\n", "image 90/2210 /content/smartvision_dataset/detection/images/image_000089.jpg: 448x640 1 person, 1 bus, 149.7ms\n", "image 91/2210 /content/smartvision_dataset/detection/images/image_000090.jpg: 448x640 1 person, 1 bus, 158.6ms\n", "image 92/2210 /content/smartvision_dataset/detection/images/image_000091.jpg: 448x640 1 motorcycle, 148.0ms\n", "image 93/2210 /content/smartvision_dataset/detection/images/image_000092.jpg: 640x640 11 persons, 223.6ms\n", "image 94/2210 /content/smartvision_dataset/detection/images/image_000093.jpg: 640x640 11 persons, 206.1ms\n", "image 95/2210 /content/smartvision_dataset/detection/images/image_000094.jpg: 448x640 7 persons, 8 bicycles, 141.9ms\n", "image 96/2210 /content/smartvision_dataset/detection/images/image_000095.jpg: 448x640 7 persons, 8 bicycles, 163.1ms\n", "image 97/2210 /content/smartvision_dataset/detection/images/image_000096.jpg: 448x640 7 persons, 8 bicycles, 149.6ms\n", "image 98/2210 /content/smartvision_dataset/detection/images/image_000097.jpg: 448x640 7 persons, 8 bicycles, 141.7ms\n", "image 99/2210 /content/smartvision_dataset/detection/images/image_000098.jpg: 448x640 7 persons, 8 bicycles, 146.4ms\n", "image 100/2210 /content/smartvision_dataset/detection/images/image_000099.jpg: 640x480 4 persons, 3 bicycles, 1 motorcycle, 157.1ms\n", "image 101/2210 /content/smartvision_dataset/detection/images/image_000100.jpg: 640x480 4 persons, 3 bicycles, 1 motorcycle, 152.4ms\n", "image 102/2210 /content/smartvision_dataset/detection/images/image_000101.jpg: 640x480 4 persons, 3 bicycles, 1 motorcycle, 159.3ms\n", "image 103/2210 /content/smartvision_dataset/detection/images/image_000102.jpg: 640x480 4 persons, 3 bicycles, 1 motorcycle, 162.2ms\n", "image 104/2210 /content/smartvision_dataset/detection/images/image_000103.jpg: 640x480 4 persons, 3 bicycles, 1 motorcycle, 153.0ms\n", "image 105/2210 /content/smartvision_dataset/detection/images/image_000104.jpg: 640x480 4 persons, 3 bicycles, 1 motorcycle, 154.5ms\n", "image 106/2210 /content/smartvision_dataset/detection/images/image_000105.jpg: 640x640 1 bicycle, 1 bus, 198.7ms\n", "image 107/2210 /content/smartvision_dataset/detection/images/image_000106.jpg: 448x640 2 bicycles, 142.9ms\n", "image 108/2210 /content/smartvision_dataset/detection/images/image_000107.jpg: 448x640 2 bicycles, 147.4ms\n", "image 109/2210 /content/smartvision_dataset/detection/images/image_000108.jpg: 480x640 3 persons, 2 bicycles, 159.4ms\n", "image 110/2210 /content/smartvision_dataset/detection/images/image_000109.jpg: 480x640 3 persons, 2 bicycles, 154.2ms\n", "image 111/2210 /content/smartvision_dataset/detection/images/image_000110.jpg: 448x640 4 persons, 145.5ms\n", "image 112/2210 /content/smartvision_dataset/detection/images/image_000111.jpg: 448x640 4 persons, 142.6ms\n", "image 113/2210 /content/smartvision_dataset/detection/images/image_000112.jpg: 448x640 1 bicycle, 1 bed, 144.3ms\n", "image 114/2210 /content/smartvision_dataset/detection/images/image_000113.jpg: 480x640 7 persons, 7 bicycles, 1 car, 154.7ms\n", "image 115/2210 /content/smartvision_dataset/detection/images/image_000114.jpg: 480x640 7 persons, 7 bicycles, 1 car, 179.3ms\n", "image 116/2210 /content/smartvision_dataset/detection/images/image_000115.jpg: 480x640 7 persons, 7 bicycles, 1 car, 158.9ms\n", "image 117/2210 /content/smartvision_dataset/detection/images/image_000116.jpg: 480x640 7 persons, 7 bicycles, 1 car, 156.3ms\n", "image 118/2210 /content/smartvision_dataset/detection/images/image_000117.jpg: 480x640 7 persons, 7 bicycles, 1 car, 155.7ms\n", "image 119/2210 /content/smartvision_dataset/detection/images/image_000118.jpg: 480x640 7 persons, 7 bicycles, 1 car, 158.0ms\n", "image 120/2210 /content/smartvision_dataset/detection/images/image_000119.jpg: 480x640 7 persons, 7 bicycles, 1 car, 152.9ms\n", "image 121/2210 /content/smartvision_dataset/detection/images/image_000120.jpg: 480x640 7 persons, 7 bicycles, 1 car, 164.8ms\n", "image 122/2210 /content/smartvision_dataset/detection/images/image_000121.jpg: 480x640 7 persons, 7 bicycles, 1 car, 157.7ms\n", "image 123/2210 /content/smartvision_dataset/detection/images/image_000122.jpg: 480x640 (no detections), 148.4ms\n", "image 124/2210 /content/smartvision_dataset/detection/images/image_000123.jpg: 480x640 (no detections), 150.6ms\n", "image 125/2210 /content/smartvision_dataset/detection/images/image_000124.jpg: 480x640 (no detections), 156.3ms\n", "image 126/2210 /content/smartvision_dataset/detection/images/image_000125.jpg: 480x640 6 persons, 157.7ms\n", "image 127/2210 /content/smartvision_dataset/detection/images/image_000126.jpg: 640x512 1 person, 1 car, 182.2ms\n", "image 128/2210 /content/smartvision_dataset/detection/images/image_000127.jpg: 640x512 1 person, 1 car, 166.6ms\n", "image 129/2210 /content/smartvision_dataset/detection/images/image_000128.jpg: 640x512 10 persons, 1 bus, 165.6ms\n", "image 130/2210 /content/smartvision_dataset/detection/images/image_000129.jpg: 640x512 10 persons, 1 bus, 252.1ms\n", "image 131/2210 /content/smartvision_dataset/detection/images/image_000130.jpg: 480x640 (no detections), 233.6ms\n", "image 132/2210 /content/smartvision_dataset/detection/images/image_000131.jpg: 512x640 2 persons, 256.6ms\n", "image 133/2210 /content/smartvision_dataset/detection/images/image_000132.jpg: 512x640 2 persons, 236.2ms\n", "image 134/2210 /content/smartvision_dataset/detection/images/image_000133.jpg: 640x448 1 person, 234.1ms\n", "image 135/2210 /content/smartvision_dataset/detection/images/image_000134.jpg: 480x640 1 bicycle, 1 bus, 234.6ms\n", "image 136/2210 /content/smartvision_dataset/detection/images/image_000135.jpg: 480x640 1 bicycle, 1 bus, 253.0ms\n", "image 137/2210 /content/smartvision_dataset/detection/images/image_000136.jpg: 480x640 1 bicycle, 1 bus, 232.7ms\n", "image 138/2210 /content/smartvision_dataset/detection/images/image_000137.jpg: 448x640 12 persons, 2 bicycles, 226.0ms\n", "image 139/2210 /content/smartvision_dataset/detection/images/image_000138.jpg: 448x640 5 persons, 12 bicycles, 1 car, 219.6ms\n", "image 140/2210 /content/smartvision_dataset/detection/images/image_000139.jpg: 448x640 5 persons, 12 bicycles, 1 car, 235.0ms\n", "image 141/2210 /content/smartvision_dataset/detection/images/image_000140.jpg: 448x640 5 persons, 12 bicycles, 1 car, 214.5ms\n", "image 142/2210 /content/smartvision_dataset/detection/images/image_000141.jpg: 448x640 5 persons, 12 bicycles, 1 car, 218.1ms\n", "image 143/2210 /content/smartvision_dataset/detection/images/image_000142.jpg: 448x640 5 persons, 12 bicycles, 1 car, 220.7ms\n", "image 144/2210 /content/smartvision_dataset/detection/images/image_000143.jpg: 448x640 5 persons, 12 bicycles, 1 car, 238.0ms\n", "image 145/2210 /content/smartvision_dataset/detection/images/image_000144.jpg: 448x640 5 persons, 12 bicycles, 1 car, 225.4ms\n", "image 146/2210 /content/smartvision_dataset/detection/images/image_000145.jpg: 448x640 5 persons, 12 bicycles, 1 car, 167.8ms\n", "image 147/2210 /content/smartvision_dataset/detection/images/image_000146.jpg: 448x640 5 persons, 12 bicycles, 1 car, 144.3ms\n", "image 148/2210 /content/smartvision_dataset/detection/images/image_000147.jpg: 448x640 5 persons, 12 bicycles, 1 car, 142.0ms\n", "image 149/2210 /content/smartvision_dataset/detection/images/image_000148.jpg: 448x640 5 persons, 12 bicycles, 1 car, 146.7ms\n", "image 150/2210 /content/smartvision_dataset/detection/images/image_000149.jpg: 448x640 4 persons, 6 bicycles, 157.5ms\n", "image 151/2210 /content/smartvision_dataset/detection/images/image_000150.jpg: 448x640 4 persons, 6 bicycles, 143.5ms\n", "image 152/2210 /content/smartvision_dataset/detection/images/image_000151.jpg: 448x640 4 persons, 6 bicycles, 142.3ms\n", "image 153/2210 /content/smartvision_dataset/detection/images/image_000152.jpg: 448x640 4 persons, 6 bicycles, 147.0ms\n", "image 154/2210 /content/smartvision_dataset/detection/images/image_000153.jpg: 448x640 2 persons, 1 cow, 143.3ms\n", "image 155/2210 /content/smartvision_dataset/detection/images/image_000154.jpg: 448x640 2 bicycles, 147.6ms\n", "image 156/2210 /content/smartvision_dataset/detection/images/image_000155.jpg: 448x640 2 bicycles, 147.4ms\n", "image 157/2210 /content/smartvision_dataset/detection/images/image_000156.jpg: 448x640 2 bicycles, 156.4ms\n", "image 158/2210 /content/smartvision_dataset/detection/images/image_000157.jpg: 448x640 2 bicycles, 144.7ms\n", "image 159/2210 /content/smartvision_dataset/detection/images/image_000158.jpg: 448x640 2 bicycles, 147.3ms\n", "image 160/2210 /content/smartvision_dataset/detection/images/image_000159.jpg: 448x640 2 bicycles, 143.4ms\n", "image 161/2210 /content/smartvision_dataset/detection/images/image_000160.jpg: 448x640 2 bicycles, 145.8ms\n", "image 162/2210 /content/smartvision_dataset/detection/images/image_000161.jpg: 448x640 2 bicycles, 143.3ms\n", "image 163/2210 /content/smartvision_dataset/detection/images/image_000162.jpg: 448x640 2 bicycles, 157.3ms\n", "image 164/2210 /content/smartvision_dataset/detection/images/image_000163.jpg: 448x640 2 bicycles, 143.1ms\n", "image 165/2210 /content/smartvision_dataset/detection/images/image_000164.jpg: 448x640 2 bicycles, 147.5ms\n", "image 166/2210 /content/smartvision_dataset/detection/images/image_000165.jpg: 448x640 7 persons, 143.9ms\n", "image 167/2210 /content/smartvision_dataset/detection/images/image_000166.jpg: 640x640 8 persons, 204.1ms\n", "image 168/2210 /content/smartvision_dataset/detection/images/image_000167.jpg: 640x640 8 persons, 200.5ms\n", "image 169/2210 /content/smartvision_dataset/detection/images/image_000168.jpg: 640x640 8 persons, 215.5ms\n", "image 170/2210 /content/smartvision_dataset/detection/images/image_000169.jpg: 640x448 1 person, 1 bicycle, 1 bus, 147.3ms\n", "image 171/2210 /content/smartvision_dataset/detection/images/image_000170.jpg: 640x480 1 person, 1 car, 177.4ms\n", "image 172/2210 /content/smartvision_dataset/detection/images/image_000171.jpg: 448x640 4 trains, 143.0ms\n", "image 173/2210 /content/smartvision_dataset/detection/images/image_000172.jpg: 448x640 4 trains, 147.4ms\n", "image 174/2210 /content/smartvision_dataset/detection/images/image_000173.jpg: 448x640 4 trains, 143.9ms\n", "image 175/2210 /content/smartvision_dataset/detection/images/image_000174.jpg: 448x640 4 trains, 162.9ms\n", "image 176/2210 /content/smartvision_dataset/detection/images/image_000175.jpg: 448x640 4 trains, 147.0ms\n", "image 177/2210 /content/smartvision_dataset/detection/images/image_000176.jpg: 448x640 4 trains, 147.5ms\n", "image 178/2210 /content/smartvision_dataset/detection/images/image_000177.jpg: 448x640 4 trains, 146.6ms\n", "image 179/2210 /content/smartvision_dataset/detection/images/image_000178.jpg: 448x640 4 trains, 147.0ms\n", "image 180/2210 /content/smartvision_dataset/detection/images/image_000179.jpg: 448x640 4 trains, 146.0ms\n", "image 181/2210 /content/smartvision_dataset/detection/images/image_000180.jpg: 448x640 4 trains, 160.7ms\n", "image 182/2210 /content/smartvision_dataset/detection/images/image_000181.jpg: 448x640 4 trains, 144.5ms\n", "image 183/2210 /content/smartvision_dataset/detection/images/image_000182.jpg: 448x640 4 trains, 152.5ms\n", "image 184/2210 /content/smartvision_dataset/detection/images/image_000183.jpg: 448x640 4 trains, 146.7ms\n", "image 185/2210 /content/smartvision_dataset/detection/images/image_000184.jpg: 448x640 (no detections), 146.7ms\n", "image 186/2210 /content/smartvision_dataset/detection/images/image_000185.jpg: 448x640 5 airplanes, 147.5ms\n", "image 187/2210 /content/smartvision_dataset/detection/images/image_000186.jpg: 448x640 5 airplanes, 144.3ms\n", "image 188/2210 /content/smartvision_dataset/detection/images/image_000187.jpg: 448x640 5 airplanes, 152.7ms\n", "image 189/2210 /content/smartvision_dataset/detection/images/image_000188.jpg: 448x640 5 airplanes, 148.9ms\n", "image 190/2210 /content/smartvision_dataset/detection/images/image_000189.jpg: 448x640 (no detections), 142.1ms\n", "image 191/2210 /content/smartvision_dataset/detection/images/image_000190.jpg: 448x640 (no detections), 148.1ms\n", "image 192/2210 /content/smartvision_dataset/detection/images/image_000191.jpg: 448x640 (no detections), 147.1ms\n", "image 193/2210 /content/smartvision_dataset/detection/images/image_000192.jpg: 448x640 (no detections), 144.0ms\n", "image 194/2210 /content/smartvision_dataset/detection/images/image_000193.jpg: 448x640 (no detections), 159.3ms\n", "image 195/2210 /content/smartvision_dataset/detection/images/image_000194.jpg: 448x640 (no detections), 149.3ms\n", "image 196/2210 /content/smartvision_dataset/detection/images/image_000195.jpg: 448x640 2 persons, 1 bus, 140.7ms\n", "image 197/2210 /content/smartvision_dataset/detection/images/image_000196.jpg: 480x640 (no detections), 153.5ms\n", "image 198/2210 /content/smartvision_dataset/detection/images/image_000197.jpg: 512x640 6 persons, 163.5ms\n", "image 199/2210 /content/smartvision_dataset/detection/images/image_000198.jpg: 448x640 (no detections), 142.4ms\n", "image 200/2210 /content/smartvision_dataset/detection/images/image_000199.jpg: 480x640 2 persons, 165.8ms\n", "image 201/2210 /content/smartvision_dataset/detection/images/image_000200.jpg: 480x640 2 persons, 158.3ms\n", "image 202/2210 /content/smartvision_dataset/detection/images/image_000201.jpg: 480x640 2 persons, 151.7ms\n", "image 203/2210 /content/smartvision_dataset/detection/images/image_000202.jpg: 448x640 2 airplanes, 143.0ms\n", "image 204/2210 /content/smartvision_dataset/detection/images/image_000203.jpg: 448x640 2 airplanes, 143.7ms\n", "image 205/2210 /content/smartvision_dataset/detection/images/image_000204.jpg: 448x640 2 airplanes, 141.9ms\n", "image 206/2210 /content/smartvision_dataset/detection/images/image_000205.jpg: 448x640 2 airplanes, 142.2ms\n", "image 207/2210 /content/smartvision_dataset/detection/images/image_000206.jpg: 448x640 2 airplanes, 245.6ms\n", "image 208/2210 /content/smartvision_dataset/detection/images/image_000207.jpg: 448x640 2 airplanes, 231.4ms\n", "image 209/2210 /content/smartvision_dataset/detection/images/image_000208.jpg: 448x640 2 airplanes, 229.8ms\n", "image 210/2210 /content/smartvision_dataset/detection/images/image_000209.jpg: 448x640 2 airplanes, 215.7ms\n", "image 211/2210 /content/smartvision_dataset/detection/images/image_000210.jpg: 448x640 2 airplanes, 237.2ms\n", "image 212/2210 /content/smartvision_dataset/detection/images/image_000211.jpg: 448x640 2 airplanes, 214.6ms\n", "image 213/2210 /content/smartvision_dataset/detection/images/image_000212.jpg: 448x640 2 airplanes, 225.0ms\n", "image 214/2210 /content/smartvision_dataset/detection/images/image_000213.jpg: 448x640 2 airplanes, 212.7ms\n", "image 215/2210 /content/smartvision_dataset/detection/images/image_000214.jpg: 448x640 2 airplanes, 219.4ms\n", "image 216/2210 /content/smartvision_dataset/detection/images/image_000215.jpg: 448x640 2 airplanes, 223.8ms\n", "image 217/2210 /content/smartvision_dataset/detection/images/image_000216.jpg: 448x640 2 cats, 225.5ms\n", "image 218/2210 /content/smartvision_dataset/detection/images/image_000217.jpg: 480x640 1 motorcycle, 227.9ms\n", "image 219/2210 /content/smartvision_dataset/detection/images/image_000218.jpg: 480x640 1 motorcycle, 227.2ms\n", "image 220/2210 /content/smartvision_dataset/detection/images/image_000219.jpg: 480x640 1 person, 1 airplane, 252.0ms\n", "image 221/2210 /content/smartvision_dataset/detection/images/image_000220.jpg: 480x640 1 person, 1 airplane, 241.3ms\n", "image 222/2210 /content/smartvision_dataset/detection/images/image_000221.jpg: 480x640 1 person, 1 airplane, 234.2ms\n", "image 223/2210 /content/smartvision_dataset/detection/images/image_000222.jpg: 480x640 1 person, 1 airplane, 190.5ms\n", "image 224/2210 /content/smartvision_dataset/detection/images/image_000223.jpg: 480x640 1 person, 1 airplane, 149.3ms\n", "image 225/2210 /content/smartvision_dataset/detection/images/image_000224.jpg: 480x640 1 person, 1 airplane, 164.1ms\n", "image 226/2210 /content/smartvision_dataset/detection/images/image_000225.jpg: 448x640 1 bus, 142.4ms\n", "image 227/2210 /content/smartvision_dataset/detection/images/image_000226.jpg: 448x640 1 bus, 141.4ms\n", "image 228/2210 /content/smartvision_dataset/detection/images/image_000227.jpg: 480x640 1 person, 1 car, 147.2ms\n", "image 229/2210 /content/smartvision_dataset/detection/images/image_000228.jpg: 480x640 1 person, 1 car, 153.9ms\n", "image 230/2210 /content/smartvision_dataset/detection/images/image_000229.jpg: 480x640 1 person, 1 car, 154.6ms\n", "image 231/2210 /content/smartvision_dataset/detection/images/image_000230.jpg: 480x640 1 person, 1 car, 165.3ms\n", "image 232/2210 /content/smartvision_dataset/detection/images/image_000231.jpg: 448x640 1 motorcycle, 140.8ms\n", "image 233/2210 /content/smartvision_dataset/detection/images/image_000232.jpg: 480x640 (no detections), 149.2ms\n", "image 234/2210 /content/smartvision_dataset/detection/images/image_000233.jpg: 640x512 2 persons, 160.0ms\n", "image 235/2210 /content/smartvision_dataset/detection/images/image_000234.jpg: 640x512 2 persons, 165.2ms\n", "image 236/2210 /content/smartvision_dataset/detection/images/image_000235.jpg: 640x512 2 persons, 169.4ms\n", "image 237/2210 /content/smartvision_dataset/detection/images/image_000236.jpg: 640x512 2 persons, 181.3ms\n", "image 238/2210 /content/smartvision_dataset/detection/images/image_000237.jpg: 640x512 2 persons, 165.9ms\n", "image 239/2210 /content/smartvision_dataset/detection/images/image_000238.jpg: 576x640 2 persons, 197.8ms\n", "image 240/2210 /content/smartvision_dataset/detection/images/image_000239.jpg: 512x640 2 trains, 162.8ms\n", "image 241/2210 /content/smartvision_dataset/detection/images/image_000240.jpg: 512x640 2 trains, 165.7ms\n", "image 242/2210 /content/smartvision_dataset/detection/images/image_000241.jpg: 512x640 2 trains, 164.4ms\n", "image 243/2210 /content/smartvision_dataset/detection/images/image_000242.jpg: 512x640 2 trains, 177.1ms\n", "image 244/2210 /content/smartvision_dataset/detection/images/image_000243.jpg: 512x640 2 trains, 161.6ms\n", "image 245/2210 /content/smartvision_dataset/detection/images/image_000244.jpg: 512x640 2 trains, 163.8ms\n", "image 246/2210 /content/smartvision_dataset/detection/images/image_000245.jpg: 512x640 2 trains, 165.5ms\n", "image 247/2210 /content/smartvision_dataset/detection/images/image_000246.jpg: 512x640 2 trains, 167.1ms\n", "image 248/2210 /content/smartvision_dataset/detection/images/image_000247.jpg: 512x640 2 trains, 180.3ms\n", "image 249/2210 /content/smartvision_dataset/detection/images/image_000248.jpg: 512x640 2 trains, 166.7ms\n", "image 250/2210 /content/smartvision_dataset/detection/images/image_000249.jpg: 512x640 2 trains, 161.7ms\n", "image 251/2210 /content/smartvision_dataset/detection/images/image_000250.jpg: 640x448 4 persons, 143.7ms\n", "image 252/2210 /content/smartvision_dataset/detection/images/image_000251.jpg: 640x448 4 persons, 152.1ms\n", "image 253/2210 /content/smartvision_dataset/detection/images/image_000252.jpg: 640x448 4 persons, 147.8ms\n", "image 254/2210 /content/smartvision_dataset/detection/images/image_000253.jpg: 448x640 7 persons, 8 bicycles, 142.1ms\n", "image 255/2210 /content/smartvision_dataset/detection/images/image_000254.jpg: 480x640 (no detections), 161.9ms\n", "image 256/2210 /content/smartvision_dataset/detection/images/image_000255.jpg: 640x576 5 motorcycles, 193.9ms\n", "image 257/2210 /content/smartvision_dataset/detection/images/image_000256.jpg: 640x576 5 motorcycles, 180.1ms\n", "image 258/2210 /content/smartvision_dataset/detection/images/image_000257.jpg: 640x512 (no detections), 167.1ms\n", "image 259/2210 /content/smartvision_dataset/detection/images/image_000258.jpg: 640x448 4 persons, 1 motorcycle, 143.2ms\n", "image 260/2210 /content/smartvision_dataset/detection/images/image_000259.jpg: 448x640 (no detections), 160.7ms\n", "image 261/2210 /content/smartvision_dataset/detection/images/image_000260.jpg: 480x640 1 motorcycle, 153.7ms\n", "image 262/2210 /content/smartvision_dataset/detection/images/image_000261.jpg: 480x640 (no detections), 149.9ms\n", "image 263/2210 /content/smartvision_dataset/detection/images/image_000262.jpg: 640x384 4 persons, 1 motorcycle, 147.2ms\n", "image 264/2210 /content/smartvision_dataset/detection/images/image_000263.jpg: 448x640 1 motorcycle, 145.3ms\n", "image 265/2210 /content/smartvision_dataset/detection/images/image_000264.jpg: 480x640 1 airplane, 155.8ms\n", "image 266/2210 /content/smartvision_dataset/detection/images/image_000265.jpg: 640x480 4 persons, 3 bicycles, 1 motorcycle, 163.2ms\n", "image 267/2210 /content/smartvision_dataset/detection/images/image_000266.jpg: 448x640 2 bicycles, 141.4ms\n", "image 268/2210 /content/smartvision_dataset/detection/images/image_000267.jpg: 480x640 2 persons, 1 bus, 149.8ms\n", "image 269/2210 /content/smartvision_dataset/detection/images/image_000268.jpg: 448x640 2 persons, 1 motorcycle, 1 dog, 140.8ms\n", "image 270/2210 /content/smartvision_dataset/detection/images/image_000269.jpg: 640x448 1 motorcycle, 1 dog, 146.5ms\n", "image 271/2210 /content/smartvision_dataset/detection/images/image_000270.jpg: 448x640 1 person, 12 bicycles, 145.9ms\n", "image 272/2210 /content/smartvision_dataset/detection/images/image_000271.jpg: 448x640 1 person, 12 bicycles, 143.6ms\n", "image 273/2210 /content/smartvision_dataset/detection/images/image_000272.jpg: 448x640 1 person, 12 bicycles, 155.5ms\n", "image 274/2210 /content/smartvision_dataset/detection/images/image_000273.jpg: 448x640 1 person, 12 bicycles, 136.8ms\n", "image 275/2210 /content/smartvision_dataset/detection/images/image_000274.jpg: 448x640 1 person, 12 bicycles, 139.3ms\n", "image 276/2210 /content/smartvision_dataset/detection/images/image_000275.jpg: 448x640 1 person, 12 bicycles, 146.0ms\n", "image 277/2210 /content/smartvision_dataset/detection/images/image_000276.jpg: 448x640 1 person, 12 bicycles, 141.5ms\n", "image 278/2210 /content/smartvision_dataset/detection/images/image_000277.jpg: 448x640 1 person, 12 bicycles, 140.8ms\n", "image 279/2210 /content/smartvision_dataset/detection/images/image_000278.jpg: 448x640 1 person, 12 bicycles, 144.5ms\n", "image 280/2210 /content/smartvision_dataset/detection/images/image_000279.jpg: 448x640 1 person, 12 bicycles, 148.2ms\n", "image 281/2210 /content/smartvision_dataset/detection/images/image_000280.jpg: 448x640 1 person, 12 bicycles, 140.5ms\n", "image 282/2210 /content/smartvision_dataset/detection/images/image_000281.jpg: 448x640 1 person, 12 bicycles, 212.2ms\n", "image 283/2210 /content/smartvision_dataset/detection/images/image_000282.jpg: 448x640 1 person, 12 bicycles, 216.1ms\n", "image 284/2210 /content/smartvision_dataset/detection/images/image_000283.jpg: 448x640 1 person, 12 bicycles, 217.7ms\n", "image 285/2210 /content/smartvision_dataset/detection/images/image_000284.jpg: 352x640 (no detections), 197.7ms\n", "image 286/2210 /content/smartvision_dataset/detection/images/image_000285.jpg: 384x640 5 motorcycles, 203.1ms\n", "image 287/2210 /content/smartvision_dataset/detection/images/image_000286.jpg: 384x640 5 motorcycles, 191.5ms\n", "image 288/2210 /content/smartvision_dataset/detection/images/image_000287.jpg: 384x640 5 motorcycles, 198.8ms\n", "image 289/2210 /content/smartvision_dataset/detection/images/image_000288.jpg: 384x640 5 motorcycles, 199.3ms\n", "image 290/2210 /content/smartvision_dataset/detection/images/image_000289.jpg: 384x640 5 motorcycles, 198.5ms\n", "image 291/2210 /content/smartvision_dataset/detection/images/image_000290.jpg: 448x640 1 person, 217.9ms\n", "image 292/2210 /content/smartvision_dataset/detection/images/image_000291.jpg: 480x640 2 motorcycles, 231.9ms\n", "image 293/2210 /content/smartvision_dataset/detection/images/image_000292.jpg: 448x640 4 persons, 227.5ms\n", "image 294/2210 /content/smartvision_dataset/detection/images/image_000293.jpg: 448x640 4 persons, 223.8ms\n", "image 295/2210 /content/smartvision_dataset/detection/images/image_000294.jpg: 640x448 4 persons, 214.7ms\n", "image 296/2210 /content/smartvision_dataset/detection/images/image_000295.jpg: 448x640 3 persons, 5 motorcycles, 218.5ms\n", "image 297/2210 /content/smartvision_dataset/detection/images/image_000296.jpg: 448x640 3 persons, 5 motorcycles, 218.7ms\n", "image 298/2210 /content/smartvision_dataset/detection/images/image_000297.jpg: 448x640 (no detections), 224.5ms\n", "image 299/2210 /content/smartvision_dataset/detection/images/image_000298.jpg: 448x640 (no detections), 231.2ms\n", "image 300/2210 /content/smartvision_dataset/detection/images/image_000299.jpg: 448x640 12 persons, 2 bicycles, 140.2ms\n", "image 301/2210 /content/smartvision_dataset/detection/images/image_000300.jpg: 448x640 12 persons, 2 bicycles, 141.9ms\n", "image 302/2210 /content/smartvision_dataset/detection/images/image_000301.jpg: 448x640 2 persons, 1 cow, 139.9ms\n", "image 303/2210 /content/smartvision_dataset/detection/images/image_000302.jpg: 384x640 1 motorcycle, 126.4ms\n", "image 304/2210 /content/smartvision_dataset/detection/images/image_000303.jpg: 480x640 1 person, 149.5ms\n", "image 305/2210 /content/smartvision_dataset/detection/images/image_000304.jpg: 480x640 4 persons, 155.0ms\n", "image 306/2210 /content/smartvision_dataset/detection/images/image_000305.jpg: 480x640 4 persons, 151.7ms\n", "image 307/2210 /content/smartvision_dataset/detection/images/image_000306.jpg: 640x640 2 persons, 194.4ms\n", "image 308/2210 /content/smartvision_dataset/detection/images/image_000307.jpg: 480x640 1 motorcycle, 150.3ms\n", "image 309/2210 /content/smartvision_dataset/detection/images/image_000308.jpg: 640x640 2 persons, 1 bicycle, 2 motorcycles, 190.0ms\n", "image 310/2210 /content/smartvision_dataset/detection/images/image_000309.jpg: 384x640 4 persons, 3 bicycles, 3 buss, 124.7ms\n", "image 311/2210 /content/smartvision_dataset/detection/images/image_000310.jpg: 384x640 4 persons, 3 bicycles, 3 buss, 125.5ms\n", "image 312/2210 /content/smartvision_dataset/detection/images/image_000311.jpg: 384x640 4 persons, 3 bicycles, 3 buss, 129.8ms\n", "image 313/2210 /content/smartvision_dataset/detection/images/image_000312.jpg: 384x640 4 persons, 3 bicycles, 3 buss, 124.0ms\n", "image 314/2210 /content/smartvision_dataset/detection/images/image_000313.jpg: 480x640 7 persons, 2 motorcycles, 149.8ms\n", "image 315/2210 /content/smartvision_dataset/detection/images/image_000314.jpg: 480x640 1 motorcycle, 152.3ms\n", "image 316/2210 /content/smartvision_dataset/detection/images/image_000315.jpg: 480x640 (no detections), 147.7ms\n", "image 317/2210 /content/smartvision_dataset/detection/images/image_000316.jpg: 480x640 4 persons, 5 bicycles, 1 car, 148.5ms\n", "image 318/2210 /content/smartvision_dataset/detection/images/image_000317.jpg: 448x640 2 motorcycles, 158.1ms\n", "image 319/2210 /content/smartvision_dataset/detection/images/image_000318.jpg: 448x640 1 person, 1 motorcycle, 140.3ms\n", "image 320/2210 /content/smartvision_dataset/detection/images/image_000319.jpg: 544x640 (no detections), 171.1ms\n", "image 321/2210 /content/smartvision_dataset/detection/images/image_000320.jpg: 480x640 5 persons, 149.3ms\n", "image 322/2210 /content/smartvision_dataset/detection/images/image_000321.jpg: 480x640 5 persons, 146.7ms\n", "image 323/2210 /content/smartvision_dataset/detection/images/image_000322.jpg: 480x640 5 persons, 154.4ms\n", "image 324/2210 /content/smartvision_dataset/detection/images/image_000323.jpg: 448x640 8 persons, 157.8ms\n", "image 325/2210 /content/smartvision_dataset/detection/images/image_000324.jpg: 480x640 3 motorcycles, 153.8ms\n", "image 326/2210 /content/smartvision_dataset/detection/images/image_000325.jpg: 448x640 2 persons, 2 bicycles, 1 horse, 142.2ms\n", "image 327/2210 /content/smartvision_dataset/detection/images/image_000326.jpg: 480x640 1 motorcycle, 153.2ms\n", "image 328/2210 /content/smartvision_dataset/detection/images/image_000327.jpg: 480x640 2 motorcycles, 150.5ms\n", "image 329/2210 /content/smartvision_dataset/detection/images/image_000328.jpg: 640x480 2 persons, 150.8ms\n", "image 330/2210 /content/smartvision_dataset/detection/images/image_000329.jpg: 512x640 3 persons, 1 motorcycle, 170.1ms\n", "image 331/2210 /content/smartvision_dataset/detection/images/image_000330.jpg: 512x640 3 persons, 1 motorcycle, 165.4ms\n", "image 332/2210 /content/smartvision_dataset/detection/images/image_000331.jpg: 512x640 3 persons, 1 motorcycle, 165.5ms\n", "image 333/2210 /content/smartvision_dataset/detection/images/image_000332.jpg: 512x640 3 persons, 1 motorcycle, 162.7ms\n", "image 334/2210 /content/smartvision_dataset/detection/images/image_000333.jpg: 448x640 (no detections), 142.7ms\n", "image 335/2210 /content/smartvision_dataset/detection/images/image_000334.jpg: 448x640 (no detections), 139.1ms\n", "image 336/2210 /content/smartvision_dataset/detection/images/image_000335.jpg: 448x640 6 persons, 1 motorcycle, 143.9ms\n", "image 337/2210 /content/smartvision_dataset/detection/images/image_000336.jpg: 640x448 1 motorcycle, 178.9ms\n", "image 338/2210 /content/smartvision_dataset/detection/images/image_000337.jpg: 640x448 1 motorcycle, 147.1ms\n", "image 339/2210 /content/smartvision_dataset/detection/images/image_000338.jpg: 640x448 1 motorcycle, 146.2ms\n", "image 340/2210 /content/smartvision_dataset/detection/images/image_000339.jpg: 640x448 1 motorcycle, 144.6ms\n", "image 341/2210 /content/smartvision_dataset/detection/images/image_000340.jpg: 448x640 1 airplane, 141.2ms\n", "image 342/2210 /content/smartvision_dataset/detection/images/image_000341.jpg: 448x640 (no detections), 145.9ms\n", "image 343/2210 /content/smartvision_dataset/detection/images/image_000342.jpg: 448x640 (no detections), 157.8ms\n", "image 344/2210 /content/smartvision_dataset/detection/images/image_000343.jpg: 256x640 2 airplanes, 102.0ms\n", "image 345/2210 /content/smartvision_dataset/detection/images/image_000344.jpg: 448x640 2 airplanes, 146.9ms\n", "image 346/2210 /content/smartvision_dataset/detection/images/image_000345.jpg: 512x640 3 persons, 161.4ms\n", "image 347/2210 /content/smartvision_dataset/detection/images/image_000346.jpg: 512x640 1 airplane, 165.4ms\n", "image 348/2210 /content/smartvision_dataset/detection/images/image_000347.jpg: 512x640 1 airplane, 169.7ms\n", "image 349/2210 /content/smartvision_dataset/detection/images/image_000348.jpg: 640x448 1 airplane, 155.9ms\n", "image 350/2210 /content/smartvision_dataset/detection/images/image_000349.jpg: 448x640 1 airplane, 154.6ms\n", "image 351/2210 /content/smartvision_dataset/detection/images/image_000350.jpg: 448x640 1 airplane, 149.0ms\n", "image 352/2210 /content/smartvision_dataset/detection/images/image_000351.jpg: 448x640 1 airplane, 140.1ms\n", "image 353/2210 /content/smartvision_dataset/detection/images/image_000352.jpg: 448x640 1 airplane, 139.4ms\n", "image 354/2210 /content/smartvision_dataset/detection/images/image_000353.jpg: 384x640 1 airplane, 127.6ms\n", "image 355/2210 /content/smartvision_dataset/detection/images/image_000354.jpg: 480x640 1 airplane, 151.1ms\n", "image 356/2210 /content/smartvision_dataset/detection/images/image_000355.jpg: 512x640 3 airplanes, 182.0ms\n", "image 357/2210 /content/smartvision_dataset/detection/images/image_000356.jpg: 512x640 3 airplanes, 167.5ms\n", "image 358/2210 /content/smartvision_dataset/detection/images/image_000357.jpg: 512x640 3 airplanes, 158.2ms\n", "image 359/2210 /content/smartvision_dataset/detection/images/image_000358.jpg: 512x640 3 airplanes, 159.1ms\n", "image 360/2210 /content/smartvision_dataset/detection/images/image_000359.jpg: 480x640 (no detections), 187.8ms\n", "image 361/2210 /content/smartvision_dataset/detection/images/image_000360.jpg: 512x640 2 airplanes, 262.2ms\n", "image 362/2210 /content/smartvision_dataset/detection/images/image_000361.jpg: 448x640 3 airplanes, 229.6ms\n", "image 363/2210 /content/smartvision_dataset/detection/images/image_000362.jpg: 448x640 3 airplanes, 225.5ms\n", "image 364/2210 /content/smartvision_dataset/detection/images/image_000363.jpg: 384x640 (no detections), 192.2ms\n", "image 365/2210 /content/smartvision_dataset/detection/images/image_000364.jpg: 448x640 1 airplane, 219.7ms\n", "image 366/2210 /content/smartvision_dataset/detection/images/image_000365.jpg: 480x640 1 airplane, 247.0ms\n", "image 367/2210 /content/smartvision_dataset/detection/images/image_000366.jpg: 352x640 1 airplane, 184.4ms\n", "image 368/2210 /content/smartvision_dataset/detection/images/image_000367.jpg: 512x640 (no detections), 246.3ms\n", "image 369/2210 /content/smartvision_dataset/detection/images/image_000368.jpg: 448x640 1 airplane, 224.5ms\n", "image 370/2210 /content/smartvision_dataset/detection/images/image_000369.jpg: 448x640 (no detections), 226.2ms\n", "image 371/2210 /content/smartvision_dataset/detection/images/image_000370.jpg: 448x640 (no detections), 220.7ms\n", "image 372/2210 /content/smartvision_dataset/detection/images/image_000371.jpg: 448x640 (no detections), 219.0ms\n", "image 373/2210 /content/smartvision_dataset/detection/images/image_000372.jpg: 448x640 (no detections), 212.9ms\n", "image 374/2210 /content/smartvision_dataset/detection/images/image_000373.jpg: 480x640 12 persons, 238.6ms\n", "image 375/2210 /content/smartvision_dataset/detection/images/image_000374.jpg: 448x640 1 airplane, 235.4ms\n", "image 376/2210 /content/smartvision_dataset/detection/images/image_000375.jpg: 480x640 16 airplanes, 233.6ms\n", "image 377/2210 /content/smartvision_dataset/detection/images/image_000376.jpg: 480x640 16 airplanes, 184.6ms\n", "image 378/2210 /content/smartvision_dataset/detection/images/image_000377.jpg: 480x640 16 airplanes, 154.5ms\n", "image 379/2210 /content/smartvision_dataset/detection/images/image_000378.jpg: 480x640 16 airplanes, 157.8ms\n", "image 380/2210 /content/smartvision_dataset/detection/images/image_000379.jpg: 480x640 16 airplanes, 162.4ms\n", "image 381/2210 /content/smartvision_dataset/detection/images/image_000380.jpg: 480x640 16 airplanes, 151.5ms\n", "image 382/2210 /content/smartvision_dataset/detection/images/image_000381.jpg: 480x640 16 airplanes, 154.4ms\n", "image 383/2210 /content/smartvision_dataset/detection/images/image_000382.jpg: 640x640 1 airplane, 200.1ms\n", "image 384/2210 /content/smartvision_dataset/detection/images/image_000383.jpg: 480x640 6 airplanes, 152.2ms\n", "image 385/2210 /content/smartvision_dataset/detection/images/image_000384.jpg: 480x640 6 airplanes, 155.5ms\n", "image 386/2210 /content/smartvision_dataset/detection/images/image_000385.jpg: 480x640 6 airplanes, 170.3ms\n", "image 387/2210 /content/smartvision_dataset/detection/images/image_000386.jpg: 480x640 6 airplanes, 152.6ms\n", "image 388/2210 /content/smartvision_dataset/detection/images/image_000387.jpg: 448x640 1 airplane, 144.4ms\n", "image 389/2210 /content/smartvision_dataset/detection/images/image_000388.jpg: 448x640 (no detections), 149.3ms\n", "image 390/2210 /content/smartvision_dataset/detection/images/image_000389.jpg: 448x640 1 airplane, 140.7ms\n", "image 391/2210 /content/smartvision_dataset/detection/images/image_000390.jpg: 512x640 1 train, 164.2ms\n", "image 392/2210 /content/smartvision_dataset/detection/images/image_000391.jpg: 448x640 4 airplanes, 148.4ms\n", "image 393/2210 /content/smartvision_dataset/detection/images/image_000392.jpg: 448x640 4 airplanes, 158.2ms\n", "image 394/2210 /content/smartvision_dataset/detection/images/image_000393.jpg: 448x640 4 airplanes, 144.2ms\n", "image 395/2210 /content/smartvision_dataset/detection/images/image_000394.jpg: 448x640 4 airplanes, 146.3ms\n", "image 396/2210 /content/smartvision_dataset/detection/images/image_000395.jpg: 448x640 4 airplanes, 139.9ms\n", "image 397/2210 /content/smartvision_dataset/detection/images/image_000396.jpg: 640x448 (no detections), 142.6ms\n", "image 398/2210 /content/smartvision_dataset/detection/images/image_000397.jpg: 448x640 (no detections), 142.2ms\n", "image 399/2210 /content/smartvision_dataset/detection/images/image_000398.jpg: 448x640 (no detections), 155.2ms\n", "image 400/2210 /content/smartvision_dataset/detection/images/image_000399.jpg: 448x640 (no detections), 142.2ms\n", "image 401/2210 /content/smartvision_dataset/detection/images/image_000400.jpg: 448x640 (no detections), 150.0ms\n", "image 402/2210 /content/smartvision_dataset/detection/images/image_000401.jpg: 448x640 1 airplane, 140.9ms\n", "image 403/2210 /content/smartvision_dataset/detection/images/image_000402.jpg: 448x640 1 airplane, 143.3ms\n", "image 404/2210 /content/smartvision_dataset/detection/images/image_000403.jpg: 448x640 1 airplane, 142.7ms\n", "image 405/2210 /content/smartvision_dataset/detection/images/image_000404.jpg: 448x640 1 airplane, 141.5ms\n", "image 406/2210 /content/smartvision_dataset/detection/images/image_000405.jpg: 448x640 2 airplanes, 154.1ms\n", "image 407/2210 /content/smartvision_dataset/detection/images/image_000406.jpg: 448x640 12 persons, 143.9ms\n", "image 408/2210 /content/smartvision_dataset/detection/images/image_000407.jpg: 448x640 12 persons, 142.3ms\n", "image 409/2210 /content/smartvision_dataset/detection/images/image_000408.jpg: 448x640 12 persons, 141.5ms\n", "image 410/2210 /content/smartvision_dataset/detection/images/image_000409.jpg: 448x640 12 persons, 142.4ms\n", "image 411/2210 /content/smartvision_dataset/detection/images/image_000410.jpg: 448x640 12 persons, 140.4ms\n", "image 412/2210 /content/smartvision_dataset/detection/images/image_000411.jpg: 640x640 1 train, 210.8ms\n", "image 413/2210 /content/smartvision_dataset/detection/images/image_000412.jpg: 256x640 (no detections), 93.5ms\n", "image 414/2210 /content/smartvision_dataset/detection/images/image_000413.jpg: 640x480 (no detections), 149.6ms\n", "image 415/2210 /content/smartvision_dataset/detection/images/image_000414.jpg: 352x640 (no detections), 115.1ms\n", "image 416/2210 /content/smartvision_dataset/detection/images/image_000415.jpg: 640x384 2 airplanes, 127.1ms\n", "image 417/2210 /content/smartvision_dataset/detection/images/image_000416.jpg: 224x640 (no detections), 97.1ms\n", "image 418/2210 /content/smartvision_dataset/detection/images/image_000417.jpg: 480x640 1 airplane, 151.1ms\n", "image 419/2210 /content/smartvision_dataset/detection/images/image_000418.jpg: 480x640 2 airplanes, 152.3ms\n", "image 420/2210 /content/smartvision_dataset/detection/images/image_000419.jpg: 480x640 2 airplanes, 163.8ms\n", "image 421/2210 /content/smartvision_dataset/detection/images/image_000420.jpg: 480x640 2 airplanes, 152.8ms\n", "image 422/2210 /content/smartvision_dataset/detection/images/image_000421.jpg: 480x640 2 airplanes, 151.5ms\n", "image 423/2210 /content/smartvision_dataset/detection/images/image_000422.jpg: 480x640 2 airplanes, 156.3ms\n", "image 424/2210 /content/smartvision_dataset/detection/images/image_000423.jpg: 448x640 1 airplane, 143.6ms\n", "image 425/2210 /content/smartvision_dataset/detection/images/image_000424.jpg: 480x640 1 airplane, 153.6ms\n", "image 426/2210 /content/smartvision_dataset/detection/images/image_000425.jpg: 448x640 2 persons, 1 bus, 160.2ms\n", "image 427/2210 /content/smartvision_dataset/detection/images/image_000426.jpg: 448x640 1 bus, 144.1ms\n", "image 428/2210 /content/smartvision_dataset/detection/images/image_000427.jpg: 480x640 3 persons, 2 buss, 150.6ms\n", "image 429/2210 /content/smartvision_dataset/detection/images/image_000428.jpg: 480x640 3 persons, 2 buss, 163.7ms\n", "image 430/2210 /content/smartvision_dataset/detection/images/image_000429.jpg: 448x640 2 airplanes, 150.5ms\n", "image 431/2210 /content/smartvision_dataset/detection/images/image_000430.jpg: 448x640 2 airplanes, 145.7ms\n", "image 432/2210 /content/smartvision_dataset/detection/images/image_000431.jpg: 448x640 1 person, 1 bus, 164.9ms\n", "image 433/2210 /content/smartvision_dataset/detection/images/image_000432.jpg: 640x480 1 bus, 155.9ms\n", "image 434/2210 /content/smartvision_dataset/detection/images/image_000433.jpg: 448x640 1 bus, 150.1ms\n", "image 435/2210 /content/smartvision_dataset/detection/images/image_000434.jpg: 448x640 3 buss, 1 train, 1 potted plant, 147.2ms\n", "image 436/2210 /content/smartvision_dataset/detection/images/image_000435.jpg: 640x480 (no detections), 154.5ms\n", "image 437/2210 /content/smartvision_dataset/detection/images/image_000436.jpg: 640x480 4 persons, 3 bicycles, 1 motorcycle, 153.1ms\n", "image 438/2210 /content/smartvision_dataset/detection/images/image_000437.jpg: 480x640 (no detections), 167.1ms\n", "image 439/2210 /content/smartvision_dataset/detection/images/image_000438.jpg: 480x640 (no detections), 235.5ms\n", "image 440/2210 /content/smartvision_dataset/detection/images/image_000439.jpg: 480x640 (no detections), 257.8ms\n", "image 441/2210 /content/smartvision_dataset/detection/images/image_000440.jpg: 480x640 (no detections), 231.9ms\n", "image 442/2210 /content/smartvision_dataset/detection/images/image_000441.jpg: 640x640 1 bicycle, 1 bus, 312.1ms\n", "image 443/2210 /content/smartvision_dataset/detection/images/image_000442.jpg: 448x640 1 person, 1 bus, 222.0ms\n", "image 444/2210 /content/smartvision_dataset/detection/images/image_000443.jpg: 640x480 2 buss, 240.0ms\n", "image 445/2210 /content/smartvision_dataset/detection/images/image_000444.jpg: 640x480 2 buss, 233.9ms\n", "image 446/2210 /content/smartvision_dataset/detection/images/image_000445.jpg: 640x480 2 persons, 236.7ms\n", "image 447/2210 /content/smartvision_dataset/detection/images/image_000446.jpg: 480x640 (no detections), 237.0ms\n", "image 448/2210 /content/smartvision_dataset/detection/images/image_000447.jpg: 384x640 2 persons, 5 buss, 196.2ms\n", "image 449/2210 /content/smartvision_dataset/detection/images/image_000448.jpg: 384x640 2 persons, 5 buss, 197.8ms\n", "image 450/2210 /content/smartvision_dataset/detection/images/image_000449.jpg: 384x640 2 persons, 5 buss, 185.6ms\n", "image 451/2210 /content/smartvision_dataset/detection/images/image_000450.jpg: 384x640 2 persons, 5 buss, 207.6ms\n", "image 452/2210 /content/smartvision_dataset/detection/images/image_000451.jpg: 384x640 2 persons, 5 buss, 202.6ms\n", "image 453/2210 /content/smartvision_dataset/detection/images/image_000452.jpg: 448x640 2 buss, 223.9ms\n", "image 454/2210 /content/smartvision_dataset/detection/images/image_000453.jpg: 480x640 (no detections), 235.9ms\n", "image 455/2210 /content/smartvision_dataset/detection/images/image_000454.jpg: 448x640 1 bus, 170.1ms\n", "image 456/2210 /content/smartvision_dataset/detection/images/image_000455.jpg: 448x640 1 bus, 161.4ms\n", "image 457/2210 /content/smartvision_dataset/detection/images/image_000456.jpg: 416x640 24 persons, 136.2ms\n", "image 458/2210 /content/smartvision_dataset/detection/images/image_000457.jpg: 448x640 (no detections), 141.4ms\n", "image 459/2210 /content/smartvision_dataset/detection/images/image_000458.jpg: 480x640 13 persons, 1 train, 150.7ms\n", "image 460/2210 /content/smartvision_dataset/detection/images/image_000459.jpg: 640x512 10 persons, 1 bus, 165.5ms\n", "image 461/2210 /content/smartvision_dataset/detection/images/image_000460.jpg: 160x640 1 bus, 70.5ms\n", "image 462/2210 /content/smartvision_dataset/detection/images/image_000461.jpg: 160x640 1 bus, 59.2ms\n", "image 463/2210 /content/smartvision_dataset/detection/images/image_000462.jpg: 160x640 1 bus, 58.9ms\n", "image 464/2210 /content/smartvision_dataset/detection/images/image_000463.jpg: 160x640 1 bus, 59.1ms\n", "image 465/2210 /content/smartvision_dataset/detection/images/image_000464.jpg: 160x640 1 bus, 62.3ms\n", "image 466/2210 /content/smartvision_dataset/detection/images/image_000465.jpg: 384x640 4 persons, 8 cars, 1 bus, 130.2ms\n", "image 467/2210 /content/smartvision_dataset/detection/images/image_000466.jpg: 480x640 1 bus, 148.9ms\n", "image 468/2210 /content/smartvision_dataset/detection/images/image_000467.jpg: 480x640 1 bicycle, 1 bus, 152.1ms\n", "image 469/2210 /content/smartvision_dataset/detection/images/image_000468.jpg: 448x640 4 persons, 6 bicycles, 140.2ms\n", "image 470/2210 /content/smartvision_dataset/detection/images/image_000469.jpg: 480x640 3 buss, 149.4ms\n", "image 471/2210 /content/smartvision_dataset/detection/images/image_000470.jpg: 480x640 3 buss, 148.3ms\n", "image 472/2210 /content/smartvision_dataset/detection/images/image_000471.jpg: 480x640 3 buss, 162.0ms\n", "image 473/2210 /content/smartvision_dataset/detection/images/image_000472.jpg: 416x640 1 bus, 134.0ms\n", "image 474/2210 /content/smartvision_dataset/detection/images/image_000473.jpg: 480x640 1 bus, 152.1ms\n", "image 475/2210 /content/smartvision_dataset/detection/images/image_000474.jpg: 480x640 6 persons, 2 buss, 150.6ms\n", "image 476/2210 /content/smartvision_dataset/detection/images/image_000475.jpg: 448x640 (no detections), 147.1ms\n", "image 477/2210 /content/smartvision_dataset/detection/images/image_000476.jpg: 480x640 1 person, 1 bus, 153.4ms\n", "image 478/2210 /content/smartvision_dataset/detection/images/image_000477.jpg: 480x640 1 bus, 163.9ms\n", "image 479/2210 /content/smartvision_dataset/detection/images/image_000478.jpg: 384x640 4 persons, 3 bicycles, 3 buss, 127.0ms\n", "image 480/2210 /content/smartvision_dataset/detection/images/image_000479.jpg: 384x640 4 persons, 3 bicycles, 3 buss, 126.7ms\n", "image 481/2210 /content/smartvision_dataset/detection/images/image_000480.jpg: 384x640 4 persons, 3 bicycles, 3 buss, 123.0ms\n", "image 482/2210 /content/smartvision_dataset/detection/images/image_000481.jpg: 384x640 4 persons, 3 bicycles, 3 buss, 125.3ms\n", "image 483/2210 /content/smartvision_dataset/detection/images/image_000482.jpg: 384x640 4 persons, 3 bicycles, 3 buss, 124.7ms\n", "image 484/2210 /content/smartvision_dataset/detection/images/image_000483.jpg: 384x640 4 persons, 3 bicycles, 3 buss, 127.9ms\n", "image 485/2210 /content/smartvision_dataset/detection/images/image_000484.jpg: 384x640 4 persons, 3 bicycles, 3 buss, 125.3ms\n", "image 486/2210 /content/smartvision_dataset/detection/images/image_000485.jpg: 384x640 4 persons, 3 bicycles, 3 buss, 137.9ms\n", "image 487/2210 /content/smartvision_dataset/detection/images/image_000486.jpg: 384x640 4 persons, 3 bicycles, 3 buss, 126.5ms\n", "image 488/2210 /content/smartvision_dataset/detection/images/image_000487.jpg: 384x640 4 persons, 3 bicycles, 3 buss, 122.3ms\n", "image 489/2210 /content/smartvision_dataset/detection/images/image_000488.jpg: 384x640 4 persons, 3 bicycles, 3 buss, 122.6ms\n", "image 490/2210 /content/smartvision_dataset/detection/images/image_000489.jpg: 384x640 4 persons, 3 bicycles, 3 buss, 126.6ms\n", "image 491/2210 /content/smartvision_dataset/detection/images/image_000490.jpg: 384x640 4 persons, 3 bicycles, 3 buss, 129.0ms\n", "image 492/2210 /content/smartvision_dataset/detection/images/image_000491.jpg: 480x640 1 person, 1 bus, 153.3ms\n", "image 493/2210 /content/smartvision_dataset/detection/images/image_000492.jpg: 640x640 8 persons, 209.9ms\n", "image 494/2210 /content/smartvision_dataset/detection/images/image_000493.jpg: 640x640 8 persons, 194.4ms\n", "image 495/2210 /content/smartvision_dataset/detection/images/image_000494.jpg: 448x640 2 buss, 143.3ms\n", "image 496/2210 /content/smartvision_dataset/detection/images/image_000495.jpg: 480x640 5 buss, 151.1ms\n", "image 497/2210 /content/smartvision_dataset/detection/images/image_000496.jpg: 480x640 5 buss, 149.5ms\n", "image 498/2210 /content/smartvision_dataset/detection/images/image_000497.jpg: 480x640 5 buss, 152.0ms\n", "image 499/2210 /content/smartvision_dataset/detection/images/image_000498.jpg: 480x640 5 buss, 155.9ms\n", "image 500/2210 /content/smartvision_dataset/detection/images/image_000499.jpg: 480x640 3 buss, 153.4ms\n", "image 501/2210 /content/smartvision_dataset/detection/images/image_000500.jpg: 480x640 3 buss, 157.8ms\n", "image 502/2210 /content/smartvision_dataset/detection/images/image_000501.jpg: 640x448 1 person, 1 bicycle, 1 bus, 144.1ms\n", "image 503/2210 /content/smartvision_dataset/detection/images/image_000502.jpg: 640x448 1 bus, 143.4ms\n", "image 504/2210 /content/smartvision_dataset/detection/images/image_000503.jpg: 640x480 5 persons, 1 train, 152.2ms\n", "image 505/2210 /content/smartvision_dataset/detection/images/image_000504.jpg: 480x640 (no detections), 162.7ms\n", "image 506/2210 /content/smartvision_dataset/detection/images/image_000505.jpg: 480x640 1 bus, 152.9ms\n", "image 507/2210 /content/smartvision_dataset/detection/images/image_000506.jpg: 448x640 1 train, 146.2ms\n", "image 508/2210 /content/smartvision_dataset/detection/images/image_000507.jpg: 448x640 1 train, 144.5ms\n", "image 509/2210 /content/smartvision_dataset/detection/images/image_000508.jpg: 448x640 1 train, 161.3ms\n", "image 510/2210 /content/smartvision_dataset/detection/images/image_000509.jpg: 448x640 1 train, 142.9ms\n", "image 511/2210 /content/smartvision_dataset/detection/images/image_000510.jpg: 448x640 4 trains, 154.5ms\n", "image 512/2210 /content/smartvision_dataset/detection/images/image_000511.jpg: 640x480 (no detections), 150.9ms\n", "image 513/2210 /content/smartvision_dataset/detection/images/image_000512.jpg: 480x640 1 train, 153.4ms\n", "image 514/2210 /content/smartvision_dataset/detection/images/image_000513.jpg: 448x640 3 persons, 1 train, 142.4ms\n", "image 515/2210 /content/smartvision_dataset/detection/images/image_000514.jpg: 448x640 4 persons, 2 trains, 142.4ms\n", "image 516/2210 /content/smartvision_dataset/detection/images/image_000515.jpg: 448x640 4 persons, 2 trains, 143.2ms\n", "image 517/2210 /content/smartvision_dataset/detection/images/image_000516.jpg: 512x640 2 trains, 165.8ms\n", "image 518/2210 /content/smartvision_dataset/detection/images/image_000517.jpg: 640x448 1 train, 153.8ms\n", "image 519/2210 /content/smartvision_dataset/detection/images/image_000518.jpg: 448x640 1 train, 145.7ms\n", "image 520/2210 /content/smartvision_dataset/detection/images/image_000519.jpg: 448x640 (no detections), 186.7ms\n", "image 521/2210 /content/smartvision_dataset/detection/images/image_000520.jpg: 480x640 (no detections), 242.0ms\n", "image 522/2210 /content/smartvision_dataset/detection/images/image_000521.jpg: 448x640 1 train, 222.1ms\n", "image 523/2210 /content/smartvision_dataset/detection/images/image_000522.jpg: 448x640 1 train, 250.1ms\n", "image 524/2210 /content/smartvision_dataset/detection/images/image_000523.jpg: 448x640 1 train, 211.0ms\n", "image 525/2210 /content/smartvision_dataset/detection/images/image_000524.jpg: 448x640 1 train, 217.2ms\n", "image 526/2210 /content/smartvision_dataset/detection/images/image_000525.jpg: 480x640 1 train, 232.8ms\n", "image 527/2210 /content/smartvision_dataset/detection/images/image_000526.jpg: 480x640 3 persons, 2 bicycles, 235.0ms\n", "image 528/2210 /content/smartvision_dataset/detection/images/image_000527.jpg: 480x640 1 train, 236.4ms\n", "image 529/2210 /content/smartvision_dataset/detection/images/image_000528.jpg: 480x640 2 trains, 233.2ms\n", "image 530/2210 /content/smartvision_dataset/detection/images/image_000529.jpg: 416x640 6 trains, 206.3ms\n", "image 531/2210 /content/smartvision_dataset/detection/images/image_000530.jpg: 416x640 6 trains, 206.8ms\n", "image 532/2210 /content/smartvision_dataset/detection/images/image_000531.jpg: 416x640 6 trains, 217.8ms\n", "image 533/2210 /content/smartvision_dataset/detection/images/image_000532.jpg: 416x640 6 trains, 209.6ms\n", "image 534/2210 /content/smartvision_dataset/detection/images/image_000533.jpg: 416x640 6 trains, 213.3ms\n", "image 535/2210 /content/smartvision_dataset/detection/images/image_000534.jpg: 416x640 6 trains, 211.5ms\n", "image 536/2210 /content/smartvision_dataset/detection/images/image_000535.jpg: 416x640 6 trains, 216.1ms\n", "image 537/2210 /content/smartvision_dataset/detection/images/image_000536.jpg: 640x448 3 trains, 204.2ms\n", "image 538/2210 /content/smartvision_dataset/detection/images/image_000537.jpg: 640x448 3 trains, 148.1ms\n", "image 539/2210 /content/smartvision_dataset/detection/images/image_000538.jpg: 480x640 3 trains, 152.8ms\n", "image 540/2210 /content/smartvision_dataset/detection/images/image_000539.jpg: 480x640 3 trains, 148.3ms\n", "image 541/2210 /content/smartvision_dataset/detection/images/image_000540.jpg: 640x640 1 train, 196.7ms\n", "image 542/2210 /content/smartvision_dataset/detection/images/image_000541.jpg: 448x640 2 trains, 157.4ms\n", "image 543/2210 /content/smartvision_dataset/detection/images/image_000542.jpg: 448x640 2 trains, 143.2ms\n", "image 544/2210 /content/smartvision_dataset/detection/images/image_000543.jpg: 448x640 2 trains, 145.2ms\n", "image 545/2210 /content/smartvision_dataset/detection/images/image_000544.jpg: 416x640 1 train, 135.0ms\n", "image 546/2210 /content/smartvision_dataset/detection/images/image_000545.jpg: 416x640 24 persons, 134.5ms\n", "image 547/2210 /content/smartvision_dataset/detection/images/image_000546.jpg: 480x640 2 trains, 150.1ms\n", "image 548/2210 /content/smartvision_dataset/detection/images/image_000547.jpg: 512x640 5 persons, 163.1ms\n", "image 549/2210 /content/smartvision_dataset/detection/images/image_000548.jpg: 640x448 1 person, 155.7ms\n", "image 550/2210 /content/smartvision_dataset/detection/images/image_000549.jpg: 640x448 (no detections), 144.0ms\n", "image 551/2210 /content/smartvision_dataset/detection/images/image_000550.jpg: 480x640 2 trains, 151.3ms\n", "image 552/2210 /content/smartvision_dataset/detection/images/image_000551.jpg: 480x640 2 buss, 1 train, 151.0ms\n", "image 553/2210 /content/smartvision_dataset/detection/images/image_000552.jpg: 448x640 1 train, 145.3ms\n", "image 554/2210 /content/smartvision_dataset/detection/images/image_000553.jpg: 448x640 1 train, 145.3ms\n", "image 555/2210 /content/smartvision_dataset/detection/images/image_000554.jpg: 448x640 4 trains, 157.6ms\n", "image 556/2210 /content/smartvision_dataset/detection/images/image_000555.jpg: 448x640 4 trains, 153.7ms\n", "image 557/2210 /content/smartvision_dataset/detection/images/image_000556.jpg: 448x640 1 train, 144.0ms\n", "image 558/2210 /content/smartvision_dataset/detection/images/image_000557.jpg: 384x640 4 trains, 131.3ms\n", "image 559/2210 /content/smartvision_dataset/detection/images/image_000558.jpg: 384x640 4 trains, 136.1ms\n", "image 560/2210 /content/smartvision_dataset/detection/images/image_000559.jpg: 640x640 10 persons, 202.9ms\n", "image 561/2210 /content/smartvision_dataset/detection/images/image_000560.jpg: 640x480 2 trains, 169.0ms\n", "image 562/2210 /content/smartvision_dataset/detection/images/image_000561.jpg: 384x640 4 trains, 126.4ms\n", "image 563/2210 /content/smartvision_dataset/detection/images/image_000562.jpg: 480x640 3 trains, 154.4ms\n", "image 564/2210 /content/smartvision_dataset/detection/images/image_000563.jpg: 512x640 (no detections), 163.0ms\n", "image 565/2210 /content/smartvision_dataset/detection/images/image_000564.jpg: 448x640 1 train, 149.2ms\n", "image 566/2210 /content/smartvision_dataset/detection/images/image_000565.jpg: 448x640 (no detections), 147.2ms\n", "image 567/2210 /content/smartvision_dataset/detection/images/image_000566.jpg: 448x640 7 persons, 1 bicycle, 1 train, 144.7ms\n", "image 568/2210 /content/smartvision_dataset/detection/images/image_000567.jpg: 448x640 (no detections), 153.9ms\n", "image 569/2210 /content/smartvision_dataset/detection/images/image_000568.jpg: 416x640 2 trains, 133.5ms\n", "image 570/2210 /content/smartvision_dataset/detection/images/image_000569.jpg: 416x640 2 trains, 131.9ms\n", "image 571/2210 /content/smartvision_dataset/detection/images/image_000570.jpg: 384x640 1 train, 126.0ms\n", "image 572/2210 /content/smartvision_dataset/detection/images/image_000571.jpg: 384x640 1 train, 131.1ms\n", "image 573/2210 /content/smartvision_dataset/detection/images/image_000572.jpg: 448x640 1 train, 140.6ms\n", "image 574/2210 /content/smartvision_dataset/detection/images/image_000573.jpg: 416x640 4 trains, 135.1ms\n", "image 575/2210 /content/smartvision_dataset/detection/images/image_000574.jpg: 416x640 4 trains, 145.5ms\n", "image 576/2210 /content/smartvision_dataset/detection/images/image_000575.jpg: 448x640 1 train, 143.3ms\n", "image 577/2210 /content/smartvision_dataset/detection/images/image_000576.jpg: 448x640 7 persons, 1 train, 141.8ms\n", "image 578/2210 /content/smartvision_dataset/detection/images/image_000577.jpg: 480x640 2 trains, 154.5ms\n", "image 579/2210 /content/smartvision_dataset/detection/images/image_000578.jpg: 448x640 (no detections), 143.1ms\n", "image 580/2210 /content/smartvision_dataset/detection/images/image_000579.jpg: 448x640 1 bus, 2 trains, 146.4ms\n", "image 581/2210 /content/smartvision_dataset/detection/images/image_000580.jpg: 448x640 1 person, 160.9ms\n", "image 582/2210 /content/smartvision_dataset/detection/images/image_000581.jpg: 448x640 7 persons, 144.4ms\n", "image 583/2210 /content/smartvision_dataset/detection/images/image_000582.jpg: 448x640 7 persons, 144.8ms\n", "image 584/2210 /content/smartvision_dataset/detection/images/image_000583.jpg: 448x640 1 train, 143.8ms\n", "image 585/2210 /content/smartvision_dataset/detection/images/image_000584.jpg: 480x640 (no detections), 153.6ms\n", "image 586/2210 /content/smartvision_dataset/detection/images/image_000585.jpg: 448x640 1 bus, 139.7ms\n", "image 587/2210 /content/smartvision_dataset/detection/images/image_000586.jpg: 480x640 7 persons, 1 train, 150.7ms\n", "image 588/2210 /content/smartvision_dataset/detection/images/image_000587.jpg: 448x640 3 trains, 151.8ms\n", "image 589/2210 /content/smartvision_dataset/detection/images/image_000588.jpg: 448x640 9 persons, 4 trains, 141.5ms\n", "image 590/2210 /content/smartvision_dataset/detection/images/image_000589.jpg: 448x640 9 persons, 4 trains, 141.6ms\n", "image 591/2210 /content/smartvision_dataset/detection/images/image_000590.jpg: 448x640 1 train, 144.2ms\n", "image 592/2210 /content/smartvision_dataset/detection/images/image_000591.jpg: 384x640 1 train, 130.1ms\n", "image 593/2210 /content/smartvision_dataset/detection/images/image_000592.jpg: 448x640 2 persons, 143.1ms\n", "image 594/2210 /content/smartvision_dataset/detection/images/image_000593.jpg: 448x640 2 persons, 159.8ms\n", "image 595/2210 /content/smartvision_dataset/detection/images/image_000594.jpg: 448x640 2 persons, 148.0ms\n", "image 596/2210 /content/smartvision_dataset/detection/images/image_000595.jpg: 640x480 1 person, 1 car, 152.6ms\n", "image 597/2210 /content/smartvision_dataset/detection/images/image_000596.jpg: 448x640 4 trains, 141.7ms\n", "image 598/2210 /content/smartvision_dataset/detection/images/image_000597.jpg: 448x640 4 trains, 145.1ms\n", "image 599/2210 /content/smartvision_dataset/detection/images/image_000598.jpg: 448x640 (no detections), 178.9ms\n", "image 600/2210 /content/smartvision_dataset/detection/images/image_000599.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 274.1ms\n", "image 601/2210 /content/smartvision_dataset/detection/images/image_000600.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 245.9ms\n", "image 602/2210 /content/smartvision_dataset/detection/images/image_000601.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 243.7ms\n", "image 603/2210 /content/smartvision_dataset/detection/images/image_000602.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 231.2ms\n", "image 604/2210 /content/smartvision_dataset/detection/images/image_000603.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 241.7ms\n", "image 605/2210 /content/smartvision_dataset/detection/images/image_000604.jpg: 448x640 2 airplanes, 224.8ms\n", "image 606/2210 /content/smartvision_dataset/detection/images/image_000605.jpg: 448x640 2 airplanes, 223.1ms\n", "image 607/2210 /content/smartvision_dataset/detection/images/image_000606.jpg: 448x640 2 airplanes, 216.7ms\n", "image 608/2210 /content/smartvision_dataset/detection/images/image_000607.jpg: 576x640 2 persons, 274.7ms\n", "image 609/2210 /content/smartvision_dataset/detection/images/image_000608.jpg: 640x448 (no detections), 231.2ms\n", "image 610/2210 /content/smartvision_dataset/detection/images/image_000609.jpg: 448x640 (no detections), 212.8ms\n", "image 611/2210 /content/smartvision_dataset/detection/images/image_000610.jpg: 640x480 4 persons, 3 bicycles, 1 motorcycle, 231.3ms\n", "image 612/2210 /content/smartvision_dataset/detection/images/image_000611.jpg: 480x640 (no detections), 240.7ms\n", "image 613/2210 /content/smartvision_dataset/detection/images/image_000612.jpg: 448x640 5 cows, 231.6ms\n", "image 614/2210 /content/smartvision_dataset/detection/images/image_000613.jpg: 480x640 (no detections), 243.8ms\n", "image 615/2210 /content/smartvision_dataset/detection/images/image_000614.jpg: 480x640 (no detections), 204.7ms\n", "image 616/2210 /content/smartvision_dataset/detection/images/image_000615.jpg: 448x640 3 persons, 147.7ms\n", "image 617/2210 /content/smartvision_dataset/detection/images/image_000616.jpg: 640x480 (no detections), 153.1ms\n", "image 618/2210 /content/smartvision_dataset/detection/images/image_000617.jpg: 448x640 1 airplane, 157.0ms\n", "image 619/2210 /content/smartvision_dataset/detection/images/image_000618.jpg: 448x640 2 bicycles, 144.4ms\n", "image 620/2210 /content/smartvision_dataset/detection/images/image_000619.jpg: 480x640 (no detections), 156.5ms\n", "image 621/2210 /content/smartvision_dataset/detection/images/image_000620.jpg: 480x640 2 persons, 1 bus, 162.5ms\n", "image 622/2210 /content/smartvision_dataset/detection/images/image_000621.jpg: 480x640 (no detections), 157.2ms\n", "image 623/2210 /content/smartvision_dataset/detection/images/image_000622.jpg: 480x640 (no detections), 149.7ms\n", "image 624/2210 /content/smartvision_dataset/detection/images/image_000623.jpg: 384x640 (no detections), 126.4ms\n", "image 625/2210 /content/smartvision_dataset/detection/images/image_000624.jpg: 480x640 1 airplane, 168.8ms\n", "image 626/2210 /content/smartvision_dataset/detection/images/image_000625.jpg: 448x640 (no detections), 140.0ms\n", "image 627/2210 /content/smartvision_dataset/detection/images/image_000626.jpg: 448x640 1 person, 12 bicycles, 140.8ms\n", "image 628/2210 /content/smartvision_dataset/detection/images/image_000627.jpg: 448x640 1 person, 12 bicycles, 146.0ms\n", "image 629/2210 /content/smartvision_dataset/detection/images/image_000628.jpg: 352x640 (no detections), 116.0ms\n", "image 630/2210 /content/smartvision_dataset/detection/images/image_000629.jpg: 480x640 1 person, 1 car, 1 truck, 3 dogs, 149.6ms\n", "image 631/2210 /content/smartvision_dataset/detection/images/image_000630.jpg: 480x640 3 persons, 170.3ms\n", "image 632/2210 /content/smartvision_dataset/detection/images/image_000631.jpg: 640x448 4 persons, 142.1ms\n", "image 633/2210 /content/smartvision_dataset/detection/images/image_000632.jpg: 512x640 (no detections), 164.3ms\n", "image 634/2210 /content/smartvision_dataset/detection/images/image_000633.jpg: 480x640 (no detections), 160.0ms\n", "image 635/2210 /content/smartvision_dataset/detection/images/image_000634.jpg: 448x640 1 airplane, 147.0ms\n", "image 636/2210 /content/smartvision_dataset/detection/images/image_000635.jpg: 448x640 1 airplane, 145.0ms\n", "image 637/2210 /content/smartvision_dataset/detection/images/image_000636.jpg: 448x640 1 airplane, 149.1ms\n", "image 638/2210 /content/smartvision_dataset/detection/images/image_000637.jpg: 384x640 1 bus, 125.1ms\n", "image 639/2210 /content/smartvision_dataset/detection/images/image_000638.jpg: 384x640 1 bus, 130.5ms\n", "image 640/2210 /content/smartvision_dataset/detection/images/image_000639.jpg: 448x640 1 bus, 142.3ms\n", "image 641/2210 /content/smartvision_dataset/detection/images/image_000640.jpg: 640x480 1 car, 155.1ms\n", "image 642/2210 /content/smartvision_dataset/detection/images/image_000641.jpg: 480x640 (no detections), 151.0ms\n", "image 643/2210 /content/smartvision_dataset/detection/images/image_000642.jpg: 448x640 1 person, 148.7ms\n", "image 644/2210 /content/smartvision_dataset/detection/images/image_000643.jpg: 416x640 24 persons, 167.0ms\n", "image 645/2210 /content/smartvision_dataset/detection/images/image_000644.jpg: 480x640 (no detections), 152.4ms\n", "image 646/2210 /content/smartvision_dataset/detection/images/image_000645.jpg: 640x512 10 persons, 1 bus, 162.2ms\n", "image 647/2210 /content/smartvision_dataset/detection/images/image_000646.jpg: 640x448 1 person, 147.5ms\n", "image 648/2210 /content/smartvision_dataset/detection/images/image_000647.jpg: 640x480 3 buss, 1 train, 154.1ms\n", "image 649/2210 /content/smartvision_dataset/detection/images/image_000648.jpg: 640x416 (no detections), 136.8ms\n", "image 650/2210 /content/smartvision_dataset/detection/images/image_000649.jpg: 384x640 4 persons, 8 cars, 1 bus, 136.4ms\n", "image 651/2210 /content/smartvision_dataset/detection/images/image_000650.jpg: 384x640 4 persons, 8 cars, 1 bus, 131.4ms\n", "image 652/2210 /content/smartvision_dataset/detection/images/image_000651.jpg: 448x640 1 car, 1 truck, 1 cat, 141.8ms\n", "image 653/2210 /content/smartvision_dataset/detection/images/image_000652.jpg: 640x448 4 persons, 146.0ms\n", "image 654/2210 /content/smartvision_dataset/detection/images/image_000653.jpg: 448x640 12 persons, 2 bicycles, 139.8ms\n", "image 655/2210 /content/smartvision_dataset/detection/images/image_000654.jpg: 480x640 (no detections), 152.2ms\n", "image 656/2210 /content/smartvision_dataset/detection/images/image_000655.jpg: 416x640 (no detections), 135.1ms\n", "image 657/2210 /content/smartvision_dataset/detection/images/image_000656.jpg: 448x640 2 horses, 158.4ms\n", "image 658/2210 /content/smartvision_dataset/detection/images/image_000657.jpg: 480x640 1 airplane, 152.6ms\n", "image 659/2210 /content/smartvision_dataset/detection/images/image_000658.jpg: 416x640 1 bus, 136.1ms\n", "image 660/2210 /content/smartvision_dataset/detection/images/image_000659.jpg: 640x640 1 airplane, 202.6ms\n", "image 661/2210 /content/smartvision_dataset/detection/images/image_000660.jpg: 640x640 1 airplane, 199.0ms\n", "image 662/2210 /content/smartvision_dataset/detection/images/image_000661.jpg: 640x640 1 airplane, 203.8ms\n", "image 663/2210 /content/smartvision_dataset/detection/images/image_000662.jpg: 448x640 7 persons, 150.6ms\n", "image 664/2210 /content/smartvision_dataset/detection/images/image_000663.jpg: 448x640 1 airplane, 143.6ms\n", "image 665/2210 /content/smartvision_dataset/detection/images/image_000664.jpg: 480x640 4 persons, 154.1ms\n", "image 666/2210 /content/smartvision_dataset/detection/images/image_000665.jpg: 640x640 2 persons, 1 bicycle, 2 motorcycles, 199.1ms\n", "image 667/2210 /content/smartvision_dataset/detection/images/image_000666.jpg: 416x640 2 persons, 4 cars, 2 buss, 135.2ms\n", "image 668/2210 /content/smartvision_dataset/detection/images/image_000667.jpg: 416x640 2 persons, 4 cars, 2 buss, 130.2ms\n", "image 669/2210 /content/smartvision_dataset/detection/images/image_000668.jpg: 448x640 5 persons, 159.3ms\n", "image 670/2210 /content/smartvision_dataset/detection/images/image_000669.jpg: 448x640 5 persons, 150.8ms\n", "image 671/2210 /content/smartvision_dataset/detection/images/image_000670.jpg: 480x640 2 buss, 1 train, 154.1ms\n", "image 672/2210 /content/smartvision_dataset/detection/images/image_000671.jpg: 512x640 1 train, 169.3ms\n", "image 673/2210 /content/smartvision_dataset/detection/images/image_000672.jpg: 512x640 1 train, 163.5ms\n", "image 674/2210 /content/smartvision_dataset/detection/images/image_000673.jpg: 512x640 1 train, 161.0ms\n", "image 675/2210 /content/smartvision_dataset/detection/images/image_000674.jpg: 512x640 1 train, 204.6ms\n", "image 676/2210 /content/smartvision_dataset/detection/images/image_000675.jpg: 480x640 3 persons, 2 bicycles, 2 airplanes, 247.4ms\n", "image 677/2210 /content/smartvision_dataset/detection/images/image_000676.jpg: 640x640 8 persons, 349.2ms\n", "image 678/2210 /content/smartvision_dataset/detection/images/image_000677.jpg: 640x480 (no detections), 287.7ms\n", "image 679/2210 /content/smartvision_dataset/detection/images/image_000678.jpg: 640x480 (no detections), 266.0ms\n", "image 680/2210 /content/smartvision_dataset/detection/images/image_000679.jpg: 640x448 1 bus, 269.9ms\n", "image 681/2210 /content/smartvision_dataset/detection/images/image_000680.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 292.7ms\n", "image 682/2210 /content/smartvision_dataset/detection/images/image_000681.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 324.4ms\n", "image 683/2210 /content/smartvision_dataset/detection/images/image_000682.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 261.5ms\n", "image 684/2210 /content/smartvision_dataset/detection/images/image_000683.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 235.7ms\n", "image 685/2210 /content/smartvision_dataset/detection/images/image_000684.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 237.1ms\n", "image 686/2210 /content/smartvision_dataset/detection/images/image_000685.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 239.3ms\n", "image 687/2210 /content/smartvision_dataset/detection/images/image_000686.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 230.7ms\n", "image 688/2210 /content/smartvision_dataset/detection/images/image_000687.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 246.1ms\n", "image 689/2210 /content/smartvision_dataset/detection/images/image_000688.jpg: 480x640 4 persons, 3 bicycles, 2 buss, 235.1ms\n", "image 690/2210 /content/smartvision_dataset/detection/images/image_000689.jpg: 448x640 (no detections), 238.8ms\n", "image 691/2210 /content/smartvision_dataset/detection/images/image_000690.jpg: 448x640 (no detections), 183.4ms\n", "image 692/2210 /content/smartvision_dataset/detection/images/image_000691.jpg: 448x640 (no detections), 152.3ms\n", "image 693/2210 /content/smartvision_dataset/detection/images/image_000692.jpg: 480x640 4 persons, 1 bench, 152.2ms\n", "image 694/2210 /content/smartvision_dataset/detection/images/image_000693.jpg: 448x640 2 airplanes, 143.2ms\n", "image 695/2210 /content/smartvision_dataset/detection/images/image_000694.jpg: 448x640 (no detections), 143.9ms\n", "image 696/2210 /content/smartvision_dataset/detection/images/image_000695.jpg: 448x640 (no detections), 156.3ms\n", "image 697/2210 /content/smartvision_dataset/detection/images/image_000696.jpg: 448x640 (no detections), 145.8ms\n", "image 698/2210 /content/smartvision_dataset/detection/images/image_000697.jpg: 512x640 2 trains, 165.1ms\n", "image 699/2210 /content/smartvision_dataset/detection/images/image_000698.jpg: 448x640 7 persons, 8 bicycles, 144.6ms\n", "image 700/2210 /content/smartvision_dataset/detection/images/image_000699.jpg: 448x640 7 persons, 8 bicycles, 142.6ms\n", "image 701/2210 /content/smartvision_dataset/detection/images/image_000700.jpg: 448x640 1 train, 147.8ms\n", "image 702/2210 /content/smartvision_dataset/detection/images/image_000701.jpg: 448x640 1 train, 140.1ms\n", "image 703/2210 /content/smartvision_dataset/detection/images/image_000702.jpg: 480x640 (no detections), 168.2ms\n", "image 704/2210 /content/smartvision_dataset/detection/images/image_000703.jpg: 480x640 (no detections), 149.0ms\n", "image 705/2210 /content/smartvision_dataset/detection/images/image_000704.jpg: 480x640 (no detections), 156.5ms\n", "image 706/2210 /content/smartvision_dataset/detection/images/image_000705.jpg: 480x640 (no detections), 152.0ms\n", "image 707/2210 /content/smartvision_dataset/detection/images/image_000706.jpg: 480x640 (no detections), 155.7ms\n", "image 708/2210 /content/smartvision_dataset/detection/images/image_000707.jpg: 480x640 (no detections), 152.5ms\n", "image 709/2210 /content/smartvision_dataset/detection/images/image_000708.jpg: 448x640 2 persons, 1 motorcycle, 1 dog, 157.5ms\n", "image 710/2210 /content/smartvision_dataset/detection/images/image_000709.jpg: 480x640 1 train, 156.3ms\n", "image 711/2210 /content/smartvision_dataset/detection/images/image_000710.jpg: 480x640 1 train, 159.4ms\n", "image 712/2210 /content/smartvision_dataset/detection/images/image_000711.jpg: 480x640 1 train, 154.8ms\n", "image 713/2210 /content/smartvision_dataset/detection/images/image_000712.jpg: 480x640 (no detections), 159.2ms\n", "image 714/2210 /content/smartvision_dataset/detection/images/image_000713.jpg: 480x640 (no detections), 156.7ms\n", "image 715/2210 /content/smartvision_dataset/detection/images/image_000714.jpg: 480x640 (no detections), 168.9ms\n", "image 716/2210 /content/smartvision_dataset/detection/images/image_000715.jpg: 352x640 (no detections), 117.4ms\n", "image 717/2210 /content/smartvision_dataset/detection/images/image_000716.jpg: 352x640 (no detections), 123.8ms\n", "image 718/2210 /content/smartvision_dataset/detection/images/image_000717.jpg: 512x640 (no detections), 193.0ms\n", "image 719/2210 /content/smartvision_dataset/detection/images/image_000718.jpg: 512x640 (no detections), 236.9ms\n", "image 720/2210 /content/smartvision_dataset/detection/images/image_000719.jpg: 480x640 (no detections), 304.5ms\n", "image 721/2210 /content/smartvision_dataset/detection/images/image_000720.jpg: 480x640 (no detections), 304.2ms\n", "image 722/2210 /content/smartvision_dataset/detection/images/image_000721.jpg: 480x640 (no detections), 262.9ms\n", "image 723/2210 /content/smartvision_dataset/detection/images/image_000722.jpg: 480x640 (no detections), 251.4ms\n", "image 724/2210 /content/smartvision_dataset/detection/images/image_000723.jpg: 480x640 (no detections), 340.9ms\n", "image 725/2210 /content/smartvision_dataset/detection/images/image_000724.jpg: 448x640 1 bus, 265.5ms\n", "image 726/2210 /content/smartvision_dataset/detection/images/image_000725.jpg: 448x640 1 bus, 245.1ms\n", "image 727/2210 /content/smartvision_dataset/detection/images/image_000726.jpg: 448x640 1 bus, 203.7ms\n", "image 728/2210 /content/smartvision_dataset/detection/images/image_000727.jpg: 480x640 (no detections), 167.4ms\n", "image 729/2210 /content/smartvision_dataset/detection/images/image_000728.jpg: 480x640 (no detections), 149.1ms\n", "image 730/2210 /content/smartvision_dataset/detection/images/image_000729.jpg: 640x512 1 person, 1 car, 166.7ms\n", "image 731/2210 /content/smartvision_dataset/detection/images/image_000730.jpg: 640x512 1 person, 1 car, 163.0ms\n", "image 732/2210 /content/smartvision_dataset/detection/images/image_000731.jpg: 640x512 1 person, 1 car, 164.9ms\n", "image 733/2210 /content/smartvision_dataset/detection/images/image_000732.jpg: 640x512 1 person, 1 car, 166.4ms\n", "image 734/2210 /content/smartvision_dataset/detection/images/image_000733.jpg: 640x512 10 persons, 1 bus, 172.7ms\n", "image 735/2210 /content/smartvision_dataset/detection/images/image_000734.jpg: 480x640 (no detections), 154.5ms\n", "image 736/2210 /content/smartvision_dataset/detection/images/image_000735.jpg: 480x640 (no detections), 156.8ms\n", "image 737/2210 /content/smartvision_dataset/detection/images/image_000736.jpg: 480x640 (no detections), 151.4ms\n", "image 738/2210 /content/smartvision_dataset/detection/images/image_000737.jpg: 480x640 (no detections), 155.0ms\n", "image 739/2210 /content/smartvision_dataset/detection/images/image_000738.jpg: 480x640 (no detections), 153.6ms\n", "image 740/2210 /content/smartvision_dataset/detection/images/image_000739.jpg: 384x640 4 persons, 8 cars, 1 bus, 143.1ms\n", "image 741/2210 /content/smartvision_dataset/detection/images/image_000740.jpg: 384x640 4 persons, 8 cars, 1 bus, 127.6ms\n", "image 742/2210 /content/smartvision_dataset/detection/images/image_000741.jpg: 384x640 4 persons, 8 cars, 1 bus, 128.4ms\n", "image 743/2210 /content/smartvision_dataset/detection/images/image_000742.jpg: 384x640 4 persons, 8 cars, 1 bus, 129.7ms\n", "image 744/2210 /content/smartvision_dataset/detection/images/image_000743.jpg: 480x640 1 bus, 152.0ms\n", "image 745/2210 /content/smartvision_dataset/detection/images/image_000744.jpg: 640x448 2 persons, 237.6ms\n", "image 746/2210 /content/smartvision_dataset/detection/images/image_000745.jpg: 448x640 7 persons, 240.5ms\n", "image 747/2210 /content/smartvision_dataset/detection/images/image_000746.jpg: 448x640 1 stop sign, 234.4ms\n", "image 748/2210 /content/smartvision_dataset/detection/images/image_000747.jpg: 448x640 1 stop sign, 233.5ms\n", "image 749/2210 /content/smartvision_dataset/detection/images/image_000748.jpg: 480x640 1 person, 1 bus, 229.5ms\n", "image 750/2210 /content/smartvision_dataset/detection/images/image_000749.jpg: 448x640 1 train, 245.3ms\n", "image 751/2210 /content/smartvision_dataset/detection/images/image_000750.jpg: 448x640 1 train, 234.8ms\n", "image 752/2210 /content/smartvision_dataset/detection/images/image_000751.jpg: 640x640 8 persons, 317.3ms\n", "image 753/2210 /content/smartvision_dataset/detection/images/image_000752.jpg: 640x640 8 persons, 309.1ms\n", "image 754/2210 /content/smartvision_dataset/detection/images/image_000753.jpg: 640x640 8 persons, 334.7ms\n", "image 755/2210 /content/smartvision_dataset/detection/images/image_000754.jpg: 640x640 8 persons, 297.6ms\n", "image 756/2210 /content/smartvision_dataset/detection/images/image_000755.jpg: 640x640 8 persons, 302.5ms\n", "image 757/2210 /content/smartvision_dataset/detection/images/image_000756.jpg: 640x640 8 persons, 319.1ms\n", "image 758/2210 /content/smartvision_dataset/detection/images/image_000757.jpg: 640x640 8 persons, 307.1ms\n", "image 759/2210 /content/smartvision_dataset/detection/images/image_000758.jpg: 640x640 8 persons, 228.5ms\n", "image 760/2210 /content/smartvision_dataset/detection/images/image_000759.jpg: 640x640 8 persons, 200.3ms\n", "image 761/2210 /content/smartvision_dataset/detection/images/image_000760.jpg: 640x640 8 persons, 219.7ms\n", "image 762/2210 /content/smartvision_dataset/detection/images/image_000761.jpg: 640x640 8 persons, 209.0ms\n", "image 763/2210 /content/smartvision_dataset/detection/images/image_000762.jpg: 640x640 8 persons, 199.5ms\n", "image 764/2210 /content/smartvision_dataset/detection/images/image_000763.jpg: 640x640 8 persons, 201.9ms\n", "image 765/2210 /content/smartvision_dataset/detection/images/image_000764.jpg: 480x640 3 buss, 154.4ms\n", "image 766/2210 /content/smartvision_dataset/detection/images/image_000765.jpg: 640x480 1 person, 1 car, 171.5ms\n", "image 767/2210 /content/smartvision_dataset/detection/images/image_000766.jpg: 640x480 (no detections), 152.3ms\n", "image 768/2210 /content/smartvision_dataset/detection/images/image_000767.jpg: 480x640 (no detections), 152.5ms\n", "image 769/2210 /content/smartvision_dataset/detection/images/image_000768.jpg: 512x640 4 stop signs, 167.5ms\n", "image 770/2210 /content/smartvision_dataset/detection/images/image_000769.jpg: 512x640 4 stop signs, 170.8ms\n", "image 771/2210 /content/smartvision_dataset/detection/images/image_000770.jpg: 480x640 2 stop signs, 156.5ms\n", "image 772/2210 /content/smartvision_dataset/detection/images/image_000771.jpg: 448x640 3 persons, 5 motorcycles, 160.8ms\n", "image 773/2210 /content/smartvision_dataset/detection/images/image_000772.jpg: 480x640 5 bicycles, 156.6ms\n", "image 774/2210 /content/smartvision_dataset/detection/images/image_000773.jpg: 448x640 1 stop sign, 142.1ms\n", "image 775/2210 /content/smartvision_dataset/detection/images/image_000774.jpg: 448x640 1 stop sign, 144.8ms\n", "image 776/2210 /content/smartvision_dataset/detection/images/image_000775.jpg: 480x640 (no detections), 153.9ms\n", "image 777/2210 /content/smartvision_dataset/detection/images/image_000776.jpg: 640x480 3 stop signs, 156.1ms\n", "image 778/2210 /content/smartvision_dataset/detection/images/image_000777.jpg: 640x480 (no detections), 169.0ms\n", "image 779/2210 /content/smartvision_dataset/detection/images/image_000778.jpg: 480x640 12 persons, 1 car, 151.0ms\n", "image 780/2210 /content/smartvision_dataset/detection/images/image_000779.jpg: 480x640 (no detections), 158.6ms\n", "image 781/2210 /content/smartvision_dataset/detection/images/image_000780.jpg: 448x640 1 person, 1 bus, 144.4ms\n", "image 782/2210 /content/smartvision_dataset/detection/images/image_000781.jpg: 512x640 2 stop signs, 171.0ms\n", "image 783/2210 /content/smartvision_dataset/detection/images/image_000782.jpg: 640x448 5 stop signs, 148.1ms\n", "image 784/2210 /content/smartvision_dataset/detection/images/image_000783.jpg: 640x480 1 stop sign, 173.7ms\n", "image 785/2210 /content/smartvision_dataset/detection/images/image_000784.jpg: 448x640 (no detections), 146.6ms\n", "image 786/2210 /content/smartvision_dataset/detection/images/image_000785.jpg: 480x640 4 persons, 154.3ms\n", "image 787/2210 /content/smartvision_dataset/detection/images/image_000786.jpg: 640x448 1 stop sign, 142.7ms\n", "image 788/2210 /content/smartvision_dataset/detection/images/image_000787.jpg: 640x480 2 persons, 2 stop signs, 151.3ms\n", "image 789/2210 /content/smartvision_dataset/detection/images/image_000788.jpg: 640x480 1 stop sign, 157.6ms\n", "image 790/2210 /content/smartvision_dataset/detection/images/image_000789.jpg: 640x480 2 stop signs, 151.7ms\n", "image 791/2210 /content/smartvision_dataset/detection/images/image_000790.jpg: 480x640 3 stop signs, 167.9ms\n", "image 792/2210 /content/smartvision_dataset/detection/images/image_000791.jpg: 640x480 (no detections), 157.8ms\n", "image 793/2210 /content/smartvision_dataset/detection/images/image_000792.jpg: 448x640 (no detections), 149.3ms\n", "image 794/2210 /content/smartvision_dataset/detection/images/image_000793.jpg: 448x640 1 person, 147.1ms\n", "image 795/2210 /content/smartvision_dataset/detection/images/image_000794.jpg: 640x640 2 stop signs, 202.3ms\n", "image 796/2210 /content/smartvision_dataset/detection/images/image_000795.jpg: 480x640 4 persons, 5 bicycles, 153.8ms\n", "image 797/2210 /content/smartvision_dataset/detection/images/image_000796.jpg: 384x640 (no detections), 134.8ms\n", "image 798/2210 /content/smartvision_dataset/detection/images/image_000797.jpg: 480x640 1 stop sign, 150.2ms\n", "image 799/2210 /content/smartvision_dataset/detection/images/image_000798.jpg: 480x640 1 stop sign, 156.5ms\n", "image 800/2210 /content/smartvision_dataset/detection/images/image_000799.jpg: 640x640 1 stop sign, 203.7ms\n", "image 801/2210 /content/smartvision_dataset/detection/images/image_000800.jpg: 640x480 (no detections), 159.8ms\n", "image 802/2210 /content/smartvision_dataset/detection/images/image_000801.jpg: 480x640 2 stop signs, 157.6ms\n", "image 803/2210 /content/smartvision_dataset/detection/images/image_000802.jpg: 640x448 1 person, 148.5ms\n", "image 804/2210 /content/smartvision_dataset/detection/images/image_000803.jpg: 448x640 1 car, 1 truck, 143.7ms\n", "image 805/2210 /content/smartvision_dataset/detection/images/image_000804.jpg: 448x640 1 car, 1 truck, 153.3ms\n", "image 806/2210 /content/smartvision_dataset/detection/images/image_000805.jpg: 640x512 4 persons, 166.3ms\n", "image 807/2210 /content/smartvision_dataset/detection/images/image_000806.jpg: 480x640 (no detections), 157.3ms\n", "image 808/2210 /content/smartvision_dataset/detection/images/image_000807.jpg: 480x640 (no detections), 154.0ms\n", "image 809/2210 /content/smartvision_dataset/detection/images/image_000808.jpg: 480x640 2 stop signs, 165.6ms\n", "image 810/2210 /content/smartvision_dataset/detection/images/image_000809.jpg: 640x448 4 stop signs, 147.3ms\n", "image 811/2210 /content/smartvision_dataset/detection/images/image_000810.jpg: 448x640 (no detections), 148.3ms\n", "image 812/2210 /content/smartvision_dataset/detection/images/image_000811.jpg: 640x384 4 persons, 133.3ms\n", "image 813/2210 /content/smartvision_dataset/detection/images/image_000812.jpg: 480x640 (no detections), 155.4ms\n", "image 814/2210 /content/smartvision_dataset/detection/images/image_000813.jpg: 448x640 (no detections), 143.3ms\n", "image 815/2210 /content/smartvision_dataset/detection/images/image_000814.jpg: 448x640 (no detections), 157.5ms\n", "image 816/2210 /content/smartvision_dataset/detection/images/image_000815.jpg: 640x640 3 persons, 279.6ms\n", "image 817/2210 /content/smartvision_dataset/detection/images/image_000816.jpg: 480x640 (no detections), 246.0ms\n", "image 818/2210 /content/smartvision_dataset/detection/images/image_000817.jpg: 448x640 2 stop signs, 221.6ms\n", "image 819/2210 /content/smartvision_dataset/detection/images/image_000818.jpg: 448x640 (no detections), 229.8ms\n", "image 820/2210 /content/smartvision_dataset/detection/images/image_000819.jpg: 640x448 4 stop signs, 224.3ms\n", "image 821/2210 /content/smartvision_dataset/detection/images/image_000820.jpg: 448x640 (no detections), 217.7ms\n", "image 822/2210 /content/smartvision_dataset/detection/images/image_000821.jpg: 640x640 2 persons, 1 stop sign, 326.3ms\n", "image 823/2210 /content/smartvision_dataset/detection/images/image_000822.jpg: 480x640 (no detections), 240.5ms\n", "image 824/2210 /content/smartvision_dataset/detection/images/image_000823.jpg: 480x640 (no detections), 240.6ms\n", "image 825/2210 /content/smartvision_dataset/detection/images/image_000824.jpg: 640x640 1 motorcycle, 306.2ms\n", "image 826/2210 /content/smartvision_dataset/detection/images/image_000825.jpg: 640x448 1 person, 1 car, 224.3ms\n", "image 827/2210 /content/smartvision_dataset/detection/images/image_000826.jpg: 640x448 1 person, 1 stop sign, 225.8ms\n", "image 828/2210 /content/smartvision_dataset/detection/images/image_000827.jpg: 640x480 3 stop signs, 252.1ms\n", "image 829/2210 /content/smartvision_dataset/detection/images/image_000828.jpg: 480x640 (no detections), 247.2ms\n", "image 830/2210 /content/smartvision_dataset/detection/images/image_000829.jpg: 480x640 2 persons, 1 motorcycle, 249.1ms\n", "image 831/2210 /content/smartvision_dataset/detection/images/image_000830.jpg: 640x480 (no detections), 226.8ms\n", "image 832/2210 /content/smartvision_dataset/detection/images/image_000831.jpg: 448x640 2 bicycles, 2 stop signs, 164.2ms\n", "image 833/2210 /content/smartvision_dataset/detection/images/image_000832.jpg: 480x640 (no detections), 158.2ms\n", "image 834/2210 /content/smartvision_dataset/detection/images/image_000833.jpg: 480x640 (no detections), 157.4ms\n", "image 835/2210 /content/smartvision_dataset/detection/images/image_000834.jpg: 640x448 4 persons, 150.0ms\n", "image 836/2210 /content/smartvision_dataset/detection/images/image_000835.jpg: 640x480 2 stop signs, 162.8ms\n", "image 837/2210 /content/smartvision_dataset/detection/images/image_000836.jpg: 640x480 2 stop signs, 156.6ms\n", "image 838/2210 /content/smartvision_dataset/detection/images/image_000837.jpg: 480x640 (no detections), 170.3ms\n", "image 839/2210 /content/smartvision_dataset/detection/images/image_000838.jpg: 640x480 (no detections), 163.4ms\n", "image 840/2210 /content/smartvision_dataset/detection/images/image_000839.jpg: 640x480 3 stop signs, 156.9ms\n", "image 841/2210 /content/smartvision_dataset/detection/images/image_000840.jpg: 480x640 1 person, 160.2ms\n", "image 842/2210 /content/smartvision_dataset/detection/images/image_000841.jpg: 512x640 1 person, 1 train, 171.1ms\n", "image 843/2210 /content/smartvision_dataset/detection/images/image_000842.jpg: 480x640 2 stop signs, 159.3ms\n", "image 844/2210 /content/smartvision_dataset/detection/images/image_000843.jpg: 480x640 2 bicycles, 1 bus, 169.2ms\n", "image 845/2210 /content/smartvision_dataset/detection/images/image_000844.jpg: 512x640 (no detections), 165.2ms\n", "image 846/2210 /content/smartvision_dataset/detection/images/image_000845.jpg: 448x640 3 stop signs, 144.3ms\n", "image 847/2210 /content/smartvision_dataset/detection/images/image_000846.jpg: 480x640 3 stop signs, 151.7ms\n", "image 848/2210 /content/smartvision_dataset/detection/images/image_000847.jpg: 480x640 3 stop signs, 147.6ms\n", "image 849/2210 /content/smartvision_dataset/detection/images/image_000848.jpg: 448x640 6 persons, 1 stop sign, 156.9ms\n", "image 850/2210 /content/smartvision_dataset/detection/images/image_000849.jpg: 448x640 (no detections), 157.6ms\n", "image 851/2210 /content/smartvision_dataset/detection/images/image_000850.jpg: 416x640 1 train, 139.8ms\n", "image 852/2210 /content/smartvision_dataset/detection/images/image_000851.jpg: 416x640 1 train, 131.5ms\n", "image 853/2210 /content/smartvision_dataset/detection/images/image_000852.jpg: 512x640 (no detections), 164.8ms\n", "image 854/2210 /content/smartvision_dataset/detection/images/image_000853.jpg: 512x640 (no detections), 164.0ms\n", "image 855/2210 /content/smartvision_dataset/detection/images/image_000854.jpg: 512x640 (no detections), 162.3ms\n", "image 856/2210 /content/smartvision_dataset/detection/images/image_000855.jpg: 512x640 (no detections), 179.9ms\n", "image 857/2210 /content/smartvision_dataset/detection/images/image_000856.jpg: 512x640 6 persons, 169.0ms\n", "image 858/2210 /content/smartvision_dataset/detection/images/image_000857.jpg: 480x640 4 persons, 1 bench, 156.4ms\n", "image 859/2210 /content/smartvision_dataset/detection/images/image_000858.jpg: 640x480 1 person, 155.7ms\n", "image 860/2210 /content/smartvision_dataset/detection/images/image_000859.jpg: 640x448 5 persons, 2 elephants, 145.5ms\n", "image 861/2210 /content/smartvision_dataset/detection/images/image_000860.jpg: 640x448 (no detections), 149.2ms\n", "image 862/2210 /content/smartvision_dataset/detection/images/image_000861.jpg: 480x640 47 persons, 2 benchs, 166.8ms\n", "image 863/2210 /content/smartvision_dataset/detection/images/image_000862.jpg: 480x640 47 persons, 2 benchs, 155.2ms\n", "image 864/2210 /content/smartvision_dataset/detection/images/image_000863.jpg: 448x640 6 persons, 140.8ms\n", "image 865/2210 /content/smartvision_dataset/detection/images/image_000864.jpg: 448x640 6 persons, 141.5ms\n", "image 866/2210 /content/smartvision_dataset/detection/images/image_000865.jpg: 480x640 10 persons, 2 cars, 150.3ms\n", "image 867/2210 /content/smartvision_dataset/detection/images/image_000866.jpg: 640x448 5 persons, 151.9ms\n", "image 868/2210 /content/smartvision_dataset/detection/images/image_000867.jpg: 448x640 (no detections), 156.9ms\n", "image 869/2210 /content/smartvision_dataset/detection/images/image_000868.jpg: 640x480 3 persons, 155.5ms\n", "image 870/2210 /content/smartvision_dataset/detection/images/image_000869.jpg: 512x640 1 person, 167.4ms\n", "image 871/2210 /content/smartvision_dataset/detection/images/image_000870.jpg: 480x640 1 person, 1 car, 1 bench, 150.9ms\n", "image 872/2210 /content/smartvision_dataset/detection/images/image_000871.jpg: 480x640 5 persons, 150.5ms\n", "image 873/2210 /content/smartvision_dataset/detection/images/image_000872.jpg: 480x640 5 persons, 157.1ms\n", "image 874/2210 /content/smartvision_dataset/detection/images/image_000873.jpg: 448x640 2 persons, 2 benchs, 148.7ms\n", "image 875/2210 /content/smartvision_dataset/detection/images/image_000874.jpg: 384x640 1 person, 135.6ms\n", "image 876/2210 /content/smartvision_dataset/detection/images/image_000875.jpg: 480x640 (no detections), 149.5ms\n", "image 877/2210 /content/smartvision_dataset/detection/images/image_000876.jpg: 448x640 19 persons, 145.5ms\n", "image 878/2210 /content/smartvision_dataset/detection/images/image_000877.jpg: 448x640 19 persons, 146.1ms\n", "image 879/2210 /content/smartvision_dataset/detection/images/image_000878.jpg: 448x640 19 persons, 153.8ms\n", "image 880/2210 /content/smartvision_dataset/detection/images/image_000879.jpg: 640x640 3 persons, 203.1ms\n", "image 881/2210 /content/smartvision_dataset/detection/images/image_000880.jpg: 640x640 3 persons, 206.1ms\n", "image 882/2210 /content/smartvision_dataset/detection/images/image_000881.jpg: 416x640 6 trains, 133.9ms\n", "image 883/2210 /content/smartvision_dataset/detection/images/image_000882.jpg: 416x640 6 trains, 134.8ms\n", "image 884/2210 /content/smartvision_dataset/detection/images/image_000883.jpg: 416x640 6 trains, 130.9ms\n", "image 885/2210 /content/smartvision_dataset/detection/images/image_000884.jpg: 416x640 6 trains, 135.9ms\n", "image 886/2210 /content/smartvision_dataset/detection/images/image_000885.jpg: 416x640 6 trains, 134.7ms\n", "image 887/2210 /content/smartvision_dataset/detection/images/image_000886.jpg: 416x640 6 trains, 147.4ms\n", "image 888/2210 /content/smartvision_dataset/detection/images/image_000887.jpg: 480x640 3 persons, 152.4ms\n", "image 889/2210 /content/smartvision_dataset/detection/images/image_000888.jpg: 480x640 1 person, 151.4ms\n", "image 890/2210 /content/smartvision_dataset/detection/images/image_000889.jpg: 640x480 (no detections), 153.6ms\n", "image 891/2210 /content/smartvision_dataset/detection/images/image_000890.jpg: 480x640 1 person, 1 bed, 239.5ms\n", "image 892/2210 /content/smartvision_dataset/detection/images/image_000891.jpg: 448x640 1 bus, 225.8ms\n", "image 893/2210 /content/smartvision_dataset/detection/images/image_000892.jpg: 480x640 1 person, 1 airplane, 237.5ms\n", "image 894/2210 /content/smartvision_dataset/detection/images/image_000893.jpg: 480x640 1 person, 1 airplane, 225.8ms\n", "image 895/2210 /content/smartvision_dataset/detection/images/image_000894.jpg: 480x640 1 person, 1 airplane, 231.9ms\n", "image 896/2210 /content/smartvision_dataset/detection/images/image_000895.jpg: 448x640 (no detections), 217.4ms\n", "image 897/2210 /content/smartvision_dataset/detection/images/image_000896.jpg: 640x448 1 person, 242.7ms\n", "image 898/2210 /content/smartvision_dataset/detection/images/image_000897.jpg: 640x448 1 bench, 228.7ms\n", "image 899/2210 /content/smartvision_dataset/detection/images/image_000898.jpg: 448x640 13 persons, 3 potted plants, 227.1ms\n", "image 900/2210 /content/smartvision_dataset/detection/images/image_000899.jpg: 512x640 1 person, 4 benchs, 1 dog, 261.6ms\n", "image 901/2210 /content/smartvision_dataset/detection/images/image_000900.jpg: 512x640 1 person, 4 benchs, 1 dog, 261.2ms\n", "image 902/2210 /content/smartvision_dataset/detection/images/image_000901.jpg: 512x640 1 person, 4 benchs, 1 dog, 245.8ms\n", "image 903/2210 /content/smartvision_dataset/detection/images/image_000902.jpg: 512x640 1 person, 4 benchs, 1 dog, 248.9ms\n", "image 904/2210 /content/smartvision_dataset/detection/images/image_000903.jpg: 480x640 2 persons, 240.9ms\n", "image 905/2210 /content/smartvision_dataset/detection/images/image_000904.jpg: 640x640 (no detections), 338.8ms\n", "image 906/2210 /content/smartvision_dataset/detection/images/image_000905.jpg: 640x480 1 cat, 215.4ms\n", "image 907/2210 /content/smartvision_dataset/detection/images/image_000906.jpg: 512x640 (no detections), 169.4ms\n", "image 908/2210 /content/smartvision_dataset/detection/images/image_000907.jpg: 640x448 (no detections), 146.5ms\n", "image 909/2210 /content/smartvision_dataset/detection/images/image_000908.jpg: 640x448 (no detections), 144.1ms\n", "image 910/2210 /content/smartvision_dataset/detection/images/image_000909.jpg: 640x448 (no detections), 158.9ms\n", "image 911/2210 /content/smartvision_dataset/detection/images/image_000910.jpg: 640x448 (no detections), 144.1ms\n", "image 912/2210 /content/smartvision_dataset/detection/images/image_000911.jpg: 640x480 (no detections), 155.8ms\n", "image 913/2210 /content/smartvision_dataset/detection/images/image_000912.jpg: 640x480 (no detections), 160.6ms\n", "image 914/2210 /content/smartvision_dataset/detection/images/image_000913.jpg: 480x640 1 bus, 156.6ms\n", "image 915/2210 /content/smartvision_dataset/detection/images/image_000914.jpg: 448x640 22 persons, 141.7ms\n", "image 916/2210 /content/smartvision_dataset/detection/images/image_000915.jpg: 448x640 22 persons, 160.1ms\n", "image 917/2210 /content/smartvision_dataset/detection/images/image_000916.jpg: 448x640 7 persons, 140.5ms\n", "image 918/2210 /content/smartvision_dataset/detection/images/image_000917.jpg: 640x416 (no detections), 135.7ms\n", "image 919/2210 /content/smartvision_dataset/detection/images/image_000918.jpg: 640x416 (no detections), 140.4ms\n", "image 920/2210 /content/smartvision_dataset/detection/images/image_000919.jpg: 480x640 1 person, 154.9ms\n", "image 921/2210 /content/smartvision_dataset/detection/images/image_000920.jpg: 480x640 1 person, 151.3ms\n", "image 922/2210 /content/smartvision_dataset/detection/images/image_000921.jpg: 448x640 (no detections), 141.7ms\n", "image 923/2210 /content/smartvision_dataset/detection/images/image_000922.jpg: 480x640 (no detections), 164.3ms\n", "image 924/2210 /content/smartvision_dataset/detection/images/image_000923.jpg: 416x640 2 persons, 4 cars, 2 buss, 135.3ms\n", "image 925/2210 /content/smartvision_dataset/detection/images/image_000924.jpg: 416x640 2 persons, 4 cars, 2 buss, 131.2ms\n", "image 926/2210 /content/smartvision_dataset/detection/images/image_000925.jpg: 416x640 2 persons, 4 cars, 2 buss, 135.4ms\n", "image 927/2210 /content/smartvision_dataset/detection/images/image_000926.jpg: 480x640 1 bench, 1 cat, 153.4ms\n", "image 928/2210 /content/smartvision_dataset/detection/images/image_000927.jpg: 448x640 3 persons, 1 potted plant, 139.6ms\n", "image 929/2210 /content/smartvision_dataset/detection/images/image_000928.jpg: 448x640 3 persons, 1 potted plant, 153.6ms\n", "image 930/2210 /content/smartvision_dataset/detection/images/image_000929.jpg: 448x640 3 persons, 1 potted plant, 151.5ms\n", "image 931/2210 /content/smartvision_dataset/detection/images/image_000930.jpg: 448x640 (no detections), 153.2ms\n", "image 932/2210 /content/smartvision_dataset/detection/images/image_000931.jpg: 448x640 (no detections), 147.3ms\n", "image 933/2210 /content/smartvision_dataset/detection/images/image_000932.jpg: 448x640 (no detections), 147.6ms\n", "image 934/2210 /content/smartvision_dataset/detection/images/image_000933.jpg: 448x640 3 persons, 144.4ms\n", "image 935/2210 /content/smartvision_dataset/detection/images/image_000934.jpg: 640x480 (no detections), 161.8ms\n", "image 936/2210 /content/smartvision_dataset/detection/images/image_000935.jpg: 544x640 2 birds, 176.7ms\n", "image 937/2210 /content/smartvision_dataset/detection/images/image_000936.jpg: 544x640 2 birds, 170.6ms\n", "image 938/2210 /content/smartvision_dataset/detection/images/image_000937.jpg: 544x640 2 birds, 168.4ms\n", "image 939/2210 /content/smartvision_dataset/detection/images/image_000938.jpg: 544x640 2 birds, 169.3ms\n", "image 940/2210 /content/smartvision_dataset/detection/images/image_000939.jpg: 544x640 2 birds, 174.7ms\n", "image 941/2210 /content/smartvision_dataset/detection/images/image_000940.jpg: 544x640 2 birds, 183.2ms\n", "image 942/2210 /content/smartvision_dataset/detection/images/image_000941.jpg: 544x640 2 birds, 174.9ms\n", "image 943/2210 /content/smartvision_dataset/detection/images/image_000942.jpg: 544x640 2 birds, 172.4ms\n", "image 944/2210 /content/smartvision_dataset/detection/images/image_000943.jpg: 480x640 3 airplanes, 150.8ms\n", "image 945/2210 /content/smartvision_dataset/detection/images/image_000944.jpg: 480x640 3 airplanes, 152.9ms\n", "image 946/2210 /content/smartvision_dataset/detection/images/image_000945.jpg: 480x640 3 airplanes, 153.6ms\n", "image 947/2210 /content/smartvision_dataset/detection/images/image_000946.jpg: 480x640 3 airplanes, 166.6ms\n", "image 948/2210 /content/smartvision_dataset/detection/images/image_000947.jpg: 480x640 3 airplanes, 156.7ms\n", "image 949/2210 /content/smartvision_dataset/detection/images/image_000948.jpg: 480x640 3 airplanes, 156.0ms\n", "image 950/2210 /content/smartvision_dataset/detection/images/image_000949.jpg: 480x640 3 airplanes, 155.0ms\n", "image 951/2210 /content/smartvision_dataset/detection/images/image_000950.jpg: 480x640 3 airplanes, 153.5ms\n", "image 952/2210 /content/smartvision_dataset/detection/images/image_000951.jpg: 416x640 4 birds, 138.5ms\n", "image 953/2210 /content/smartvision_dataset/detection/images/image_000952.jpg: 416x640 4 birds, 148.1ms\n", "image 954/2210 /content/smartvision_dataset/detection/images/image_000953.jpg: 416x640 4 birds, 131.2ms\n", "image 955/2210 /content/smartvision_dataset/detection/images/image_000954.jpg: 448x640 6 birds, 146.5ms\n", "image 956/2210 /content/smartvision_dataset/detection/images/image_000955.jpg: 448x640 6 birds, 142.3ms\n", "image 957/2210 /content/smartvision_dataset/detection/images/image_000956.jpg: 448x640 6 birds, 144.5ms\n", "image 958/2210 /content/smartvision_dataset/detection/images/image_000957.jpg: 448x640 6 birds, 143.7ms\n", "image 959/2210 /content/smartvision_dataset/detection/images/image_000958.jpg: 448x640 6 birds, 139.0ms\n", "image 960/2210 /content/smartvision_dataset/detection/images/image_000959.jpg: 448x640 6 birds, 155.7ms\n", "image 961/2210 /content/smartvision_dataset/detection/images/image_000960.jpg: 448x640 6 birds, 144.4ms\n", "image 962/2210 /content/smartvision_dataset/detection/images/image_000961.jpg: 448x640 6 birds, 145.6ms\n", "image 963/2210 /content/smartvision_dataset/detection/images/image_000962.jpg: 448x640 6 birds, 143.5ms\n", "image 964/2210 /content/smartvision_dataset/detection/images/image_000963.jpg: 352x640 (no detections), 117.2ms\n", "image 965/2210 /content/smartvision_dataset/detection/images/image_000964.jpg: 448x640 (no detections), 143.2ms\n", "image 966/2210 /content/smartvision_dataset/detection/images/image_000965.jpg: 480x640 1 couch, 153.8ms\n", "image 967/2210 /content/smartvision_dataset/detection/images/image_000966.jpg: 480x640 (no detections), 241.8ms\n", "image 968/2210 /content/smartvision_dataset/detection/images/image_000967.jpg: 448x640 (no detections), 217.6ms\n", "image 969/2210 /content/smartvision_dataset/detection/images/image_000968.jpg: 448x640 (no detections), 220.5ms\n", "image 970/2210 /content/smartvision_dataset/detection/images/image_000969.jpg: 448x640 (no detections), 214.5ms\n", "image 971/2210 /content/smartvision_dataset/detection/images/image_000970.jpg: 448x640 (no detections), 225.5ms\n", "image 972/2210 /content/smartvision_dataset/detection/images/image_000971.jpg: 448x640 (no detections), 211.7ms\n", "image 973/2210 /content/smartvision_dataset/detection/images/image_000972.jpg: 448x640 (no detections), 219.5ms\n", "image 974/2210 /content/smartvision_dataset/detection/images/image_000973.jpg: 448x640 (no detections), 214.7ms\n", "image 975/2210 /content/smartvision_dataset/detection/images/image_000974.jpg: 448x640 (no detections), 225.5ms\n", "image 976/2210 /content/smartvision_dataset/detection/images/image_000975.jpg: 448x640 (no detections), 242.6ms\n", "image 977/2210 /content/smartvision_dataset/detection/images/image_000976.jpg: 448x640 (no detections), 219.4ms\n", "image 978/2210 /content/smartvision_dataset/detection/images/image_000977.jpg: 448x640 (no detections), 216.5ms\n", "image 979/2210 /content/smartvision_dataset/detection/images/image_000978.jpg: 448x640 (no detections), 214.5ms\n", "image 980/2210 /content/smartvision_dataset/detection/images/image_000979.jpg: 448x640 (no detections), 227.2ms\n", "image 981/2210 /content/smartvision_dataset/detection/images/image_000980.jpg: 640x384 2 persons, 211.7ms\n", "image 982/2210 /content/smartvision_dataset/detection/images/image_000981.jpg: 512x640 1 person, 262.0ms\n", "image 983/2210 /content/smartvision_dataset/detection/images/image_000982.jpg: 512x640 1 person, 251.0ms\n", "image 984/2210 /content/smartvision_dataset/detection/images/image_000983.jpg: 512x640 1 person, 184.2ms\n", "image 985/2210 /content/smartvision_dataset/detection/images/image_000984.jpg: 512x640 1 person, 174.2ms\n", "image 986/2210 /content/smartvision_dataset/detection/images/image_000985.jpg: 512x640 1 person, 160.7ms\n", "image 987/2210 /content/smartvision_dataset/detection/images/image_000986.jpg: 512x640 1 person, 168.1ms\n", "image 988/2210 /content/smartvision_dataset/detection/images/image_000987.jpg: 512x640 1 person, 163.6ms\n", "image 989/2210 /content/smartvision_dataset/detection/images/image_000988.jpg: 512x640 1 person, 163.5ms\n", "image 990/2210 /content/smartvision_dataset/detection/images/image_000989.jpg: 512x640 1 person, 160.2ms\n", "image 991/2210 /content/smartvision_dataset/detection/images/image_000990.jpg: 512x640 1 person, 173.0ms\n", "image 992/2210 /content/smartvision_dataset/detection/images/image_000991.jpg: 512x640 1 person, 162.7ms\n", "image 993/2210 /content/smartvision_dataset/detection/images/image_000992.jpg: 512x640 1 person, 157.7ms\n", "image 994/2210 /content/smartvision_dataset/detection/images/image_000993.jpg: 512x640 1 person, 167.5ms\n", "image 995/2210 /content/smartvision_dataset/detection/images/image_000994.jpg: 512x640 1 person, 167.4ms\n", "image 996/2210 /content/smartvision_dataset/detection/images/image_000995.jpg: 512x640 1 person, 175.7ms\n", "image 997/2210 /content/smartvision_dataset/detection/images/image_000996.jpg: 512x640 1 person, 159.8ms\n", "image 998/2210 /content/smartvision_dataset/detection/images/image_000997.jpg: 384x640 (no detections), 128.0ms\n", "image 999/2210 /content/smartvision_dataset/detection/images/image_000998.jpg: 384x640 (no detections), 126.4ms\n", "image 1000/2210 /content/smartvision_dataset/detection/images/image_000999.jpg: 384x640 (no detections), 126.4ms\n", "image 1001/2210 /content/smartvision_dataset/detection/images/image_001000.jpg: 384x640 (no detections), 127.7ms\n", "image 1002/2210 /content/smartvision_dataset/detection/images/image_001001.jpg: 384x640 (no detections), 130.7ms\n", "image 1003/2210 /content/smartvision_dataset/detection/images/image_001002.jpg: 384x640 (no detections), 123.6ms\n", "image 1004/2210 /content/smartvision_dataset/detection/images/image_001003.jpg: 384x640 (no detections), 143.8ms\n", "image 1005/2210 /content/smartvision_dataset/detection/images/image_001004.jpg: 384x640 (no detections), 127.4ms\n", "image 1006/2210 /content/smartvision_dataset/detection/images/image_001005.jpg: 384x640 (no detections), 130.8ms\n", "image 1007/2210 /content/smartvision_dataset/detection/images/image_001006.jpg: 384x640 (no detections), 124.1ms\n", "image 1008/2210 /content/smartvision_dataset/detection/images/image_001007.jpg: 384x640 (no detections), 125.0ms\n", "image 1009/2210 /content/smartvision_dataset/detection/images/image_001008.jpg: 512x640 1 bird, 165.4ms\n", "image 1010/2210 /content/smartvision_dataset/detection/images/image_001009.jpg: 448x640 1 bird, 139.8ms\n", "image 1011/2210 /content/smartvision_dataset/detection/images/image_001010.jpg: 448x640 1 bird, 157.2ms\n", "image 1012/2210 /content/smartvision_dataset/detection/images/image_001011.jpg: 640x640 2 birds, 201.3ms\n", "image 1013/2210 /content/smartvision_dataset/detection/images/image_001012.jpg: 512x640 4 birds, 167.3ms\n", "image 1014/2210 /content/smartvision_dataset/detection/images/image_001013.jpg: 512x640 4 birds, 161.8ms\n", "image 1015/2210 /content/smartvision_dataset/detection/images/image_001014.jpg: 640x640 2 birds, 196.9ms\n", "image 1016/2210 /content/smartvision_dataset/detection/images/image_001015.jpg: 416x640 2 birds, 148.0ms\n", "image 1017/2210 /content/smartvision_dataset/detection/images/image_001016.jpg: 480x640 1 bird, 151.5ms\n", "image 1018/2210 /content/smartvision_dataset/detection/images/image_001017.jpg: 448x640 3 birds, 143.9ms\n", "image 1019/2210 /content/smartvision_dataset/detection/images/image_001018.jpg: 448x640 3 birds, 141.4ms\n", "image 1020/2210 /content/smartvision_dataset/detection/images/image_001019.jpg: 448x640 3 birds, 141.8ms\n", "image 1021/2210 /content/smartvision_dataset/detection/images/image_001020.jpg: 480x640 2 cats, 149.8ms\n", "image 1022/2210 /content/smartvision_dataset/detection/images/image_001021.jpg: 544x640 3 cats, 166.4ms\n", "image 1023/2210 /content/smartvision_dataset/detection/images/image_001022.jpg: 416x640 1 cat, 142.4ms\n", "image 1024/2210 /content/smartvision_dataset/detection/images/image_001023.jpg: 448x640 2 cats, 143.3ms\n", "image 1025/2210 /content/smartvision_dataset/detection/images/image_001024.jpg: 480x640 1 cat, 154.2ms\n", "image 1026/2210 /content/smartvision_dataset/detection/images/image_001025.jpg: 640x640 (no detections), 200.3ms\n", "image 1027/2210 /content/smartvision_dataset/detection/images/image_001026.jpg: 480x640 1 bed, 154.2ms\n", "image 1028/2210 /content/smartvision_dataset/detection/images/image_001027.jpg: 480x640 1 cat, 153.0ms\n", "image 1029/2210 /content/smartvision_dataset/detection/images/image_001028.jpg: 384x640 2 cats, 1 couch, 138.5ms\n", "image 1030/2210 /content/smartvision_dataset/detection/images/image_001029.jpg: 384x640 2 cats, 1 couch, 126.5ms\n", "image 1031/2210 /content/smartvision_dataset/detection/images/image_001030.jpg: 384x640 2 cats, 1 couch, 125.9ms\n", "image 1032/2210 /content/smartvision_dataset/detection/images/image_001031.jpg: 480x640 2 cats, 1 bed, 150.8ms\n", "image 1033/2210 /content/smartvision_dataset/detection/images/image_001032.jpg: 640x448 1 cat, 147.2ms\n", "image 1034/2210 /content/smartvision_dataset/detection/images/image_001033.jpg: 640x576 2 persons, 176.0ms\n", "image 1035/2210 /content/smartvision_dataset/detection/images/image_001034.jpg: 640x576 2 persons, 191.9ms\n", "image 1036/2210 /content/smartvision_dataset/detection/images/image_001035.jpg: 480x640 (no detections), 154.8ms\n", "image 1037/2210 /content/smartvision_dataset/detection/images/image_001036.jpg: 640x544 (no detections), 191.1ms\n", "image 1038/2210 /content/smartvision_dataset/detection/images/image_001037.jpg: 480x640 1 cat, 153.3ms\n", "image 1039/2210 /content/smartvision_dataset/detection/images/image_001038.jpg: 640x640 1 cat, 204.9ms\n", "image 1040/2210 /content/smartvision_dataset/detection/images/image_001039.jpg: 448x640 (no detections), 142.4ms\n", "image 1041/2210 /content/smartvision_dataset/detection/images/image_001040.jpg: 480x640 1 cat, 1 bowl, 167.8ms\n", "image 1042/2210 /content/smartvision_dataset/detection/images/image_001041.jpg: 640x640 2 cats, 200.8ms\n", "image 1043/2210 /content/smartvision_dataset/detection/images/image_001042.jpg: 640x512 1 cat, 1 bottle, 196.5ms\n", "image 1044/2210 /content/smartvision_dataset/detection/images/image_001043.jpg: 640x448 1 potted plant, 222.7ms\n", "image 1045/2210 /content/smartvision_dataset/detection/images/image_001044.jpg: 480x640 (no detections), 235.9ms\n", "image 1046/2210 /content/smartvision_dataset/detection/images/image_001045.jpg: 480x640 2 cats, 233.3ms\n", "image 1047/2210 /content/smartvision_dataset/detection/images/image_001046.jpg: 448x640 (no detections), 217.6ms\n", "image 1048/2210 /content/smartvision_dataset/detection/images/image_001047.jpg: 640x448 2 cats, 231.9ms\n", "image 1049/2210 /content/smartvision_dataset/detection/images/image_001048.jpg: 640x480 1 car, 1 truck, 2 cats, 225.1ms\n", "image 1050/2210 /content/smartvision_dataset/detection/images/image_001049.jpg: 480x640 (no detections), 242.0ms\n", "image 1051/2210 /content/smartvision_dataset/detection/images/image_001050.jpg: 576x640 1 cat, 270.7ms\n", "image 1052/2210 /content/smartvision_dataset/detection/images/image_001051.jpg: 448x640 1 cat, 216.1ms\n", "image 1053/2210 /content/smartvision_dataset/detection/images/image_001052.jpg: 448x640 1 car, 1 truck, 1 cat, 219.5ms\n", "image 1054/2210 /content/smartvision_dataset/detection/images/image_001053.jpg: 448x640 1 car, 1 truck, 1 cat, 233.4ms\n", "image 1055/2210 /content/smartvision_dataset/detection/images/image_001054.jpg: 480x640 2 cats, 228.5ms\n", "image 1056/2210 /content/smartvision_dataset/detection/images/image_001055.jpg: 480x640 1 cat, 229.8ms\n", "image 1057/2210 /content/smartvision_dataset/detection/images/image_001056.jpg: 640x480 1 cat, 246.8ms\n", "image 1058/2210 /content/smartvision_dataset/detection/images/image_001057.jpg: 480x640 (no detections), 235.4ms\n", "image 1059/2210 /content/smartvision_dataset/detection/images/image_001058.jpg: 480x640 1 cat, 1 couch, 1 bed, 245.1ms\n", "image 1060/2210 /content/smartvision_dataset/detection/images/image_001059.jpg: 640x480 (no detections), 178.2ms\n", "image 1061/2210 /content/smartvision_dataset/detection/images/image_001060.jpg: 480x640 (no detections), 152.2ms\n", "image 1062/2210 /content/smartvision_dataset/detection/images/image_001061.jpg: 512x640 2 cats, 159.0ms\n", "image 1063/2210 /content/smartvision_dataset/detection/images/image_001062.jpg: 640x480 (no detections), 159.8ms\n", "image 1064/2210 /content/smartvision_dataset/detection/images/image_001063.jpg: 480x640 1 bench, 1 cat, 152.0ms\n", "image 1065/2210 /content/smartvision_dataset/detection/images/image_001064.jpg: 640x448 1 cat, 148.6ms\n", "image 1066/2210 /content/smartvision_dataset/detection/images/image_001065.jpg: 480x640 1 cat, 156.6ms\n", "image 1067/2210 /content/smartvision_dataset/detection/images/image_001066.jpg: 480x640 (no detections), 157.0ms\n", "image 1068/2210 /content/smartvision_dataset/detection/images/image_001067.jpg: 480x640 (no detections), 157.9ms\n", "image 1069/2210 /content/smartvision_dataset/detection/images/image_001068.jpg: 480x640 (no detections), 151.4ms\n", "image 1070/2210 /content/smartvision_dataset/detection/images/image_001069.jpg: 640x480 (no detections), 155.8ms\n", "image 1071/2210 /content/smartvision_dataset/detection/images/image_001070.jpg: 448x640 (no detections), 156.2ms\n", "image 1072/2210 /content/smartvision_dataset/detection/images/image_001071.jpg: 640x480 1 cat, 153.8ms\n", "image 1073/2210 /content/smartvision_dataset/detection/images/image_001072.jpg: 480x640 2 cats, 155.1ms\n", "image 1074/2210 /content/smartvision_dataset/detection/images/image_001073.jpg: 480x640 1 cat, 153.3ms\n", "image 1075/2210 /content/smartvision_dataset/detection/images/image_001074.jpg: 480x640 4 cats, 2 potted plants, 147.2ms\n", "image 1076/2210 /content/smartvision_dataset/detection/images/image_001075.jpg: 480x640 4 cats, 2 potted plants, 154.6ms\n", "image 1077/2210 /content/smartvision_dataset/detection/images/image_001076.jpg: 448x640 (no detections), 164.8ms\n", "image 1078/2210 /content/smartvision_dataset/detection/images/image_001077.jpg: 640x480 2 cats, 157.4ms\n", "image 1079/2210 /content/smartvision_dataset/detection/images/image_001078.jpg: 640x480 2 cats, 159.9ms\n", "image 1080/2210 /content/smartvision_dataset/detection/images/image_001079.jpg: 640x480 1 cat, 162.7ms\n", "image 1081/2210 /content/smartvision_dataset/detection/images/image_001080.jpg: 640x480 (no detections), 153.7ms\n", "image 1082/2210 /content/smartvision_dataset/detection/images/image_001081.jpg: 448x640 1 cat, 148.8ms\n", "image 1083/2210 /content/smartvision_dataset/detection/images/image_001082.jpg: 448x640 (no detections), 162.9ms\n", "image 1084/2210 /content/smartvision_dataset/detection/images/image_001083.jpg: 448x640 (no detections), 143.7ms\n", "image 1085/2210 /content/smartvision_dataset/detection/images/image_001084.jpg: 480x640 1 cat, 158.0ms\n", "image 1086/2210 /content/smartvision_dataset/detection/images/image_001085.jpg: 480x640 1 cat, 1 bed, 154.4ms\n", "image 1087/2210 /content/smartvision_dataset/detection/images/image_001086.jpg: 480x640 1 cat, 149.6ms\n", "image 1088/2210 /content/smartvision_dataset/detection/images/image_001087.jpg: 512x640 1 cat, 161.0ms\n", "image 1089/2210 /content/smartvision_dataset/detection/images/image_001088.jpg: 480x640 1 cat, 165.9ms\n", "image 1090/2210 /content/smartvision_dataset/detection/images/image_001089.jpg: 480x640 2 persons, 1 pizza, 152.0ms\n", "image 1091/2210 /content/smartvision_dataset/detection/images/image_001090.jpg: 384x640 (no detections), 127.0ms\n", "image 1092/2210 /content/smartvision_dataset/detection/images/image_001091.jpg: 384x640 (no detections), 127.2ms\n", "image 1093/2210 /content/smartvision_dataset/detection/images/image_001092.jpg: 448x640 2 cats, 144.7ms\n", "image 1094/2210 /content/smartvision_dataset/detection/images/image_001093.jpg: 640x512 (no detections), 163.9ms\n", "image 1095/2210 /content/smartvision_dataset/detection/images/image_001094.jpg: 640x512 (no detections), 161.4ms\n", "image 1096/2210 /content/smartvision_dataset/detection/images/image_001095.jpg: 448x640 1 cat, 153.1ms\n", "image 1097/2210 /content/smartvision_dataset/detection/images/image_001096.jpg: 448x640 2 cats, 143.2ms\n", "image 1098/2210 /content/smartvision_dataset/detection/images/image_001097.jpg: 640x640 1 truck, 202.0ms\n", "image 1099/2210 /content/smartvision_dataset/detection/images/image_001098.jpg: 448x640 1 cat, 138.4ms\n", "image 1100/2210 /content/smartvision_dataset/detection/images/image_001099.jpg: 480x640 1 cat, 149.6ms\n", "image 1101/2210 /content/smartvision_dataset/detection/images/image_001100.jpg: 480x640 1 couch, 148.3ms\n", "image 1102/2210 /content/smartvision_dataset/detection/images/image_001101.jpg: 640x480 1 person, 166.8ms\n", "image 1103/2210 /content/smartvision_dataset/detection/images/image_001102.jpg: 480x640 1 cat, 153.2ms\n", "image 1104/2210 /content/smartvision_dataset/detection/images/image_001103.jpg: 512x640 2 persons, 165.5ms\n", "image 1105/2210 /content/smartvision_dataset/detection/images/image_001104.jpg: 448x640 (no detections), 139.9ms\n", "image 1106/2210 /content/smartvision_dataset/detection/images/image_001105.jpg: 480x640 (no detections), 149.8ms\n", "image 1107/2210 /content/smartvision_dataset/detection/images/image_001106.jpg: 448x640 2 persons, 140.9ms\n", "image 1108/2210 /content/smartvision_dataset/detection/images/image_001107.jpg: 416x640 1 train, 147.7ms\n", "image 1109/2210 /content/smartvision_dataset/detection/images/image_001108.jpg: 480x640 (no detections), 151.1ms\n", "image 1110/2210 /content/smartvision_dataset/detection/images/image_001109.jpg: 640x640 (no detections), 203.6ms\n", "image 1111/2210 /content/smartvision_dataset/detection/images/image_001110.jpg: 640x640 (no detections), 199.4ms\n", "image 1112/2210 /content/smartvision_dataset/detection/images/image_001111.jpg: 640x640 (no detections), 197.3ms\n", "image 1113/2210 /content/smartvision_dataset/detection/images/image_001112.jpg: 640x640 (no detections), 215.0ms\n", "image 1114/2210 /content/smartvision_dataset/detection/images/image_001113.jpg: 640x608 (no detections), 209.0ms\n", "image 1115/2210 /content/smartvision_dataset/detection/images/image_001114.jpg: 448x640 6 persons, 1 potted plant, 152.1ms\n", "image 1116/2210 /content/smartvision_dataset/detection/images/image_001115.jpg: 448x640 1 person, 1 dog, 143.3ms\n", "image 1117/2210 /content/smartvision_dataset/detection/images/image_001116.jpg: 448x640 (no detections), 139.5ms\n", "image 1118/2210 /content/smartvision_dataset/detection/images/image_001117.jpg: 640x576 2 persons, 225.0ms\n", "image 1119/2210 /content/smartvision_dataset/detection/images/image_001118.jpg: 640x640 2 persons, 311.7ms\n", "image 1120/2210 /content/smartvision_dataset/detection/images/image_001119.jpg: 480x640 (no detections), 239.7ms\n", "image 1121/2210 /content/smartvision_dataset/detection/images/image_001120.jpg: 576x640 (no detections), 276.6ms\n", "image 1122/2210 /content/smartvision_dataset/detection/images/image_001121.jpg: 640x448 1 dog, 222.0ms\n", "image 1123/2210 /content/smartvision_dataset/detection/images/image_001122.jpg: 640x640 (no detections), 313.5ms\n", "image 1124/2210 /content/smartvision_dataset/detection/images/image_001123.jpg: 544x640 1 person, 1 dog, 266.5ms\n", "image 1125/2210 /content/smartvision_dataset/detection/images/image_001124.jpg: 544x640 1 person, 1 dog, 265.2ms\n", "image 1126/2210 /content/smartvision_dataset/detection/images/image_001125.jpg: 352x640 (no detections), 194.4ms\n", "image 1127/2210 /content/smartvision_dataset/detection/images/image_001126.jpg: 352x640 (no detections), 182.3ms\n", "image 1128/2210 /content/smartvision_dataset/detection/images/image_001127.jpg: 352x640 (no detections), 181.6ms\n", "image 1129/2210 /content/smartvision_dataset/detection/images/image_001128.jpg: 352x640 (no detections), 179.2ms\n", "image 1130/2210 /content/smartvision_dataset/detection/images/image_001129.jpg: 352x640 (no detections), 177.8ms\n", "image 1131/2210 /content/smartvision_dataset/detection/images/image_001130.jpg: 352x640 (no detections), 184.7ms\n", "image 1132/2210 /content/smartvision_dataset/detection/images/image_001131.jpg: 352x640 (no detections), 196.3ms\n", "image 1133/2210 /content/smartvision_dataset/detection/images/image_001132.jpg: 352x640 (no detections), 189.2ms\n", "image 1134/2210 /content/smartvision_dataset/detection/images/image_001133.jpg: 352x640 (no detections), 186.0ms\n", "image 1135/2210 /content/smartvision_dataset/detection/images/image_001134.jpg: 448x640 2 persons, 1 motorcycle, 1 dog, 202.6ms\n", "image 1136/2210 /content/smartvision_dataset/detection/images/image_001135.jpg: 448x640 1 couch, 141.5ms\n", "image 1137/2210 /content/smartvision_dataset/detection/images/image_001136.jpg: 448x640 1 couch, 148.1ms\n", "image 1138/2210 /content/smartvision_dataset/detection/images/image_001137.jpg: 640x448 1 motorcycle, 1 dog, 156.2ms\n", "image 1139/2210 /content/smartvision_dataset/detection/images/image_001138.jpg: 448x640 2 persons, 2 benchs, 138.8ms\n", "image 1140/2210 /content/smartvision_dataset/detection/images/image_001139.jpg: 448x640 2 persons, 2 benchs, 141.7ms\n", "image 1141/2210 /content/smartvision_dataset/detection/images/image_001140.jpg: 480x640 1 person, 2 couchs, 150.5ms\n", "image 1142/2210 /content/smartvision_dataset/detection/images/image_001141.jpg: 448x640 1 dog, 141.8ms\n", "image 1143/2210 /content/smartvision_dataset/detection/images/image_001142.jpg: 480x640 1 person, 1 car, 1 truck, 3 dogs, 167.3ms\n", "image 1144/2210 /content/smartvision_dataset/detection/images/image_001143.jpg: 512x640 (no detections), 177.7ms\n", "image 1145/2210 /content/smartvision_dataset/detection/images/image_001144.jpg: 576x640 1 dog, 174.6ms\n", "image 1146/2210 /content/smartvision_dataset/detection/images/image_001145.jpg: 640x512 1 dog, 165.9ms\n", "image 1147/2210 /content/smartvision_dataset/detection/images/image_001146.jpg: 448x640 (no detections), 143.3ms\n", "image 1148/2210 /content/smartvision_dataset/detection/images/image_001147.jpg: 448x640 (no detections), 143.8ms\n", "image 1149/2210 /content/smartvision_dataset/detection/images/image_001148.jpg: 544x640 1 person, 171.3ms\n", "image 1150/2210 /content/smartvision_dataset/detection/images/image_001149.jpg: 512x640 1 person, 4 benchs, 1 dog, 179.5ms\n", "image 1151/2210 /content/smartvision_dataset/detection/images/image_001150.jpg: 512x640 1 person, 4 benchs, 1 dog, 162.4ms\n", "image 1152/2210 /content/smartvision_dataset/detection/images/image_001151.jpg: 320x640 (no detections), 123.0ms\n", "image 1153/2210 /content/smartvision_dataset/detection/images/image_001152.jpg: 640x448 (no detections), 146.0ms\n", "image 1154/2210 /content/smartvision_dataset/detection/images/image_001153.jpg: 480x640 1 person, 150.5ms\n", "image 1155/2210 /content/smartvision_dataset/detection/images/image_001154.jpg: 480x640 (no detections), 148.5ms\n", "image 1156/2210 /content/smartvision_dataset/detection/images/image_001155.jpg: 480x640 1 couch, 165.0ms\n", "image 1157/2210 /content/smartvision_dataset/detection/images/image_001156.jpg: 480x640 1 couch, 149.5ms\n", "image 1158/2210 /content/smartvision_dataset/detection/images/image_001157.jpg: 448x640 (no detections), 151.5ms\n", "image 1159/2210 /content/smartvision_dataset/detection/images/image_001158.jpg: 640x480 2 potted plants, 155.7ms\n", "image 1160/2210 /content/smartvision_dataset/detection/images/image_001159.jpg: 480x640 (no detections), 151.0ms\n", "image 1161/2210 /content/smartvision_dataset/detection/images/image_001160.jpg: 480x640 (no detections), 146.5ms\n", "image 1162/2210 /content/smartvision_dataset/detection/images/image_001161.jpg: 480x640 2 dogs, 2 couchs, 168.1ms\n", "image 1163/2210 /content/smartvision_dataset/detection/images/image_001162.jpg: 480x640 2 dogs, 2 couchs, 147.7ms\n", "image 1164/2210 /content/smartvision_dataset/detection/images/image_001163.jpg: 448x640 1 person, 1 bench, 142.5ms\n", "image 1165/2210 /content/smartvision_dataset/detection/images/image_001164.jpg: 480x640 (no detections), 157.3ms\n", "image 1166/2210 /content/smartvision_dataset/detection/images/image_001165.jpg: 384x640 1 truck, 126.5ms\n", "image 1167/2210 /content/smartvision_dataset/detection/images/image_001166.jpg: 416x640 20 dogs, 132.0ms\n", "image 1168/2210 /content/smartvision_dataset/detection/images/image_001167.jpg: 416x640 20 dogs, 135.0ms\n", "image 1169/2210 /content/smartvision_dataset/detection/images/image_001168.jpg: 416x640 20 dogs, 142.7ms\n", "image 1170/2210 /content/smartvision_dataset/detection/images/image_001169.jpg: 416x640 20 dogs, 133.3ms\n", "image 1171/2210 /content/smartvision_dataset/detection/images/image_001170.jpg: 416x640 20 dogs, 132.1ms\n", "image 1172/2210 /content/smartvision_dataset/detection/images/image_001171.jpg: 416x640 20 dogs, 135.3ms\n", "image 1173/2210 /content/smartvision_dataset/detection/images/image_001172.jpg: 416x640 20 dogs, 132.9ms\n", "image 1174/2210 /content/smartvision_dataset/detection/images/image_001173.jpg: 416x640 20 dogs, 138.5ms\n", "image 1175/2210 /content/smartvision_dataset/detection/images/image_001174.jpg: 416x640 20 dogs, 131.9ms\n", "image 1176/2210 /content/smartvision_dataset/detection/images/image_001175.jpg: 416x640 20 dogs, 151.3ms\n", "image 1177/2210 /content/smartvision_dataset/detection/images/image_001176.jpg: 480x640 (no detections), 154.5ms\n", "image 1178/2210 /content/smartvision_dataset/detection/images/image_001177.jpg: 640x448 (no detections), 143.0ms\n", "image 1179/2210 /content/smartvision_dataset/detection/images/image_001178.jpg: 480x640 5 persons, 156.7ms\n", "image 1180/2210 /content/smartvision_dataset/detection/images/image_001179.jpg: 480x640 (no detections), 152.2ms\n", "image 1181/2210 /content/smartvision_dataset/detection/images/image_001180.jpg: 480x640 1 person, 3 couchs, 1 bed, 148.2ms\n", "image 1182/2210 /content/smartvision_dataset/detection/images/image_001181.jpg: 480x640 1 person, 165.4ms\n", "image 1183/2210 /content/smartvision_dataset/detection/images/image_001182.jpg: 448x640 (no detections), 142.9ms\n", "image 1184/2210 /content/smartvision_dataset/detection/images/image_001183.jpg: 416x640 (no detections), 134.2ms\n", "image 1185/2210 /content/smartvision_dataset/detection/images/image_001184.jpg: 416x640 (no detections), 130.0ms\n", "image 1186/2210 /content/smartvision_dataset/detection/images/image_001185.jpg: 448x640 (no detections), 145.2ms\n", "image 1187/2210 /content/smartvision_dataset/detection/images/image_001186.jpg: 512x640 (no detections), 160.6ms\n", "image 1188/2210 /content/smartvision_dataset/detection/images/image_001187.jpg: 512x640 (no detections), 163.0ms\n", "image 1189/2210 /content/smartvision_dataset/detection/images/image_001188.jpg: 640x448 1 dog, 150.7ms\n", "image 1190/2210 /content/smartvision_dataset/detection/images/image_001189.jpg: 448x640 1 person, 1 couch, 140.6ms\n", "image 1191/2210 /content/smartvision_dataset/detection/images/image_001190.jpg: 640x512 (no detections), 168.1ms\n", "image 1192/2210 /content/smartvision_dataset/detection/images/image_001191.jpg: 640x512 (no detections), 167.8ms\n", "image 1193/2210 /content/smartvision_dataset/detection/images/image_001192.jpg: 448x640 (no detections), 142.8ms\n", "image 1194/2210 /content/smartvision_dataset/detection/images/image_001193.jpg: 480x640 (no detections), 156.8ms\n", "image 1195/2210 /content/smartvision_dataset/detection/images/image_001194.jpg: 448x640 3 persons, 2 horses, 158.9ms\n", "image 1196/2210 /content/smartvision_dataset/detection/images/image_001195.jpg: 448x640 1 person, 155.1ms\n", "image 1197/2210 /content/smartvision_dataset/detection/images/image_001196.jpg: 448x640 1 person, 224.0ms\n", "image 1198/2210 /content/smartvision_dataset/detection/images/image_001197.jpg: 448x640 1 person, 226.3ms\n", "image 1199/2210 /content/smartvision_dataset/detection/images/image_001198.jpg: 448x640 1 person, 218.5ms\n", "image 1200/2210 /content/smartvision_dataset/detection/images/image_001199.jpg: 480x640 3 persons, 2 bicycles, 236.1ms\n", "image 1201/2210 /content/smartvision_dataset/detection/images/image_001200.jpg: 480x640 3 persons, 2 bicycles, 231.5ms\n", "image 1202/2210 /content/smartvision_dataset/detection/images/image_001201.jpg: 480x640 3 persons, 2 bicycles, 228.9ms\n", "image 1203/2210 /content/smartvision_dataset/detection/images/image_001202.jpg: 480x640 3 persons, 2 bicycles, 237.3ms\n", "image 1204/2210 /content/smartvision_dataset/detection/images/image_001203.jpg: 480x640 3 persons, 2 bicycles, 246.2ms\n", "image 1205/2210 /content/smartvision_dataset/detection/images/image_001204.jpg: 448x640 3 persons, 2 horses, 221.1ms\n", "image 1206/2210 /content/smartvision_dataset/detection/images/image_001205.jpg: 480x640 (no detections), 231.7ms\n", "image 1207/2210 /content/smartvision_dataset/detection/images/image_001206.jpg: 480x640 (no detections), 230.0ms\n", "image 1208/2210 /content/smartvision_dataset/detection/images/image_001207.jpg: 480x640 (no detections), 243.4ms\n", "image 1209/2210 /content/smartvision_dataset/detection/images/image_001208.jpg: 480x640 (no detections), 241.4ms\n", "image 1210/2210 /content/smartvision_dataset/detection/images/image_001209.jpg: 480x640 (no detections), 253.8ms\n", "image 1211/2210 /content/smartvision_dataset/detection/images/image_001210.jpg: 480x640 14 persons, 4 horses, 239.4ms\n", "image 1212/2210 /content/smartvision_dataset/detection/images/image_001211.jpg: 480x640 14 persons, 4 horses, 250.8ms\n", "image 1213/2210 /content/smartvision_dataset/detection/images/image_001212.jpg: 480x640 14 persons, 4 horses, 184.5ms\n", "image 1214/2210 /content/smartvision_dataset/detection/images/image_001213.jpg: 480x640 14 persons, 4 horses, 151.3ms\n", "image 1215/2210 /content/smartvision_dataset/detection/images/image_001214.jpg: 480x640 14 persons, 4 horses, 155.5ms\n", "image 1216/2210 /content/smartvision_dataset/detection/images/image_001215.jpg: 480x640 (no detections), 151.6ms\n", "image 1217/2210 /content/smartvision_dataset/detection/images/image_001216.jpg: 480x640 (no detections), 152.5ms\n", "image 1218/2210 /content/smartvision_dataset/detection/images/image_001217.jpg: 448x640 1 person, 1 horse, 160.2ms\n", "image 1219/2210 /content/smartvision_dataset/detection/images/image_001218.jpg: 448x640 1 person, 1 horse, 144.0ms\n", "image 1220/2210 /content/smartvision_dataset/detection/images/image_001219.jpg: 448x640 2 horses, 145.3ms\n", "image 1221/2210 /content/smartvision_dataset/detection/images/image_001220.jpg: 448x640 2 horses, 146.1ms\n", "image 1222/2210 /content/smartvision_dataset/detection/images/image_001221.jpg: 448x640 3 persons, 141.2ms\n", "image 1223/2210 /content/smartvision_dataset/detection/images/image_001222.jpg: 448x640 3 persons, 147.0ms\n", "image 1224/2210 /content/smartvision_dataset/detection/images/image_001223.jpg: 448x640 3 persons, 146.0ms\n", "image 1225/2210 /content/smartvision_dataset/detection/images/image_001224.jpg: 448x640 3 persons, 161.7ms\n", "image 1226/2210 /content/smartvision_dataset/detection/images/image_001225.jpg: 640x640 8 persons, 2 horses, 211.9ms\n", "image 1227/2210 /content/smartvision_dataset/detection/images/image_001226.jpg: 640x640 8 persons, 2 horses, 205.8ms\n", "image 1228/2210 /content/smartvision_dataset/detection/images/image_001227.jpg: 480x640 3 persons, 1 horse, 157.8ms\n", "image 1229/2210 /content/smartvision_dataset/detection/images/image_001228.jpg: 480x640 6 persons, 152.4ms\n", "image 1230/2210 /content/smartvision_dataset/detection/images/image_001229.jpg: 480x640 1 person, 1 horse, 172.7ms\n", "image 1231/2210 /content/smartvision_dataset/detection/images/image_001230.jpg: 480x640 1 person, 1 airplane, 159.3ms\n", "image 1232/2210 /content/smartvision_dataset/detection/images/image_001231.jpg: 480x640 1 person, 1 airplane, 154.3ms\n", "image 1233/2210 /content/smartvision_dataset/detection/images/image_001232.jpg: 640x480 3 buss, 1 train, 154.5ms\n", "image 1234/2210 /content/smartvision_dataset/detection/images/image_001233.jpg: 640x480 3 buss, 1 train, 154.1ms\n", "image 1235/2210 /content/smartvision_dataset/detection/images/image_001234.jpg: 640x480 3 buss, 1 train, 151.2ms\n", "image 1236/2210 /content/smartvision_dataset/detection/images/image_001235.jpg: 640x448 24 persons, 166.2ms\n", "image 1237/2210 /content/smartvision_dataset/detection/images/image_001236.jpg: 640x448 24 persons, 143.0ms\n", "image 1238/2210 /content/smartvision_dataset/detection/images/image_001237.jpg: 480x640 12 persons, 150.7ms\n", "image 1239/2210 /content/smartvision_dataset/detection/images/image_001238.jpg: 448x640 2 horses, 142.7ms\n", "image 1240/2210 /content/smartvision_dataset/detection/images/image_001239.jpg: 448x640 2 horses, 146.5ms\n", "image 1241/2210 /content/smartvision_dataset/detection/images/image_001240.jpg: 640x640 5 persons, 1 horse, 199.0ms\n", "image 1242/2210 /content/smartvision_dataset/detection/images/image_001241.jpg: 352x640 1 person, 134.3ms\n", "image 1243/2210 /content/smartvision_dataset/detection/images/image_001242.jpg: 640x448 2 horses, 145.1ms\n", "image 1244/2210 /content/smartvision_dataset/detection/images/image_001243.jpg: 448x640 1 horse, 142.0ms\n", "image 1245/2210 /content/smartvision_dataset/detection/images/image_001244.jpg: 448x640 1 horse, 142.2ms\n", "image 1246/2210 /content/smartvision_dataset/detection/images/image_001245.jpg: 544x640 1 person, 172.8ms\n", "image 1247/2210 /content/smartvision_dataset/detection/images/image_001246.jpg: 544x640 1 person, 170.3ms\n", "image 1248/2210 /content/smartvision_dataset/detection/images/image_001247.jpg: 480x640 2 horses, 168.9ms\n", "image 1249/2210 /content/smartvision_dataset/detection/images/image_001248.jpg: 384x640 2 cows, 132.3ms\n", "image 1250/2210 /content/smartvision_dataset/detection/images/image_001249.jpg: 384x640 2 cows, 129.9ms\n", "image 1251/2210 /content/smartvision_dataset/detection/images/image_001250.jpg: 384x640 2 cows, 126.9ms\n", "image 1252/2210 /content/smartvision_dataset/detection/images/image_001251.jpg: 384x640 2 cows, 129.3ms\n", "image 1253/2210 /content/smartvision_dataset/detection/images/image_001252.jpg: 384x640 2 cows, 131.7ms\n", "image 1254/2210 /content/smartvision_dataset/detection/images/image_001253.jpg: 384x640 2 cows, 127.1ms\n", "image 1255/2210 /content/smartvision_dataset/detection/images/image_001254.jpg: 448x640 1 horse, 143.6ms\n", "image 1256/2210 /content/smartvision_dataset/detection/images/image_001255.jpg: 480x640 3 persons, 1 horse, 160.2ms\n", "image 1257/2210 /content/smartvision_dataset/detection/images/image_001256.jpg: 480x640 3 persons, 1 horse, 155.3ms\n", "image 1258/2210 /content/smartvision_dataset/detection/images/image_001257.jpg: 512x640 1 person, 161.8ms\n", "image 1259/2210 /content/smartvision_dataset/detection/images/image_001258.jpg: 448x640 1 horse, 137.6ms\n", "image 1260/2210 /content/smartvision_dataset/detection/images/image_001259.jpg: 448x640 2 horses, 139.6ms\n", "image 1261/2210 /content/smartvision_dataset/detection/images/image_001260.jpg: 448x640 2 horses, 142.5ms\n", "image 1262/2210 /content/smartvision_dataset/detection/images/image_001261.jpg: 448x640 2 horses, 152.4ms\n", "image 1263/2210 /content/smartvision_dataset/detection/images/image_001262.jpg: 416x640 4 persons, 1 bicycle, 131.1ms\n", "image 1264/2210 /content/smartvision_dataset/detection/images/image_001263.jpg: 416x640 4 persons, 1 bicycle, 129.9ms\n", "image 1265/2210 /content/smartvision_dataset/detection/images/image_001264.jpg: 416x640 4 persons, 1 bicycle, 129.9ms\n", "image 1266/2210 /content/smartvision_dataset/detection/images/image_001265.jpg: 416x640 4 persons, 1 bicycle, 132.6ms\n", "image 1267/2210 /content/smartvision_dataset/detection/images/image_001266.jpg: 416x640 4 persons, 1 bicycle, 135.3ms\n", "image 1268/2210 /content/smartvision_dataset/detection/images/image_001267.jpg: 416x640 4 persons, 1 bicycle, 133.0ms\n", "image 1269/2210 /content/smartvision_dataset/detection/images/image_001268.jpg: 416x640 4 persons, 1 bicycle, 144.8ms\n", "image 1270/2210 /content/smartvision_dataset/detection/images/image_001269.jpg: 384x640 1 person, 1 horse, 126.4ms\n", "image 1271/2210 /content/smartvision_dataset/detection/images/image_001270.jpg: 384x640 1 person, 1 horse, 122.9ms\n", "image 1272/2210 /content/smartvision_dataset/detection/images/image_001271.jpg: 640x640 (no detections), 196.1ms\n", "image 1273/2210 /content/smartvision_dataset/detection/images/image_001272.jpg: 640x640 (no detections), 206.7ms\n", "image 1274/2210 /content/smartvision_dataset/detection/images/image_001273.jpg: 640x640 (no detections), 326.3ms\n", "image 1275/2210 /content/smartvision_dataset/detection/images/image_001274.jpg: 640x640 (no detections), 297.2ms\n", "image 1276/2210 /content/smartvision_dataset/detection/images/image_001275.jpg: 448x640 4 cows, 219.6ms\n", "image 1277/2210 /content/smartvision_dataset/detection/images/image_001276.jpg: 448x640 4 cows, 224.3ms\n", "image 1278/2210 /content/smartvision_dataset/detection/images/image_001277.jpg: 448x640 4 cows, 219.8ms\n", "image 1279/2210 /content/smartvision_dataset/detection/images/image_001278.jpg: 544x640 1 person, 1 cat, 256.5ms\n", "image 1280/2210 /content/smartvision_dataset/detection/images/image_001279.jpg: 448x640 5 cows, 215.5ms\n", "image 1281/2210 /content/smartvision_dataset/detection/images/image_001280.jpg: 448x640 5 cows, 215.5ms\n", "image 1282/2210 /content/smartvision_dataset/detection/images/image_001281.jpg: 448x640 5 cows, 219.0ms\n", "image 1283/2210 /content/smartvision_dataset/detection/images/image_001282.jpg: 448x640 5 cows, 221.1ms\n", "image 1284/2210 /content/smartvision_dataset/detection/images/image_001283.jpg: 448x640 5 cows, 213.0ms\n", "image 1285/2210 /content/smartvision_dataset/detection/images/image_001284.jpg: 640x480 (no detections), 236.5ms\n", "image 1286/2210 /content/smartvision_dataset/detection/images/image_001285.jpg: 480x640 (no detections), 228.9ms\n", "image 1287/2210 /content/smartvision_dataset/detection/images/image_001286.jpg: 480x640 (no detections), 257.5ms\n", "image 1288/2210 /content/smartvision_dataset/detection/images/image_001287.jpg: 480x640 5 persons, 233.6ms\n", "image 1289/2210 /content/smartvision_dataset/detection/images/image_001288.jpg: 480x640 1 cow, 235.6ms\n", "image 1290/2210 /content/smartvision_dataset/detection/images/image_001289.jpg: 480x640 1 cow, 151.5ms\n", "image 1291/2210 /content/smartvision_dataset/detection/images/image_001290.jpg: 448x640 3 cows, 146.6ms\n", "image 1292/2210 /content/smartvision_dataset/detection/images/image_001291.jpg: 448x640 3 cows, 154.7ms\n", "image 1293/2210 /content/smartvision_dataset/detection/images/image_001292.jpg: 448x640 3 cows, 137.9ms\n", "image 1294/2210 /content/smartvision_dataset/detection/images/image_001293.jpg: 448x640 3 cows, 138.8ms\n", "image 1295/2210 /content/smartvision_dataset/detection/images/image_001294.jpg: 448x640 3 cows, 140.9ms\n", "image 1296/2210 /content/smartvision_dataset/detection/images/image_001295.jpg: 480x640 1 cow, 152.4ms\n", "image 1297/2210 /content/smartvision_dataset/detection/images/image_001296.jpg: 448x640 2 persons, 1 cow, 141.2ms\n", "image 1298/2210 /content/smartvision_dataset/detection/images/image_001297.jpg: 448x640 (no detections), 142.5ms\n", "image 1299/2210 /content/smartvision_dataset/detection/images/image_001298.jpg: 544x640 1 person, 185.0ms\n", "image 1300/2210 /content/smartvision_dataset/detection/images/image_001299.jpg: 544x640 1 person, 169.1ms\n", "image 1301/2210 /content/smartvision_dataset/detection/images/image_001300.jpg: 544x640 1 person, 177.5ms\n", "image 1302/2210 /content/smartvision_dataset/detection/images/image_001301.jpg: 640x480 (no detections), 149.5ms\n", "image 1303/2210 /content/smartvision_dataset/detection/images/image_001302.jpg: 448x640 1 cow, 138.4ms\n", "image 1304/2210 /content/smartvision_dataset/detection/images/image_001303.jpg: 448x640 1 cow, 138.9ms\n", "image 1305/2210 /content/smartvision_dataset/detection/images/image_001304.jpg: 480x640 5 persons, 162.7ms\n", "image 1306/2210 /content/smartvision_dataset/detection/images/image_001305.jpg: 448x640 7 cows, 138.5ms\n", "image 1307/2210 /content/smartvision_dataset/detection/images/image_001306.jpg: 448x640 7 cows, 144.8ms\n", "image 1308/2210 /content/smartvision_dataset/detection/images/image_001307.jpg: 448x640 7 cows, 159.3ms\n", "image 1309/2210 /content/smartvision_dataset/detection/images/image_001308.jpg: 448x640 7 cows, 142.9ms\n", "image 1310/2210 /content/smartvision_dataset/detection/images/image_001309.jpg: 448x640 7 cows, 139.1ms\n", "image 1311/2210 /content/smartvision_dataset/detection/images/image_001310.jpg: 448x640 7 cows, 154.6ms\n", "image 1312/2210 /content/smartvision_dataset/detection/images/image_001311.jpg: 448x640 7 cows, 142.8ms\n", "image 1313/2210 /content/smartvision_dataset/detection/images/image_001312.jpg: 448x640 7 cows, 148.7ms\n", "image 1314/2210 /content/smartvision_dataset/detection/images/image_001313.jpg: 448x640 1 cow, 161.9ms\n", "image 1315/2210 /content/smartvision_dataset/detection/images/image_001314.jpg: 448x640 1 cow, 178.0ms\n", "image 1316/2210 /content/smartvision_dataset/detection/images/image_001315.jpg: 448x640 1 cow, 181.3ms\n", "image 1317/2210 /content/smartvision_dataset/detection/images/image_001316.jpg: 448x640 1 person, 2 cows, 203.5ms\n", "image 1318/2210 /content/smartvision_dataset/detection/images/image_001317.jpg: 448x640 1 person, 2 cows, 152.4ms\n", "image 1319/2210 /content/smartvision_dataset/detection/images/image_001318.jpg: 448x640 1 person, 2 cows, 144.7ms\n", "image 1320/2210 /content/smartvision_dataset/detection/images/image_001319.jpg: 640x448 1 cow, 150.5ms\n", "image 1321/2210 /content/smartvision_dataset/detection/images/image_001320.jpg: 640x448 1 cow, 146.3ms\n", "image 1322/2210 /content/smartvision_dataset/detection/images/image_001321.jpg: 416x640 2 cows, 136.7ms\n", "image 1323/2210 /content/smartvision_dataset/detection/images/image_001322.jpg: 640x448 1 cow, 155.7ms\n", "image 1324/2210 /content/smartvision_dataset/detection/images/image_001323.jpg: 448x640 1 cow, 140.2ms\n", "image 1325/2210 /content/smartvision_dataset/detection/images/image_001324.jpg: 448x640 1 cow, 146.7ms\n", "image 1326/2210 /content/smartvision_dataset/detection/images/image_001325.jpg: 448x640 1 cow, 138.2ms\n", "image 1327/2210 /content/smartvision_dataset/detection/images/image_001326.jpg: 448x640 1 cow, 140.6ms\n", "image 1328/2210 /content/smartvision_dataset/detection/images/image_001327.jpg: 448x640 1 cow, 139.2ms\n", "image 1329/2210 /content/smartvision_dataset/detection/images/image_001328.jpg: 480x640 (no detections), 156.4ms\n", "image 1330/2210 /content/smartvision_dataset/detection/images/image_001329.jpg: 640x640 3 cows, 216.4ms\n", "image 1331/2210 /content/smartvision_dataset/detection/images/image_001330.jpg: 640x640 3 cows, 204.9ms\n", "image 1332/2210 /content/smartvision_dataset/detection/images/image_001331.jpg: 640x640 3 cows, 194.9ms\n", "image 1333/2210 /content/smartvision_dataset/detection/images/image_001332.jpg: 640x640 3 cows, 196.1ms\n", "image 1334/2210 /content/smartvision_dataset/detection/images/image_001333.jpg: 640x640 3 cows, 211.4ms\n", "image 1335/2210 /content/smartvision_dataset/detection/images/image_001334.jpg: 480x640 2 persons, 2 cows, 151.3ms\n", "image 1336/2210 /content/smartvision_dataset/detection/images/image_001335.jpg: 480x640 2 persons, 2 cows, 148.9ms\n", "image 1337/2210 /content/smartvision_dataset/detection/images/image_001336.jpg: 448x640 1 elephant, 139.5ms\n", "image 1338/2210 /content/smartvision_dataset/detection/images/image_001337.jpg: 448x640 1 elephant, 143.2ms\n", "image 1339/2210 /content/smartvision_dataset/detection/images/image_001338.jpg: 448x640 1 elephant, 142.8ms\n", "image 1340/2210 /content/smartvision_dataset/detection/images/image_001339.jpg: 448x640 1 elephant, 137.5ms\n", "image 1341/2210 /content/smartvision_dataset/detection/images/image_001340.jpg: 480x640 (no detections), 166.2ms\n", "image 1342/2210 /content/smartvision_dataset/detection/images/image_001341.jpg: 480x640 (no detections), 153.3ms\n", "image 1343/2210 /content/smartvision_dataset/detection/images/image_001342.jpg: 480x640 (no detections), 148.5ms\n", "image 1344/2210 /content/smartvision_dataset/detection/images/image_001343.jpg: 480x640 (no detections), 151.4ms\n", "image 1345/2210 /content/smartvision_dataset/detection/images/image_001344.jpg: 480x640 (no detections), 151.9ms\n", "image 1346/2210 /content/smartvision_dataset/detection/images/image_001345.jpg: 480x640 (no detections), 146.3ms\n", "image 1347/2210 /content/smartvision_dataset/detection/images/image_001346.jpg: 480x640 (no detections), 163.4ms\n", "image 1348/2210 /content/smartvision_dataset/detection/images/image_001347.jpg: 448x640 (no detections), 173.7ms\n", "image 1349/2210 /content/smartvision_dataset/detection/images/image_001348.jpg: 480x640 (no detections), 241.6ms\n", "image 1350/2210 /content/smartvision_dataset/detection/images/image_001349.jpg: 480x640 (no detections), 232.7ms\n", "image 1351/2210 /content/smartvision_dataset/detection/images/image_001350.jpg: 480x640 2 cows, 240.7ms\n", "image 1352/2210 /content/smartvision_dataset/detection/images/image_001351.jpg: 448x640 4 cows, 219.4ms\n", "image 1353/2210 /content/smartvision_dataset/detection/images/image_001352.jpg: 448x640 4 cows, 216.0ms\n", "image 1354/2210 /content/smartvision_dataset/detection/images/image_001353.jpg: 640x640 2 cows, 306.2ms\n", "image 1355/2210 /content/smartvision_dataset/detection/images/image_001354.jpg: 640x640 2 cows, 338.6ms\n", "image 1356/2210 /content/smartvision_dataset/detection/images/image_001355.jpg: 640x640 2 cows, 299.4ms\n", "image 1357/2210 /content/smartvision_dataset/detection/images/image_001356.jpg: 448x640 1 horse, 1 cow, 219.7ms\n", "image 1358/2210 /content/smartvision_dataset/detection/images/image_001357.jpg: 448x640 1 horse, 1 cow, 229.9ms\n", "image 1359/2210 /content/smartvision_dataset/detection/images/image_001358.jpg: 448x640 1 horse, 1 cow, 223.0ms\n", "image 1360/2210 /content/smartvision_dataset/detection/images/image_001359.jpg: 448x640 1 horse, 1 cow, 217.9ms\n", "image 1361/2210 /content/smartvision_dataset/detection/images/image_001360.jpg: 512x640 2 persons, 2 elephants, 248.7ms\n", "image 1362/2210 /content/smartvision_dataset/detection/images/image_001361.jpg: 512x640 2 persons, 2 elephants, 250.3ms\n", "image 1363/2210 /content/smartvision_dataset/detection/images/image_001362.jpg: 640x608 4 elephants, 301.2ms\n", "image 1364/2210 /content/smartvision_dataset/detection/images/image_001363.jpg: 640x608 4 elephants, 191.1ms\n", "image 1365/2210 /content/smartvision_dataset/detection/images/image_001364.jpg: 448x640 5 elephants, 142.2ms\n", "image 1366/2210 /content/smartvision_dataset/detection/images/image_001365.jpg: 448x640 5 elephants, 144.7ms\n", "image 1367/2210 /content/smartvision_dataset/detection/images/image_001366.jpg: 448x640 5 elephants, 140.8ms\n", "image 1368/2210 /content/smartvision_dataset/detection/images/image_001367.jpg: 448x640 5 elephants, 145.5ms\n", "image 1369/2210 /content/smartvision_dataset/detection/images/image_001368.jpg: 448x640 5 elephants, 140.4ms\n", "image 1370/2210 /content/smartvision_dataset/detection/images/image_001369.jpg: 448x640 5 elephants, 156.0ms\n", "image 1371/2210 /content/smartvision_dataset/detection/images/image_001370.jpg: 256x640 (no detections), 92.4ms\n", "image 1372/2210 /content/smartvision_dataset/detection/images/image_001371.jpg: 256x640 (no detections), 95.7ms\n", "image 1373/2210 /content/smartvision_dataset/detection/images/image_001372.jpg: 256x640 (no detections), 88.3ms\n", "image 1374/2210 /content/smartvision_dataset/detection/images/image_001373.jpg: 256x640 (no detections), 87.9ms\n", "image 1375/2210 /content/smartvision_dataset/detection/images/image_001374.jpg: 256x640 (no detections), 88.9ms\n", "image 1376/2210 /content/smartvision_dataset/detection/images/image_001375.jpg: 256x640 (no detections), 89.6ms\n", "image 1377/2210 /content/smartvision_dataset/detection/images/image_001376.jpg: 256x640 (no detections), 96.1ms\n", "image 1378/2210 /content/smartvision_dataset/detection/images/image_001377.jpg: 448x640 4 elephants, 138.5ms\n", "image 1379/2210 /content/smartvision_dataset/detection/images/image_001378.jpg: 448x640 4 elephants, 154.2ms\n", "image 1380/2210 /content/smartvision_dataset/detection/images/image_001379.jpg: 448x640 4 elephants, 142.2ms\n", "image 1381/2210 /content/smartvision_dataset/detection/images/image_001380.jpg: 384x640 3 elephants, 130.2ms\n", "image 1382/2210 /content/smartvision_dataset/detection/images/image_001381.jpg: 384x640 3 elephants, 122.0ms\n", "image 1383/2210 /content/smartvision_dataset/detection/images/image_001382.jpg: 480x640 1 elephant, 148.1ms\n", "image 1384/2210 /content/smartvision_dataset/detection/images/image_001383.jpg: 480x640 1 elephant, 156.4ms\n", "image 1385/2210 /content/smartvision_dataset/detection/images/image_001384.jpg: 480x640 1 elephant, 168.6ms\n", "image 1386/2210 /content/smartvision_dataset/detection/images/image_001385.jpg: 640x448 5 persons, 2 elephants, 144.3ms\n", "image 1387/2210 /content/smartvision_dataset/detection/images/image_001386.jpg: 448x640 2 elephants, 145.6ms\n", "image 1388/2210 /content/smartvision_dataset/detection/images/image_001387.jpg: 352x640 6 elephants, 115.4ms\n", "image 1389/2210 /content/smartvision_dataset/detection/images/image_001388.jpg: 352x640 6 elephants, 111.6ms\n", "image 1390/2210 /content/smartvision_dataset/detection/images/image_001389.jpg: 352x640 6 elephants, 120.2ms\n", "image 1391/2210 /content/smartvision_dataset/detection/images/image_001390.jpg: 352x640 6 elephants, 122.8ms\n", "image 1392/2210 /content/smartvision_dataset/detection/images/image_001391.jpg: 352x640 6 elephants, 116.2ms\n", "image 1393/2210 /content/smartvision_dataset/detection/images/image_001392.jpg: 448x640 2 elephants, 152.4ms\n", "image 1394/2210 /content/smartvision_dataset/detection/images/image_001393.jpg: 448x640 2 elephants, 139.7ms\n", "image 1395/2210 /content/smartvision_dataset/detection/images/image_001394.jpg: 448x640 2 elephants, 139.1ms\n", "image 1396/2210 /content/smartvision_dataset/detection/images/image_001395.jpg: 448x640 2 elephants, 141.1ms\n", "image 1397/2210 /content/smartvision_dataset/detection/images/image_001396.jpg: 448x640 2 elephants, 146.2ms\n", "image 1398/2210 /content/smartvision_dataset/detection/images/image_001397.jpg: 480x640 (no detections), 150.6ms\n", "image 1399/2210 /content/smartvision_dataset/detection/images/image_001398.jpg: 480x640 (no detections), 172.6ms\n", "image 1400/2210 /content/smartvision_dataset/detection/images/image_001399.jpg: 480x640 (no detections), 148.8ms\n", "image 1401/2210 /content/smartvision_dataset/detection/images/image_001400.jpg: 480x640 (no detections), 148.9ms\n", "image 1402/2210 /content/smartvision_dataset/detection/images/image_001401.jpg: 480x640 (no detections), 147.7ms\n", "image 1403/2210 /content/smartvision_dataset/detection/images/image_001402.jpg: 480x640 (no detections), 152.4ms\n", "image 1404/2210 /content/smartvision_dataset/detection/images/image_001403.jpg: 480x640 (no detections), 150.1ms\n", "image 1405/2210 /content/smartvision_dataset/detection/images/image_001404.jpg: 480x640 (no detections), 171.1ms\n", "image 1406/2210 /content/smartvision_dataset/detection/images/image_001405.jpg: 480x640 (no detections), 151.8ms\n", "image 1407/2210 /content/smartvision_dataset/detection/images/image_001406.jpg: 480x640 (no detections), 147.7ms\n", "image 1408/2210 /content/smartvision_dataset/detection/images/image_001407.jpg: 480x640 (no detections), 147.2ms\n", "image 1409/2210 /content/smartvision_dataset/detection/images/image_001408.jpg: 480x640 (no detections), 151.4ms\n", "image 1410/2210 /content/smartvision_dataset/detection/images/image_001409.jpg: 480x640 (no detections), 148.3ms\n", "image 1411/2210 /content/smartvision_dataset/detection/images/image_001410.jpg: 480x640 1 elephant, 150.7ms\n", "image 1412/2210 /content/smartvision_dataset/detection/images/image_001411.jpg: 512x640 (no detections), 175.9ms\n", "image 1413/2210 /content/smartvision_dataset/detection/images/image_001412.jpg: 480x640 1 person, 2 elephants, 152.6ms\n", "image 1414/2210 /content/smartvision_dataset/detection/images/image_001413.jpg: 480x640 1 elephant, 153.3ms\n", "image 1415/2210 /content/smartvision_dataset/detection/images/image_001414.jpg: 576x640 4 elephants, 184.4ms\n", "image 1416/2210 /content/smartvision_dataset/detection/images/image_001415.jpg: 576x640 4 elephants, 182.3ms\n", "image 1417/2210 /content/smartvision_dataset/detection/images/image_001416.jpg: 576x640 4 elephants, 193.4ms\n", "image 1418/2210 /content/smartvision_dataset/detection/images/image_001417.jpg: 448x640 2 elephants, 138.9ms\n", "image 1419/2210 /content/smartvision_dataset/detection/images/image_001418.jpg: 448x640 2 elephants, 136.9ms\n", "image 1420/2210 /content/smartvision_dataset/detection/images/image_001419.jpg: 448x640 2 elephants, 141.7ms\n", "image 1421/2210 /content/smartvision_dataset/detection/images/image_001420.jpg: 448x640 2 elephants, 143.5ms\n", "image 1422/2210 /content/smartvision_dataset/detection/images/image_001421.jpg: 480x640 2 elephants, 151.9ms\n", "image 1423/2210 /content/smartvision_dataset/detection/images/image_001422.jpg: 480x640 2 elephants, 150.0ms\n", "image 1424/2210 /content/smartvision_dataset/detection/images/image_001423.jpg: 480x640 2 elephants, 158.0ms\n", "image 1425/2210 /content/smartvision_dataset/detection/images/image_001424.jpg: 480x640 2 elephants, 149.9ms\n", "image 1426/2210 /content/smartvision_dataset/detection/images/image_001425.jpg: 480x640 2 elephants, 147.7ms\n", "image 1427/2210 /content/smartvision_dataset/detection/images/image_001426.jpg: 480x640 2 elephants, 147.5ms\n", "image 1428/2210 /content/smartvision_dataset/detection/images/image_001427.jpg: 512x640 1 elephant, 253.2ms\n", "image 1429/2210 /content/smartvision_dataset/detection/images/image_001428.jpg: 512x640 1 elephant, 252.4ms\n", "image 1430/2210 /content/smartvision_dataset/detection/images/image_001429.jpg: 480x640 3 elephants, 236.6ms\n", "image 1431/2210 /content/smartvision_dataset/detection/images/image_001430.jpg: 640x480 5 persons, 1 elephant, 234.9ms\n", "image 1432/2210 /content/smartvision_dataset/detection/images/image_001431.jpg: 640x448 2 persons, 219.9ms\n", "image 1433/2210 /content/smartvision_dataset/detection/images/image_001432.jpg: 640x448 2 persons, 229.7ms\n", "image 1434/2210 /content/smartvision_dataset/detection/images/image_001433.jpg: 640x448 2 elephants, 230.9ms\n", "image 1435/2210 /content/smartvision_dataset/detection/images/image_001434.jpg: 640x448 2 elephants, 221.9ms\n", "image 1436/2210 /content/smartvision_dataset/detection/images/image_001435.jpg: 480x640 2 elephants, 235.8ms\n", "image 1437/2210 /content/smartvision_dataset/detection/images/image_001436.jpg: 448x640 4 elephants, 215.8ms\n", "image 1438/2210 /content/smartvision_dataset/detection/images/image_001437.jpg: 448x640 4 elephants, 223.1ms\n", "image 1439/2210 /content/smartvision_dataset/detection/images/image_001438.jpg: 640x480 8 persons, 233.4ms\n", "image 1440/2210 /content/smartvision_dataset/detection/images/image_001439.jpg: 480x640 2 elephants, 245.1ms\n", "image 1441/2210 /content/smartvision_dataset/detection/images/image_001440.jpg: 480x640 (no detections), 235.1ms\n", "image 1442/2210 /content/smartvision_dataset/detection/images/image_001441.jpg: 640x480 2 elephants, 247.5ms\n", "image 1443/2210 /content/smartvision_dataset/detection/images/image_001442.jpg: 448x640 (no detections), 225.4ms\n", "image 1444/2210 /content/smartvision_dataset/detection/images/image_001443.jpg: 448x640 5 elephants, 158.1ms\n", "image 1445/2210 /content/smartvision_dataset/detection/images/image_001444.jpg: 448x640 5 elephants, 139.2ms\n", "image 1446/2210 /content/smartvision_dataset/detection/images/image_001445.jpg: 640x480 (no detections), 149.1ms\n", "image 1447/2210 /content/smartvision_dataset/detection/images/image_001446.jpg: 480x640 (no detections), 161.8ms\n", "image 1448/2210 /content/smartvision_dataset/detection/images/image_001447.jpg: 480x640 (no detections), 150.6ms\n", "image 1449/2210 /content/smartvision_dataset/detection/images/image_001448.jpg: 480x640 (no detections), 152.5ms\n", "image 1450/2210 /content/smartvision_dataset/detection/images/image_001449.jpg: 480x640 (no detections), 147.7ms\n", "image 1451/2210 /content/smartvision_dataset/detection/images/image_001450.jpg: 480x640 (no detections), 151.4ms\n", "image 1452/2210 /content/smartvision_dataset/detection/images/image_001451.jpg: 480x640 (no detections), 146.0ms\n", "image 1453/2210 /content/smartvision_dataset/detection/images/image_001452.jpg: 480x640 (no detections), 144.2ms\n", "image 1454/2210 /content/smartvision_dataset/detection/images/image_001453.jpg: 480x640 (no detections), 161.8ms\n", "image 1455/2210 /content/smartvision_dataset/detection/images/image_001454.jpg: 480x640 (no detections), 152.1ms\n", "image 1456/2210 /content/smartvision_dataset/detection/images/image_001455.jpg: 480x640 (no detections), 145.8ms\n", "image 1457/2210 /content/smartvision_dataset/detection/images/image_001456.jpg: 640x480 6 persons, 150.3ms\n", "image 1458/2210 /content/smartvision_dataset/detection/images/image_001457.jpg: 640x448 (no detections), 140.5ms\n", "image 1459/2210 /content/smartvision_dataset/detection/images/image_001458.jpg: 448x640 (no detections), 141.9ms\n", "image 1460/2210 /content/smartvision_dataset/detection/images/image_001459.jpg: 448x640 (no detections), 153.5ms\n", "image 1461/2210 /content/smartvision_dataset/detection/images/image_001460.jpg: 448x640 (no detections), 146.6ms\n", "image 1462/2210 /content/smartvision_dataset/detection/images/image_001461.jpg: 480x640 (no detections), 151.7ms\n", "image 1463/2210 /content/smartvision_dataset/detection/images/image_001462.jpg: 480x640 (no detections), 152.6ms\n", "image 1464/2210 /content/smartvision_dataset/detection/images/image_001463.jpg: 480x640 1 cat, 152.3ms\n", "image 1465/2210 /content/smartvision_dataset/detection/images/image_001464.jpg: 448x640 (no detections), 140.8ms\n", "image 1466/2210 /content/smartvision_dataset/detection/images/image_001465.jpg: 448x640 (no detections), 138.8ms\n", "image 1467/2210 /content/smartvision_dataset/detection/images/image_001466.jpg: 640x448 (no detections), 160.4ms\n", "image 1468/2210 /content/smartvision_dataset/detection/images/image_001467.jpg: 448x640 6 persons, 1 potted plant, 139.7ms\n", "image 1469/2210 /content/smartvision_dataset/detection/images/image_001468.jpg: 448x640 6 persons, 1 potted plant, 139.2ms\n", "image 1470/2210 /content/smartvision_dataset/detection/images/image_001469.jpg: 448x640 6 persons, 1 potted plant, 140.0ms\n", "image 1471/2210 /content/smartvision_dataset/detection/images/image_001470.jpg: 448x640 6 persons, 1 potted plant, 142.4ms\n", "image 1472/2210 /content/smartvision_dataset/detection/images/image_001471.jpg: 448x640 6 persons, 1 potted plant, 137.8ms\n", "image 1473/2210 /content/smartvision_dataset/detection/images/image_001472.jpg: 448x640 6 persons, 1 potted plant, 153.5ms\n", "image 1474/2210 /content/smartvision_dataset/detection/images/image_001473.jpg: 640x448 1 bottle, 157.9ms\n", "image 1475/2210 /content/smartvision_dataset/detection/images/image_001474.jpg: 640x448 3 persons, 142.3ms\n", "image 1476/2210 /content/smartvision_dataset/detection/images/image_001475.jpg: 480x640 3 persons, 147.9ms\n", "image 1477/2210 /content/smartvision_dataset/detection/images/image_001476.jpg: 480x640 3 persons, 143.5ms\n", "image 1478/2210 /content/smartvision_dataset/detection/images/image_001477.jpg: 480x640 3 persons, 147.4ms\n", "image 1479/2210 /content/smartvision_dataset/detection/images/image_001478.jpg: 480x640 3 persons, 152.5ms\n", "image 1480/2210 /content/smartvision_dataset/detection/images/image_001479.jpg: 480x640 3 persons, 162.6ms\n", "image 1481/2210 /content/smartvision_dataset/detection/images/image_001480.jpg: 480x640 3 persons, 150.4ms\n", "image 1482/2210 /content/smartvision_dataset/detection/images/image_001481.jpg: 480x640 3 persons, 155.5ms\n", "image 1483/2210 /content/smartvision_dataset/detection/images/image_001482.jpg: 480x640 3 persons, 147.7ms\n", "image 1484/2210 /content/smartvision_dataset/detection/images/image_001483.jpg: 480x640 3 persons, 146.4ms\n", "image 1485/2210 /content/smartvision_dataset/detection/images/image_001484.jpg: 480x640 3 persons, 151.2ms\n", "image 1486/2210 /content/smartvision_dataset/detection/images/image_001485.jpg: 480x640 3 persons, 163.6ms\n", "image 1487/2210 /content/smartvision_dataset/detection/images/image_001486.jpg: 480x640 3 persons, 147.9ms\n", "image 1488/2210 /content/smartvision_dataset/detection/images/image_001487.jpg: 480x640 3 persons, 152.4ms\n", "image 1489/2210 /content/smartvision_dataset/detection/images/image_001488.jpg: 480x640 3 persons, 149.0ms\n", "image 1490/2210 /content/smartvision_dataset/detection/images/image_001489.jpg: 480x640 (no detections), 144.3ms\n", "image 1491/2210 /content/smartvision_dataset/detection/images/image_001490.jpg: 480x640 (no detections), 151.1ms\n", "image 1492/2210 /content/smartvision_dataset/detection/images/image_001491.jpg: 480x640 (no detections), 173.6ms\n", "image 1493/2210 /content/smartvision_dataset/detection/images/image_001492.jpg: 480x640 (no detections), 149.2ms\n", "image 1494/2210 /content/smartvision_dataset/detection/images/image_001493.jpg: 480x640 (no detections), 151.5ms\n", "image 1495/2210 /content/smartvision_dataset/detection/images/image_001494.jpg: 480x640 (no detections), 150.4ms\n", "image 1496/2210 /content/smartvision_dataset/detection/images/image_001495.jpg: 480x640 47 persons, 2 benchs, 149.1ms\n", "image 1497/2210 /content/smartvision_dataset/detection/images/image_001496.jpg: 480x640 47 persons, 2 benchs, 154.3ms\n", "image 1498/2210 /content/smartvision_dataset/detection/images/image_001497.jpg: 512x640 5 persons, 178.4ms\n", "image 1499/2210 /content/smartvision_dataset/detection/images/image_001498.jpg: 480x640 1 person, 147.9ms\n", "image 1500/2210 /content/smartvision_dataset/detection/images/image_001499.jpg: 448x640 6 persons, 142.0ms\n", "image 1501/2210 /content/smartvision_dataset/detection/images/image_001500.jpg: 448x640 6 persons, 139.2ms\n", "image 1502/2210 /content/smartvision_dataset/detection/images/image_001501.jpg: 448x640 6 persons, 139.0ms\n", "image 1503/2210 /content/smartvision_dataset/detection/images/image_001502.jpg: 448x640 6 persons, 144.6ms\n", "image 1504/2210 /content/smartvision_dataset/detection/images/image_001503.jpg: 448x640 6 persons, 142.8ms\n", "image 1505/2210 /content/smartvision_dataset/detection/images/image_001504.jpg: 448x640 6 persons, 189.7ms\n", "image 1506/2210 /content/smartvision_dataset/detection/images/image_001505.jpg: 480x640 (no detections), 235.8ms\n", "image 1507/2210 /content/smartvision_dataset/detection/images/image_001506.jpg: 640x480 (no detections), 226.6ms\n", "image 1508/2210 /content/smartvision_dataset/detection/images/image_001507.jpg: 640x480 (no detections), 236.0ms\n", "image 1509/2210 /content/smartvision_dataset/detection/images/image_001508.jpg: 480x640 1 bowl, 1 pizza, 235.4ms\n", "image 1510/2210 /content/smartvision_dataset/detection/images/image_001509.jpg: 480x640 10 persons, 228.3ms\n", "image 1511/2210 /content/smartvision_dataset/detection/images/image_001510.jpg: 480x640 10 persons, 227.3ms\n", "image 1512/2210 /content/smartvision_dataset/detection/images/image_001511.jpg: 480x640 10 persons, 228.5ms\n", "image 1513/2210 /content/smartvision_dataset/detection/images/image_001512.jpg: 480x640 10 persons, 253.4ms\n", "image 1514/2210 /content/smartvision_dataset/detection/images/image_001513.jpg: 480x640 10 persons, 230.8ms\n", "image 1515/2210 /content/smartvision_dataset/detection/images/image_001514.jpg: 480x640 10 persons, 230.1ms\n", "image 1516/2210 /content/smartvision_dataset/detection/images/image_001515.jpg: 480x640 10 persons, 229.6ms\n", "image 1517/2210 /content/smartvision_dataset/detection/images/image_001516.jpg: 640x544 1 person, 259.9ms\n", "image 1518/2210 /content/smartvision_dataset/detection/images/image_001517.jpg: 640x544 1 person, 281.6ms\n", "image 1519/2210 /content/smartvision_dataset/detection/images/image_001518.jpg: 640x544 1 person, 264.5ms\n", "image 1520/2210 /content/smartvision_dataset/detection/images/image_001519.jpg: 640x544 1 person, 267.7ms\n", "image 1521/2210 /content/smartvision_dataset/detection/images/image_001520.jpg: 640x544 1 person, 190.9ms\n", "image 1522/2210 /content/smartvision_dataset/detection/images/image_001521.jpg: 640x544 1 person, 190.5ms\n", "image 1523/2210 /content/smartvision_dataset/detection/images/image_001522.jpg: 640x544 1 person, 166.3ms\n", "image 1524/2210 /content/smartvision_dataset/detection/images/image_001523.jpg: 640x544 1 person, 170.7ms\n", "image 1525/2210 /content/smartvision_dataset/detection/images/image_001524.jpg: 640x544 1 person, 182.5ms\n", "image 1526/2210 /content/smartvision_dataset/detection/images/image_001525.jpg: 640x544 1 person, 169.8ms\n", "image 1527/2210 /content/smartvision_dataset/detection/images/image_001526.jpg: 480x640 (no detections), 165.7ms\n", "image 1528/2210 /content/smartvision_dataset/detection/images/image_001527.jpg: 480x640 (no detections), 152.2ms\n", "image 1529/2210 /content/smartvision_dataset/detection/images/image_001528.jpg: 480x640 (no detections), 151.5ms\n", "image 1530/2210 /content/smartvision_dataset/detection/images/image_001529.jpg: 480x640 (no detections), 147.0ms\n", "image 1531/2210 /content/smartvision_dataset/detection/images/image_001530.jpg: 480x640 2 persons, 155.9ms\n", "image 1532/2210 /content/smartvision_dataset/detection/images/image_001531.jpg: 480x640 2 persons, 147.3ms\n", "image 1533/2210 /content/smartvision_dataset/detection/images/image_001532.jpg: 480x640 2 persons, 149.9ms\n", "image 1534/2210 /content/smartvision_dataset/detection/images/image_001533.jpg: 480x640 2 persons, 160.2ms\n", "image 1535/2210 /content/smartvision_dataset/detection/images/image_001534.jpg: 640x416 4 persons, 132.4ms\n", "image 1536/2210 /content/smartvision_dataset/detection/images/image_001535.jpg: 640x416 4 persons, 136.6ms\n", "image 1537/2210 /content/smartvision_dataset/detection/images/image_001536.jpg: 640x416 4 persons, 139.0ms\n", "image 1538/2210 /content/smartvision_dataset/detection/images/image_001537.jpg: 640x416 4 persons, 135.4ms\n", "image 1539/2210 /content/smartvision_dataset/detection/images/image_001538.jpg: 640x416 4 persons, 130.3ms\n", "image 1540/2210 /content/smartvision_dataset/detection/images/image_001539.jpg: 640x416 4 persons, 143.1ms\n", "image 1541/2210 /content/smartvision_dataset/detection/images/image_001540.jpg: 640x416 4 persons, 130.1ms\n", "image 1542/2210 /content/smartvision_dataset/detection/images/image_001541.jpg: 640x416 4 persons, 130.1ms\n", "image 1543/2210 /content/smartvision_dataset/detection/images/image_001542.jpg: 640x416 4 persons, 132.3ms\n", "image 1544/2210 /content/smartvision_dataset/detection/images/image_001543.jpg: 640x416 4 persons, 138.3ms\n", "image 1545/2210 /content/smartvision_dataset/detection/images/image_001544.jpg: 512x640 (no detections), 159.3ms\n", "image 1546/2210 /content/smartvision_dataset/detection/images/image_001545.jpg: 480x640 (no detections), 152.3ms\n", "image 1547/2210 /content/smartvision_dataset/detection/images/image_001546.jpg: 480x640 (no detections), 159.2ms\n", "image 1548/2210 /content/smartvision_dataset/detection/images/image_001547.jpg: 480x640 (no detections), 149.2ms\n", "image 1549/2210 /content/smartvision_dataset/detection/images/image_001548.jpg: 480x640 (no detections), 153.1ms\n", "image 1550/2210 /content/smartvision_dataset/detection/images/image_001549.jpg: 480x640 (no detections), 160.7ms\n", "image 1551/2210 /content/smartvision_dataset/detection/images/image_001550.jpg: 480x640 (no detections), 149.7ms\n", "image 1552/2210 /content/smartvision_dataset/detection/images/image_001551.jpg: 480x640 (no detections), 154.0ms\n", "image 1553/2210 /content/smartvision_dataset/detection/images/image_001552.jpg: 480x640 (no detections), 163.7ms\n", "image 1554/2210 /content/smartvision_dataset/detection/images/image_001553.jpg: 480x640 (no detections), 144.8ms\n", "image 1555/2210 /content/smartvision_dataset/detection/images/image_001554.jpg: 480x640 (no detections), 149.7ms\n", "image 1556/2210 /content/smartvision_dataset/detection/images/image_001555.jpg: 640x480 6 persons, 156.8ms\n", "image 1557/2210 /content/smartvision_dataset/detection/images/image_001556.jpg: 640x480 6 persons, 153.0ms\n", "image 1558/2210 /content/smartvision_dataset/detection/images/image_001557.jpg: 480x640 3 bowls, 148.6ms\n", "image 1559/2210 /content/smartvision_dataset/detection/images/image_001558.jpg: 480x640 3 bowls, 164.7ms\n", "image 1560/2210 /content/smartvision_dataset/detection/images/image_001559.jpg: 480x640 3 bowls, 149.5ms\n", "image 1561/2210 /content/smartvision_dataset/detection/images/image_001560.jpg: 480x640 (no detections), 155.1ms\n", "image 1562/2210 /content/smartvision_dataset/detection/images/image_001561.jpg: 480x640 (no detections), 156.5ms\n", "image 1563/2210 /content/smartvision_dataset/detection/images/image_001562.jpg: 480x640 1 pizza, 153.0ms\n", "image 1564/2210 /content/smartvision_dataset/detection/images/image_001563.jpg: 640x448 (no detections), 144.5ms\n", "image 1565/2210 /content/smartvision_dataset/detection/images/image_001564.jpg: 480x640 (no detections), 147.0ms\n", "image 1566/2210 /content/smartvision_dataset/detection/images/image_001565.jpg: 480x640 (no detections), 164.0ms\n", "image 1567/2210 /content/smartvision_dataset/detection/images/image_001566.jpg: 480x640 1 cat, 149.1ms\n", "image 1568/2210 /content/smartvision_dataset/detection/images/image_001567.jpg: 480x640 1 cat, 155.3ms\n", "image 1569/2210 /content/smartvision_dataset/detection/images/image_001568.jpg: 480x640 1 bowl, 150.3ms\n", "image 1570/2210 /content/smartvision_dataset/detection/images/image_001569.jpg: 480x640 1 bowl, 152.7ms\n", "image 1571/2210 /content/smartvision_dataset/detection/images/image_001570.jpg: 480x640 1 bowl, 150.0ms\n", "image 1572/2210 /content/smartvision_dataset/detection/images/image_001571.jpg: 480x640 1 bowl, 160.7ms\n", "image 1573/2210 /content/smartvision_dataset/detection/images/image_001572.jpg: 480x640 1 bowl, 150.6ms\n", "image 1574/2210 /content/smartvision_dataset/detection/images/image_001573.jpg: 480x640 1 bowl, 152.3ms\n", "image 1575/2210 /content/smartvision_dataset/detection/images/image_001574.jpg: 480x640 1 bowl, 148.4ms\n", "image 1576/2210 /content/smartvision_dataset/detection/images/image_001575.jpg: 480x640 1 bowl, 151.8ms\n", "image 1577/2210 /content/smartvision_dataset/detection/images/image_001576.jpg: 480x640 1 bowl, 148.3ms\n", "image 1578/2210 /content/smartvision_dataset/detection/images/image_001577.jpg: 480x640 1 bowl, 160.7ms\n", "image 1579/2210 /content/smartvision_dataset/detection/images/image_001578.jpg: 480x640 1 bowl, 152.3ms\n", "image 1580/2210 /content/smartvision_dataset/detection/images/image_001579.jpg: 480x640 1 bowl, 150.2ms\n", "image 1581/2210 /content/smartvision_dataset/detection/images/image_001580.jpg: 480x640 (no detections), 230.6ms\n", "image 1582/2210 /content/smartvision_dataset/detection/images/image_001581.jpg: 448x640 6 persons, 1 potted plant, 218.0ms\n", "image 1583/2210 /content/smartvision_dataset/detection/images/image_001582.jpg: 448x640 6 persons, 1 potted plant, 220.5ms\n", "image 1584/2210 /content/smartvision_dataset/detection/images/image_001583.jpg: 448x640 6 persons, 1 potted plant, 220.4ms\n", "image 1585/2210 /content/smartvision_dataset/detection/images/image_001584.jpg: 448x640 6 persons, 1 potted plant, 215.6ms\n", "image 1586/2210 /content/smartvision_dataset/detection/images/image_001585.jpg: 640x448 1 bottle, 213.8ms\n", "image 1587/2210 /content/smartvision_dataset/detection/images/image_001586.jpg: 480x640 (no detections), 232.0ms\n", "image 1588/2210 /content/smartvision_dataset/detection/images/image_001587.jpg: 480x640 (no detections), 252.5ms\n", "image 1589/2210 /content/smartvision_dataset/detection/images/image_001588.jpg: 480x640 (no detections), 227.9ms\n", "image 1590/2210 /content/smartvision_dataset/detection/images/image_001589.jpg: 480x640 (no detections), 226.0ms\n", "image 1591/2210 /content/smartvision_dataset/detection/images/image_001590.jpg: 480x640 (no detections), 232.2ms\n", "image 1592/2210 /content/smartvision_dataset/detection/images/image_001591.jpg: 576x640 1 person, 312.3ms\n", "image 1593/2210 /content/smartvision_dataset/detection/images/image_001592.jpg: 480x640 3 persons, 233.0ms\n", "image 1594/2210 /content/smartvision_dataset/detection/images/image_001593.jpg: 480x640 3 persons, 228.3ms\n", "image 1595/2210 /content/smartvision_dataset/detection/images/image_001594.jpg: 480x640 3 persons, 239.6ms\n", "image 1596/2210 /content/smartvision_dataset/detection/images/image_001595.jpg: 544x640 1 person, 279.0ms\n", "image 1597/2210 /content/smartvision_dataset/detection/images/image_001596.jpg: 448x640 1 person, 155.8ms\n", "image 1598/2210 /content/smartvision_dataset/detection/images/image_001597.jpg: 640x576 2 persons, 179.7ms\n", "image 1599/2210 /content/smartvision_dataset/detection/images/image_001598.jpg: 480x640 (no detections), 153.0ms\n", "image 1600/2210 /content/smartvision_dataset/detection/images/image_001599.jpg: 480x640 (no detections), 151.9ms\n", "image 1601/2210 /content/smartvision_dataset/detection/images/image_001600.jpg: 640x640 2 persons, 215.2ms\n", "image 1602/2210 /content/smartvision_dataset/detection/images/image_001601.jpg: 480x640 (no detections), 150.3ms\n", "image 1603/2210 /content/smartvision_dataset/detection/images/image_001602.jpg: 480x640 (no detections), 155.3ms\n", "image 1604/2210 /content/smartvision_dataset/detection/images/image_001603.jpg: 640x640 1 bowl, 192.9ms\n", "image 1605/2210 /content/smartvision_dataset/detection/images/image_001604.jpg: 480x640 2 bowls, 148.9ms\n", "image 1606/2210 /content/smartvision_dataset/detection/images/image_001605.jpg: 480x640 2 bowls, 145.7ms\n", "image 1607/2210 /content/smartvision_dataset/detection/images/image_001606.jpg: 480x640 (no detections), 166.8ms\n", "image 1608/2210 /content/smartvision_dataset/detection/images/image_001607.jpg: 480x640 (no detections), 148.5ms\n", "image 1609/2210 /content/smartvision_dataset/detection/images/image_001608.jpg: 480x640 (no detections), 147.5ms\n", "image 1610/2210 /content/smartvision_dataset/detection/images/image_001609.jpg: 480x640 (no detections), 147.5ms\n", "image 1611/2210 /content/smartvision_dataset/detection/images/image_001610.jpg: 480x640 (no detections), 151.8ms\n", "image 1612/2210 /content/smartvision_dataset/detection/images/image_001611.jpg: 480x640 (no detections), 147.9ms\n", "image 1613/2210 /content/smartvision_dataset/detection/images/image_001612.jpg: 480x640 10 persons, 165.1ms\n", "image 1614/2210 /content/smartvision_dataset/detection/images/image_001613.jpg: 480x640 10 persons, 153.4ms\n", "image 1615/2210 /content/smartvision_dataset/detection/images/image_001614.jpg: 640x640 1 bowl, 194.2ms\n", "image 1616/2210 /content/smartvision_dataset/detection/images/image_001615.jpg: 480x640 3 bowls, 149.5ms\n", "image 1617/2210 /content/smartvision_dataset/detection/images/image_001616.jpg: 480x640 3 bowls, 151.8ms\n", "image 1618/2210 /content/smartvision_dataset/detection/images/image_001617.jpg: 480x640 3 bowls, 151.9ms\n", "image 1619/2210 /content/smartvision_dataset/detection/images/image_001618.jpg: 576x640 (no detections), 195.4ms\n", "image 1620/2210 /content/smartvision_dataset/detection/images/image_001619.jpg: 480x640 (no detections), 151.6ms\n", "image 1621/2210 /content/smartvision_dataset/detection/images/image_001620.jpg: 480x640 (no detections), 147.3ms\n", "image 1622/2210 /content/smartvision_dataset/detection/images/image_001621.jpg: 480x640 (no detections), 151.5ms\n", "image 1623/2210 /content/smartvision_dataset/detection/images/image_001622.jpg: 480x640 (no detections), 150.8ms\n", "image 1624/2210 /content/smartvision_dataset/detection/images/image_001623.jpg: 480x640 (no detections), 153.2ms\n", "image 1625/2210 /content/smartvision_dataset/detection/images/image_001624.jpg: 480x640 (no detections), 171.5ms\n", "image 1626/2210 /content/smartvision_dataset/detection/images/image_001625.jpg: 480x640 5 bowls, 152.5ms\n", "image 1627/2210 /content/smartvision_dataset/detection/images/image_001626.jpg: 480x640 5 bowls, 150.4ms\n", "image 1628/2210 /content/smartvision_dataset/detection/images/image_001627.jpg: 480x640 5 bowls, 150.3ms\n", "image 1629/2210 /content/smartvision_dataset/detection/images/image_001628.jpg: 480x640 5 bowls, 152.3ms\n", "image 1630/2210 /content/smartvision_dataset/detection/images/image_001629.jpg: 480x640 5 bowls, 153.8ms\n", "image 1631/2210 /content/smartvision_dataset/detection/images/image_001630.jpg: 480x640 5 bowls, 162.4ms\n", "image 1632/2210 /content/smartvision_dataset/detection/images/image_001631.jpg: 480x640 5 bowls, 151.7ms\n", "image 1633/2210 /content/smartvision_dataset/detection/images/image_001632.jpg: 480x640 5 bowls, 148.9ms\n", "image 1634/2210 /content/smartvision_dataset/detection/images/image_001633.jpg: 480x640 5 bowls, 145.1ms\n", "image 1635/2210 /content/smartvision_dataset/detection/images/image_001634.jpg: 480x640 5 bowls, 149.5ms\n", "image 1636/2210 /content/smartvision_dataset/detection/images/image_001635.jpg: 480x640 5 bowls, 151.9ms\n", "image 1637/2210 /content/smartvision_dataset/detection/images/image_001636.jpg: 480x640 5 bowls, 149.9ms\n", "image 1638/2210 /content/smartvision_dataset/detection/images/image_001637.jpg: 480x640 5 bowls, 160.4ms\n", "image 1639/2210 /content/smartvision_dataset/detection/images/image_001638.jpg: 480x640 3 bowls, 147.7ms\n", "image 1640/2210 /content/smartvision_dataset/detection/images/image_001639.jpg: 448x640 (no detections), 141.4ms\n", "image 1641/2210 /content/smartvision_dataset/detection/images/image_001640.jpg: 448x640 (no detections), 143.6ms\n", "image 1642/2210 /content/smartvision_dataset/detection/images/image_001641.jpg: 640x544 (no detections), 176.0ms\n", "image 1643/2210 /content/smartvision_dataset/detection/images/image_001642.jpg: 480x640 (no detections), 150.5ms\n", "image 1644/2210 /content/smartvision_dataset/detection/images/image_001643.jpg: 384x640 2 persons, 142.1ms\n", "image 1645/2210 /content/smartvision_dataset/detection/images/image_001644.jpg: 448x640 6 persons, 1 potted plant, 137.7ms\n", "image 1646/2210 /content/smartvision_dataset/detection/images/image_001645.jpg: 480x640 1 bowl, 149.5ms\n", "image 1647/2210 /content/smartvision_dataset/detection/images/image_001646.jpg: 448x640 (no detections), 143.9ms\n", "image 1648/2210 /content/smartvision_dataset/detection/images/image_001647.jpg: 640x640 (no detections), 199.0ms\n", "image 1649/2210 /content/smartvision_dataset/detection/images/image_001648.jpg: 480x640 (no detections), 154.1ms\n", "image 1650/2210 /content/smartvision_dataset/detection/images/image_001649.jpg: 480x640 3 persons, 164.1ms\n", "image 1651/2210 /content/smartvision_dataset/detection/images/image_001650.jpg: 480x640 (no detections), 148.7ms\n", "image 1652/2210 /content/smartvision_dataset/detection/images/image_001651.jpg: 640x448 3 persons, 140.0ms\n", "image 1653/2210 /content/smartvision_dataset/detection/images/image_001652.jpg: 480x640 2 bowls, 1 cake, 150.6ms\n", "image 1654/2210 /content/smartvision_dataset/detection/images/image_001653.jpg: 480x640 2 bowls, 1 cake, 154.0ms\n", "image 1655/2210 /content/smartvision_dataset/detection/images/image_001654.jpg: 480x640 (no detections), 154.6ms\n", "image 1656/2210 /content/smartvision_dataset/detection/images/image_001655.jpg: 480x640 (no detections), 249.8ms\n", "image 1657/2210 /content/smartvision_dataset/detection/images/image_001656.jpg: 480x640 (no detections), 237.3ms\n", "image 1658/2210 /content/smartvision_dataset/detection/images/image_001657.jpg: 640x640 2 persons, 307.7ms\n", "image 1659/2210 /content/smartvision_dataset/detection/images/image_001658.jpg: 640x640 1 bowl, 296.2ms\n", "image 1660/2210 /content/smartvision_dataset/detection/images/image_001659.jpg: 480x640 1 bowl, 1 pizza, 234.5ms\n", "image 1661/2210 /content/smartvision_dataset/detection/images/image_001660.jpg: 480x640 2 bowls, 252.7ms\n", "image 1662/2210 /content/smartvision_dataset/detection/images/image_001661.jpg: 480x640 2 bowls, 238.4ms\n", "image 1663/2210 /content/smartvision_dataset/detection/images/image_001662.jpg: 544x640 1 person, 1 dog, 260.4ms\n", "image 1664/2210 /content/smartvision_dataset/detection/images/image_001663.jpg: 640x640 (no detections), 314.2ms\n", "image 1665/2210 /content/smartvision_dataset/detection/images/image_001664.jpg: 640x640 1 bowl, 299.8ms\n", "image 1666/2210 /content/smartvision_dataset/detection/images/image_001665.jpg: 640x640 1 bowl, 292.9ms\n", "image 1667/2210 /content/smartvision_dataset/detection/images/image_001666.jpg: 640x480 1 bowl, 264.4ms\n", "image 1668/2210 /content/smartvision_dataset/detection/images/image_001667.jpg: 480x640 (no detections), 239.8ms\n", "image 1669/2210 /content/smartvision_dataset/detection/images/image_001668.jpg: 448x640 1 bed, 235.8ms\n", "image 1670/2210 /content/smartvision_dataset/detection/images/image_001669.jpg: 448x640 1 bed, 208.0ms\n", "image 1671/2210 /content/smartvision_dataset/detection/images/image_001670.jpg: 448x640 1 bed, 139.3ms\n", "image 1672/2210 /content/smartvision_dataset/detection/images/image_001671.jpg: 480x640 7 persons, 162.0ms\n", "image 1673/2210 /content/smartvision_dataset/detection/images/image_001672.jpg: 480x640 7 persons, 150.8ms\n", "image 1674/2210 /content/smartvision_dataset/detection/images/image_001673.jpg: 448x640 2 persons, 147.2ms\n", "image 1675/2210 /content/smartvision_dataset/detection/images/image_001674.jpg: 480x640 (no detections), 154.0ms\n", "image 1676/2210 /content/smartvision_dataset/detection/images/image_001675.jpg: 480x640 (no detections), 150.3ms\n", "image 1677/2210 /content/smartvision_dataset/detection/images/image_001676.jpg: 480x640 (no detections), 151.3ms\n", "image 1678/2210 /content/smartvision_dataset/detection/images/image_001677.jpg: 480x640 1 cat, 1 bowl, 168.4ms\n", "image 1679/2210 /content/smartvision_dataset/detection/images/image_001678.jpg: 480x640 (no detections), 149.9ms\n", "image 1680/2210 /content/smartvision_dataset/detection/images/image_001679.jpg: 640x640 2 cats, 200.0ms\n", "image 1681/2210 /content/smartvision_dataset/detection/images/image_001680.jpg: 640x640 2 cats, 199.6ms\n", "image 1682/2210 /content/smartvision_dataset/detection/images/image_001681.jpg: 480x640 (no detections), 155.5ms\n", "image 1683/2210 /content/smartvision_dataset/detection/images/image_001682.jpg: 480x640 1 person, 156.8ms\n", "image 1684/2210 /content/smartvision_dataset/detection/images/image_001683.jpg: 480x640 2 persons, 1 pizza, 165.8ms\n", "image 1685/2210 /content/smartvision_dataset/detection/images/image_001684.jpg: 448x640 2 bowls, 139.7ms\n", "image 1686/2210 /content/smartvision_dataset/detection/images/image_001685.jpg: 448x640 2 bowls, 142.8ms\n", "image 1687/2210 /content/smartvision_dataset/detection/images/image_001686.jpg: 512x640 (no detections), 164.2ms\n", "image 1688/2210 /content/smartvision_dataset/detection/images/image_001687.jpg: 512x640 (no detections), 159.6ms\n", "image 1689/2210 /content/smartvision_dataset/detection/images/image_001688.jpg: 480x640 1 bowl, 150.2ms\n", "image 1690/2210 /content/smartvision_dataset/detection/images/image_001689.jpg: 448x640 (no detections), 158.9ms\n", "image 1691/2210 /content/smartvision_dataset/detection/images/image_001690.jpg: 384x640 4 persons, 124.3ms\n", "image 1692/2210 /content/smartvision_dataset/detection/images/image_001691.jpg: 384x640 4 persons, 127.1ms\n", "image 1693/2210 /content/smartvision_dataset/detection/images/image_001692.jpg: 384x640 4 persons, 126.2ms\n", "image 1694/2210 /content/smartvision_dataset/detection/images/image_001693.jpg: 640x512 2 persons, 165.0ms\n", "image 1695/2210 /content/smartvision_dataset/detection/images/image_001694.jpg: 640x448 1 potted plant, 140.8ms\n", "image 1696/2210 /content/smartvision_dataset/detection/images/image_001695.jpg: 480x640 2 bowls, 152.3ms\n", "image 1697/2210 /content/smartvision_dataset/detection/images/image_001696.jpg: 480x640 2 bowls, 161.9ms\n", "image 1698/2210 /content/smartvision_dataset/detection/images/image_001697.jpg: 448x640 (no detections), 146.9ms\n", "image 1699/2210 /content/smartvision_dataset/detection/images/image_001698.jpg: 480x640 1 bowl, 153.7ms\n", "image 1700/2210 /content/smartvision_dataset/detection/images/image_001699.jpg: 480x640 (no detections), 150.0ms\n", "image 1701/2210 /content/smartvision_dataset/detection/images/image_001700.jpg: 480x640 2 persons, 145.2ms\n", "image 1702/2210 /content/smartvision_dataset/detection/images/image_001701.jpg: 480x640 1 pizza, 149.7ms\n", "image 1703/2210 /content/smartvision_dataset/detection/images/image_001702.jpg: 480x640 (no detections), 162.3ms\n", "image 1704/2210 /content/smartvision_dataset/detection/images/image_001703.jpg: 480x640 1 pizza, 151.4ms\n", "image 1705/2210 /content/smartvision_dataset/detection/images/image_001704.jpg: 640x480 (no detections), 151.8ms\n", "image 1706/2210 /content/smartvision_dataset/detection/images/image_001705.jpg: 448x640 1 pizza, 144.1ms\n", "image 1707/2210 /content/smartvision_dataset/detection/images/image_001706.jpg: 480x640 1 bowl, 1 pizza, 151.3ms\n", "image 1708/2210 /content/smartvision_dataset/detection/images/image_001707.jpg: 480x640 1 pizza, 156.7ms\n", "image 1709/2210 /content/smartvision_dataset/detection/images/image_001708.jpg: 480x640 1 pizza, 164.8ms\n", "image 1710/2210 /content/smartvision_dataset/detection/images/image_001709.jpg: 480x640 2 persons, 1 pizza, 153.3ms\n", "image 1711/2210 /content/smartvision_dataset/detection/images/image_001710.jpg: 480x640 2 persons, 1 pizza, 155.2ms\n", "image 1712/2210 /content/smartvision_dataset/detection/images/image_001711.jpg: 480x640 2 persons, 1 pizza, 150.9ms\n", "image 1713/2210 /content/smartvision_dataset/detection/images/image_001712.jpg: 480x640 2 persons, 1 pizza, 147.5ms\n", "image 1714/2210 /content/smartvision_dataset/detection/images/image_001713.jpg: 480x640 2 persons, 1 pizza, 147.9ms\n", "image 1715/2210 /content/smartvision_dataset/detection/images/image_001714.jpg: 448x640 (no detections), 152.2ms\n", "image 1716/2210 /content/smartvision_dataset/detection/images/image_001715.jpg: 448x640 (no detections), 147.6ms\n", "image 1717/2210 /content/smartvision_dataset/detection/images/image_001716.jpg: 640x480 1 pizza, 159.8ms\n", "image 1718/2210 /content/smartvision_dataset/detection/images/image_001717.jpg: 480x640 1 pizza, 154.4ms\n", "image 1719/2210 /content/smartvision_dataset/detection/images/image_001718.jpg: 480x640 1 pizza, 148.5ms\n", "image 1720/2210 /content/smartvision_dataset/detection/images/image_001719.jpg: 640x640 (no detections), 208.7ms\n", "image 1721/2210 /content/smartvision_dataset/detection/images/image_001720.jpg: 480x640 1 pizza, 167.3ms\n", "image 1722/2210 /content/smartvision_dataset/detection/images/image_001721.jpg: 480x640 1 person, 160.4ms\n", "image 1723/2210 /content/smartvision_dataset/detection/images/image_001722.jpg: 480x640 1 pizza, 154.5ms\n", "image 1724/2210 /content/smartvision_dataset/detection/images/image_001723.jpg: 640x480 1 person, 149.2ms\n", "image 1725/2210 /content/smartvision_dataset/detection/images/image_001724.jpg: 640x640 2 pizzas, 194.2ms\n", "image 1726/2210 /content/smartvision_dataset/detection/images/image_001725.jpg: 640x640 2 pizzas, 195.2ms\n", "image 1727/2210 /content/smartvision_dataset/detection/images/image_001726.jpg: 640x640 1 pizza, 214.0ms\n", "image 1728/2210 /content/smartvision_dataset/detection/images/image_001727.jpg: 480x640 1 pizza, 152.9ms\n", "image 1729/2210 /content/smartvision_dataset/detection/images/image_001728.jpg: 480x640 1 pizza, 196.6ms\n", "image 1730/2210 /content/smartvision_dataset/detection/images/image_001729.jpg: 480x640 (no detections), 238.1ms\n", "image 1731/2210 /content/smartvision_dataset/detection/images/image_001730.jpg: 448x640 1 person, 225.3ms\n", "image 1732/2210 /content/smartvision_dataset/detection/images/image_001731.jpg: 480x640 1 bowl, 250.7ms\n", "image 1733/2210 /content/smartvision_dataset/detection/images/image_001732.jpg: 480x640 (no detections), 224.1ms\n", "image 1734/2210 /content/smartvision_dataset/detection/images/image_001733.jpg: 640x480 1 person, 224.7ms\n", "image 1735/2210 /content/smartvision_dataset/detection/images/image_001734.jpg: 480x640 (no detections), 231.4ms\n", "image 1736/2210 /content/smartvision_dataset/detection/images/image_001735.jpg: 480x640 5 persons, 1 bowl, 240.5ms\n", "image 1737/2210 /content/smartvision_dataset/detection/images/image_001736.jpg: 480x640 5 persons, 1 bowl, 227.3ms\n", "image 1738/2210 /content/smartvision_dataset/detection/images/image_001737.jpg: 480x640 3 persons, 225.2ms\n", "image 1739/2210 /content/smartvision_dataset/detection/images/image_001738.jpg: 480x640 (no detections), 239.4ms\n", "image 1740/2210 /content/smartvision_dataset/detection/images/image_001739.jpg: 480x640 (no detections), 247.3ms\n", "image 1741/2210 /content/smartvision_dataset/detection/images/image_001740.jpg: 480x640 (no detections), 229.3ms\n", "image 1742/2210 /content/smartvision_dataset/detection/images/image_001741.jpg: 480x640 1 pizza, 230.8ms\n", "image 1743/2210 /content/smartvision_dataset/detection/images/image_001742.jpg: 480x640 1 pizza, 226.2ms\n", "image 1744/2210 /content/smartvision_dataset/detection/images/image_001743.jpg: 480x640 1 pizza, 238.2ms\n", "image 1745/2210 /content/smartvision_dataset/detection/images/image_001744.jpg: 480x640 2 persons, 231.4ms\n", "image 1746/2210 /content/smartvision_dataset/detection/images/image_001745.jpg: 480x640 2 persons, 150.7ms\n", "image 1747/2210 /content/smartvision_dataset/detection/images/image_001746.jpg: 480x640 2 persons, 152.6ms\n", "image 1748/2210 /content/smartvision_dataset/detection/images/image_001747.jpg: 448x640 1 pizza, 139.5ms\n", "image 1749/2210 /content/smartvision_dataset/detection/images/image_001748.jpg: 352x640 1 pizza, 118.3ms\n", "image 1750/2210 /content/smartvision_dataset/detection/images/image_001749.jpg: 352x640 1 pizza, 115.1ms\n", "image 1751/2210 /content/smartvision_dataset/detection/images/image_001750.jpg: 352x640 1 pizza, 114.1ms\n", "image 1752/2210 /content/smartvision_dataset/detection/images/image_001751.jpg: 448x640 1 pizza, 148.7ms\n", "image 1753/2210 /content/smartvision_dataset/detection/images/image_001752.jpg: 480x640 2 persons, 149.1ms\n", "image 1754/2210 /content/smartvision_dataset/detection/images/image_001753.jpg: 480x640 2 persons, 145.1ms\n", "image 1755/2210 /content/smartvision_dataset/detection/images/image_001754.jpg: 384x640 1 pizza, 128.4ms\n", "image 1756/2210 /content/smartvision_dataset/detection/images/image_001755.jpg: 640x512 1 pizza, 162.0ms\n", "image 1757/2210 /content/smartvision_dataset/detection/images/image_001756.jpg: 448x640 1 pizza, 143.3ms\n", "image 1758/2210 /content/smartvision_dataset/detection/images/image_001757.jpg: 480x640 2 persons, 1 pizza, 161.0ms\n", "image 1759/2210 /content/smartvision_dataset/detection/images/image_001758.jpg: 480x640 2 pizzas, 148.9ms\n", "image 1760/2210 /content/smartvision_dataset/detection/images/image_001759.jpg: 480x640 2 pizzas, 150.7ms\n", "image 1761/2210 /content/smartvision_dataset/detection/images/image_001760.jpg: 480x640 2 pizzas, 149.7ms\n", "image 1762/2210 /content/smartvision_dataset/detection/images/image_001761.jpg: 480x640 2 pizzas, 150.6ms\n", "image 1763/2210 /content/smartvision_dataset/detection/images/image_001762.jpg: 480x640 2 pizzas, 153.0ms\n", "image 1764/2210 /content/smartvision_dataset/detection/images/image_001763.jpg: 480x640 2 pizzas, 162.7ms\n", "image 1765/2210 /content/smartvision_dataset/detection/images/image_001764.jpg: 480x640 2 pizzas, 154.1ms\n", "image 1766/2210 /content/smartvision_dataset/detection/images/image_001765.jpg: 480x640 2 pizzas, 147.4ms\n", "image 1767/2210 /content/smartvision_dataset/detection/images/image_001766.jpg: 480x640 2 pizzas, 156.4ms\n", "image 1768/2210 /content/smartvision_dataset/detection/images/image_001767.jpg: 480x640 2 pizzas, 149.4ms\n", "image 1769/2210 /content/smartvision_dataset/detection/images/image_001768.jpg: 480x640 8 persons, 153.5ms\n", "image 1770/2210 /content/smartvision_dataset/detection/images/image_001769.jpg: 384x640 1 pizza, 129.1ms\n", "image 1771/2210 /content/smartvision_dataset/detection/images/image_001770.jpg: 480x640 1 bowl, 165.6ms\n", "image 1772/2210 /content/smartvision_dataset/detection/images/image_001771.jpg: 480x640 2 pizzas, 154.4ms\n", "image 1773/2210 /content/smartvision_dataset/detection/images/image_001772.jpg: 480x640 (no detections), 154.3ms\n", "image 1774/2210 /content/smartvision_dataset/detection/images/image_001773.jpg: 640x512 (no detections), 163.2ms\n", "image 1775/2210 /content/smartvision_dataset/detection/images/image_001774.jpg: 640x512 (no detections), 165.3ms\n", "image 1776/2210 /content/smartvision_dataset/detection/images/image_001775.jpg: 480x640 1 pizza, 146.5ms\n", "image 1777/2210 /content/smartvision_dataset/detection/images/image_001776.jpg: 448x640 1 person, 153.8ms\n", "image 1778/2210 /content/smartvision_dataset/detection/images/image_001777.jpg: 480x640 1 pizza, 146.2ms\n", "image 1779/2210 /content/smartvision_dataset/detection/images/image_001778.jpg: 640x640 1 pizza, 200.7ms\n", "image 1780/2210 /content/smartvision_dataset/detection/images/image_001779.jpg: 448x640 1 person, 1 bowl, 139.4ms\n", "image 1781/2210 /content/smartvision_dataset/detection/images/image_001780.jpg: 480x640 1 person, 155.3ms\n", "image 1782/2210 /content/smartvision_dataset/detection/images/image_001781.jpg: 480x640 1 person, 146.8ms\n", "image 1783/2210 /content/smartvision_dataset/detection/images/image_001782.jpg: 480x640 7 persons, 163.5ms\n", "image 1784/2210 /content/smartvision_dataset/detection/images/image_001783.jpg: 480x640 7 persons, 150.0ms\n", "image 1785/2210 /content/smartvision_dataset/detection/images/image_001784.jpg: 480x640 7 persons, 155.4ms\n", "image 1786/2210 /content/smartvision_dataset/detection/images/image_001785.jpg: 448x640 (no detections), 142.6ms\n", "image 1787/2210 /content/smartvision_dataset/detection/images/image_001786.jpg: 640x416 4 persons, 137.4ms\n", "image 1788/2210 /content/smartvision_dataset/detection/images/image_001787.jpg: 512x640 (no detections), 161.9ms\n", "image 1789/2210 /content/smartvision_dataset/detection/images/image_001788.jpg: 384x640 1 person, 142.8ms\n", "image 1790/2210 /content/smartvision_dataset/detection/images/image_001789.jpg: 384x640 2 persons, 122.9ms\n", "image 1791/2210 /content/smartvision_dataset/detection/images/image_001790.jpg: 384x640 2 persons, 127.2ms\n", "image 1792/2210 /content/smartvision_dataset/detection/images/image_001791.jpg: 384x640 2 persons, 124.9ms\n", "image 1793/2210 /content/smartvision_dataset/detection/images/image_001792.jpg: 448x640 6 persons, 1 potted plant, 137.4ms\n", "image 1794/2210 /content/smartvision_dataset/detection/images/image_001793.jpg: 640x480 (no detections), 152.8ms\n", "image 1795/2210 /content/smartvision_dataset/detection/images/image_001794.jpg: 640x448 3 persons, 141.8ms\n", "image 1796/2210 /content/smartvision_dataset/detection/images/image_001795.jpg: 480x640 2 bowls, 1 cake, 162.2ms\n", "image 1797/2210 /content/smartvision_dataset/detection/images/image_001796.jpg: 544x640 1 person, 169.4ms\n", "image 1798/2210 /content/smartvision_dataset/detection/images/image_001797.jpg: 480x640 3 persons, 151.0ms\n", "image 1799/2210 /content/smartvision_dataset/detection/images/image_001798.jpg: 480x640 1 pizza, 152.4ms\n", "image 1800/2210 /content/smartvision_dataset/detection/images/image_001799.jpg: 640x448 5 persons, 142.3ms\n", "image 1801/2210 /content/smartvision_dataset/detection/images/image_001800.jpg: 640x448 5 persons, 144.5ms\n", "image 1802/2210 /content/smartvision_dataset/detection/images/image_001801.jpg: 640x448 5 persons, 156.7ms\n", "image 1803/2210 /content/smartvision_dataset/detection/images/image_001802.jpg: 640x640 (no detections), 200.3ms\n", "image 1804/2210 /content/smartvision_dataset/detection/images/image_001803.jpg: 640x640 (no detections), 204.1ms\n", "image 1805/2210 /content/smartvision_dataset/detection/images/image_001804.jpg: 640x480 1 bowl, 156.0ms\n", "image 1806/2210 /content/smartvision_dataset/detection/images/image_001805.jpg: 480x640 (no detections), 145.8ms\n", "image 1807/2210 /content/smartvision_dataset/detection/images/image_001806.jpg: 480x640 (no detections), 235.0ms\n", "image 1808/2210 /content/smartvision_dataset/detection/images/image_001807.jpg: 448x640 3 persons, 225.2ms\n", "image 1809/2210 /content/smartvision_dataset/detection/images/image_001808.jpg: 448x640 3 persons, 229.4ms\n", "image 1810/2210 /content/smartvision_dataset/detection/images/image_001809.jpg: 448x640 3 persons, 221.5ms\n", "image 1811/2210 /content/smartvision_dataset/detection/images/image_001810.jpg: 448x640 3 persons, 214.3ms\n", "image 1812/2210 /content/smartvision_dataset/detection/images/image_001811.jpg: 448x640 3 persons, 226.7ms\n", "image 1813/2210 /content/smartvision_dataset/detection/images/image_001812.jpg: 448x640 3 persons, 220.8ms\n", "image 1814/2210 /content/smartvision_dataset/detection/images/image_001813.jpg: 448x640 3 persons, 220.5ms\n", "image 1815/2210 /content/smartvision_dataset/detection/images/image_001814.jpg: 448x640 3 persons, 219.4ms\n", "image 1816/2210 /content/smartvision_dataset/detection/images/image_001815.jpg: 448x640 3 persons, 219.1ms\n", "image 1817/2210 /content/smartvision_dataset/detection/images/image_001816.jpg: 448x640 3 persons, 222.1ms\n", "image 1818/2210 /content/smartvision_dataset/detection/images/image_001817.jpg: 448x640 3 persons, 215.6ms\n", "image 1819/2210 /content/smartvision_dataset/detection/images/image_001818.jpg: 448x640 3 persons, 210.1ms\n", "image 1820/2210 /content/smartvision_dataset/detection/images/image_001819.jpg: 448x640 3 persons, 216.4ms\n", "image 1821/2210 /content/smartvision_dataset/detection/images/image_001820.jpg: 448x640 3 persons, 257.1ms\n", "image 1822/2210 /content/smartvision_dataset/detection/images/image_001821.jpg: 640x448 1 person, 259.1ms\n", "image 1823/2210 /content/smartvision_dataset/detection/images/image_001822.jpg: 480x640 1 bowl, 231.2ms\n", "image 1824/2210 /content/smartvision_dataset/detection/images/image_001823.jpg: 480x640 12 persons, 150.4ms\n", "image 1825/2210 /content/smartvision_dataset/detection/images/image_001824.jpg: 480x640 12 persons, 150.9ms\n", "image 1826/2210 /content/smartvision_dataset/detection/images/image_001825.jpg: 480x640 12 persons, 153.6ms\n", "image 1827/2210 /content/smartvision_dataset/detection/images/image_001826.jpg: 480x640 12 persons, 164.0ms\n", "image 1828/2210 /content/smartvision_dataset/detection/images/image_001827.jpg: 480x640 12 persons, 150.7ms\n", "image 1829/2210 /content/smartvision_dataset/detection/images/image_001828.jpg: 480x640 12 persons, 154.0ms\n", "image 1830/2210 /content/smartvision_dataset/detection/images/image_001829.jpg: 480x640 12 persons, 146.6ms\n", "image 1831/2210 /content/smartvision_dataset/detection/images/image_001830.jpg: 480x640 12 persons, 152.5ms\n", "image 1832/2210 /content/smartvision_dataset/detection/images/image_001831.jpg: 480x640 12 persons, 170.7ms\n", "image 1833/2210 /content/smartvision_dataset/detection/images/image_001832.jpg: 480x640 12 persons, 168.9ms\n", "image 1834/2210 /content/smartvision_dataset/detection/images/image_001833.jpg: 480x640 12 persons, 147.5ms\n", "image 1835/2210 /content/smartvision_dataset/detection/images/image_001834.jpg: 480x640 12 persons, 147.3ms\n", "image 1836/2210 /content/smartvision_dataset/detection/images/image_001835.jpg: 480x640 12 persons, 148.1ms\n", "image 1837/2210 /content/smartvision_dataset/detection/images/image_001836.jpg: 480x640 12 persons, 152.4ms\n", "image 1838/2210 /content/smartvision_dataset/detection/images/image_001837.jpg: 480x640 2 bowls, 155.3ms\n", "image 1839/2210 /content/smartvision_dataset/detection/images/image_001838.jpg: 480x640 (no detections), 165.4ms\n", "image 1840/2210 /content/smartvision_dataset/detection/images/image_001839.jpg: 544x640 1 person, 171.8ms\n", "image 1841/2210 /content/smartvision_dataset/detection/images/image_001840.jpg: 640x480 (no detections), 153.8ms\n", "image 1842/2210 /content/smartvision_dataset/detection/images/image_001841.jpg: 640x480 (no detections), 149.7ms\n", "image 1843/2210 /content/smartvision_dataset/detection/images/image_001842.jpg: 640x480 (no detections), 151.7ms\n", "image 1844/2210 /content/smartvision_dataset/detection/images/image_001843.jpg: 640x480 (no detections), 150.0ms\n", "image 1845/2210 /content/smartvision_dataset/detection/images/image_001844.jpg: 640x480 (no detections), 166.8ms\n", "image 1846/2210 /content/smartvision_dataset/detection/images/image_001845.jpg: 640x480 (no detections), 150.6ms\n", "image 1847/2210 /content/smartvision_dataset/detection/images/image_001846.jpg: 640x480 (no detections), 150.1ms\n", "image 1848/2210 /content/smartvision_dataset/detection/images/image_001847.jpg: 640x480 (no detections), 148.0ms\n", "image 1849/2210 /content/smartvision_dataset/detection/images/image_001848.jpg: 640x480 (no detections), 152.7ms\n", "image 1850/2210 /content/smartvision_dataset/detection/images/image_001849.jpg: 640x480 (no detections), 151.6ms\n", "image 1851/2210 /content/smartvision_dataset/detection/images/image_001850.jpg: 640x480 (no detections), 168.4ms\n", "image 1852/2210 /content/smartvision_dataset/detection/images/image_001851.jpg: 480x640 12 persons, 148.1ms\n", "image 1853/2210 /content/smartvision_dataset/detection/images/image_001852.jpg: 640x448 (no detections), 141.7ms\n", "image 1854/2210 /content/smartvision_dataset/detection/images/image_001853.jpg: 640x448 (no detections), 139.6ms\n", "image 1855/2210 /content/smartvision_dataset/detection/images/image_001854.jpg: 640x448 (no detections), 145.1ms\n", "image 1856/2210 /content/smartvision_dataset/detection/images/image_001855.jpg: 640x448 (no detections), 142.1ms\n", "image 1857/2210 /content/smartvision_dataset/detection/images/image_001856.jpg: 640x448 (no detections), 141.7ms\n", "image 1858/2210 /content/smartvision_dataset/detection/images/image_001857.jpg: 640x448 (no detections), 156.7ms\n", "image 1859/2210 /content/smartvision_dataset/detection/images/image_001858.jpg: 640x448 (no detections), 140.2ms\n", "image 1860/2210 /content/smartvision_dataset/detection/images/image_001859.jpg: 640x448 (no detections), 141.8ms\n", "image 1861/2210 /content/smartvision_dataset/detection/images/image_001860.jpg: 640x448 (no detections), 143.7ms\n", "image 1862/2210 /content/smartvision_dataset/detection/images/image_001861.jpg: 640x448 (no detections), 142.0ms\n", "image 1863/2210 /content/smartvision_dataset/detection/images/image_001862.jpg: 640x448 (no detections), 144.4ms\n", "image 1864/2210 /content/smartvision_dataset/detection/images/image_001863.jpg: 640x448 (no detections), 156.5ms\n", "image 1865/2210 /content/smartvision_dataset/detection/images/image_001864.jpg: 640x448 (no detections), 141.4ms\n", "image 1866/2210 /content/smartvision_dataset/detection/images/image_001865.jpg: 640x448 (no detections), 141.4ms\n", "image 1867/2210 /content/smartvision_dataset/detection/images/image_001866.jpg: 640x480 (no detections), 153.2ms\n", "image 1868/2210 /content/smartvision_dataset/detection/images/image_001867.jpg: 480x640 (no detections), 149.2ms\n", "image 1869/2210 /content/smartvision_dataset/detection/images/image_001868.jpg: 640x640 2 persons, 199.2ms\n", "image 1870/2210 /content/smartvision_dataset/detection/images/image_001869.jpg: 640x640 2 persons, 212.9ms\n", "image 1871/2210 /content/smartvision_dataset/detection/images/image_001870.jpg: 480x640 2 persons, 149.3ms\n", "image 1872/2210 /content/smartvision_dataset/detection/images/image_001871.jpg: 480x640 2 persons, 151.6ms\n", "image 1873/2210 /content/smartvision_dataset/detection/images/image_001872.jpg: 480x640 2 persons, 155.0ms\n", "image 1874/2210 /content/smartvision_dataset/detection/images/image_001873.jpg: 480x640 2 persons, 150.6ms\n", "image 1875/2210 /content/smartvision_dataset/detection/images/image_001874.jpg: 640x416 4 persons, 139.1ms\n", "image 1876/2210 /content/smartvision_dataset/detection/images/image_001875.jpg: 640x416 4 persons, 149.1ms\n", "image 1877/2210 /content/smartvision_dataset/detection/images/image_001876.jpg: 480x640 (no detections), 148.8ms\n", "image 1878/2210 /content/smartvision_dataset/detection/images/image_001877.jpg: 640x448 (no detections), 140.3ms\n", "image 1879/2210 /content/smartvision_dataset/detection/images/image_001878.jpg: 640x448 (no detections), 146.6ms\n", "image 1880/2210 /content/smartvision_dataset/detection/images/image_001879.jpg: 448x640 2 persons, 140.0ms\n", "image 1881/2210 /content/smartvision_dataset/detection/images/image_001880.jpg: 448x640 2 persons, 147.7ms\n", "image 1882/2210 /content/smartvision_dataset/detection/images/image_001881.jpg: 448x640 2 persons, 141.0ms\n", "image 1883/2210 /content/smartvision_dataset/detection/images/image_001882.jpg: 448x640 2 persons, 152.9ms\n", "image 1884/2210 /content/smartvision_dataset/detection/images/image_001883.jpg: 448x640 2 persons, 203.1ms\n", "image 1885/2210 /content/smartvision_dataset/detection/images/image_001884.jpg: 448x640 2 persons, 224.2ms\n", "image 1886/2210 /content/smartvision_dataset/detection/images/image_001885.jpg: 448x640 2 persons, 235.1ms\n", "image 1887/2210 /content/smartvision_dataset/detection/images/image_001886.jpg: 448x640 2 persons, 228.8ms\n", "image 1888/2210 /content/smartvision_dataset/detection/images/image_001887.jpg: 448x640 2 persons, 223.0ms\n", "image 1889/2210 /content/smartvision_dataset/detection/images/image_001888.jpg: 480x640 (no detections), 234.0ms\n", "image 1890/2210 /content/smartvision_dataset/detection/images/image_001889.jpg: 480x640 5 persons, 241.8ms\n", "image 1891/2210 /content/smartvision_dataset/detection/images/image_001890.jpg: 480x640 5 persons, 235.3ms\n", "image 1892/2210 /content/smartvision_dataset/detection/images/image_001891.jpg: 640x544 2 persons, 270.4ms\n", "image 1893/2210 /content/smartvision_dataset/detection/images/image_001892.jpg: 640x544 2 persons, 257.3ms\n", "image 1894/2210 /content/smartvision_dataset/detection/images/image_001893.jpg: 640x544 2 persons, 263.3ms\n", "image 1895/2210 /content/smartvision_dataset/detection/images/image_001894.jpg: 640x544 2 persons, 258.4ms\n", "image 1896/2210 /content/smartvision_dataset/detection/images/image_001895.jpg: 640x544 2 persons, 266.9ms\n", "image 1897/2210 /content/smartvision_dataset/detection/images/image_001896.jpg: 640x544 2 persons, 265.2ms\n", "image 1898/2210 /content/smartvision_dataset/detection/images/image_001897.jpg: 640x544 2 persons, 266.9ms\n", "image 1899/2210 /content/smartvision_dataset/detection/images/image_001898.jpg: 640x544 2 persons, 263.4ms\n", "image 1900/2210 /content/smartvision_dataset/detection/images/image_001899.jpg: 640x544 2 persons, 184.9ms\n", "image 1901/2210 /content/smartvision_dataset/detection/images/image_001900.jpg: 640x544 2 persons, 167.0ms\n", "image 1902/2210 /content/smartvision_dataset/detection/images/image_001901.jpg: 640x544 2 persons, 166.0ms\n", "image 1903/2210 /content/smartvision_dataset/detection/images/image_001902.jpg: 640x544 2 persons, 163.6ms\n", "image 1904/2210 /content/smartvision_dataset/detection/images/image_001903.jpg: 640x544 2 persons, 166.6ms\n", "image 1905/2210 /content/smartvision_dataset/detection/images/image_001904.jpg: 640x544 2 persons, 180.5ms\n", "image 1906/2210 /content/smartvision_dataset/detection/images/image_001905.jpg: 640x480 1 person, 152.1ms\n", "image 1907/2210 /content/smartvision_dataset/detection/images/image_001906.jpg: 480x640 1 bed, 150.1ms\n", "image 1908/2210 /content/smartvision_dataset/detection/images/image_001907.jpg: 480x640 (no detections), 149.9ms\n", "image 1909/2210 /content/smartvision_dataset/detection/images/image_001908.jpg: 480x640 (no detections), 147.9ms\n", "image 1910/2210 /content/smartvision_dataset/detection/images/image_001909.jpg: 480x640 (no detections), 156.9ms\n", "image 1911/2210 /content/smartvision_dataset/detection/images/image_001910.jpg: 640x384 4 persons, 1 motorcycle, 125.0ms\n", "image 1912/2210 /content/smartvision_dataset/detection/images/image_001911.jpg: 448x640 (no detections), 159.5ms\n", "image 1913/2210 /content/smartvision_dataset/detection/images/image_001912.jpg: 448x640 (no detections), 145.0ms\n", "image 1914/2210 /content/smartvision_dataset/detection/images/image_001913.jpg: 448x640 (no detections), 140.6ms\n", "image 1915/2210 /content/smartvision_dataset/detection/images/image_001914.jpg: 448x640 (no detections), 144.5ms\n", "image 1916/2210 /content/smartvision_dataset/detection/images/image_001915.jpg: 448x640 6 persons, 1 potted plant, 140.2ms\n", "image 1917/2210 /content/smartvision_dataset/detection/images/image_001916.jpg: 448x640 6 persons, 1 potted plant, 142.1ms\n", "image 1918/2210 /content/smartvision_dataset/detection/images/image_001917.jpg: 448x640 6 persons, 1 potted plant, 157.7ms\n", "image 1919/2210 /content/smartvision_dataset/detection/images/image_001918.jpg: 448x640 6 persons, 1 potted plant, 141.9ms\n", "image 1920/2210 /content/smartvision_dataset/detection/images/image_001919.jpg: 480x640 (no detections), 154.1ms\n", "image 1921/2210 /content/smartvision_dataset/detection/images/image_001920.jpg: 480x640 (no detections), 152.8ms\n", "image 1922/2210 /content/smartvision_dataset/detection/images/image_001921.jpg: 480x640 (no detections), 150.3ms\n", "image 1923/2210 /content/smartvision_dataset/detection/images/image_001922.jpg: 480x640 (no detections), 147.0ms\n", "image 1924/2210 /content/smartvision_dataset/detection/images/image_001923.jpg: 480x640 (no detections), 148.8ms\n", "image 1925/2210 /content/smartvision_dataset/detection/images/image_001924.jpg: 448x640 3 buss, 1 train, 1 potted plant, 145.4ms\n", "image 1926/2210 /content/smartvision_dataset/detection/images/image_001925.jpg: 448x640 3 buss, 1 train, 1 potted plant, 141.6ms\n", "image 1927/2210 /content/smartvision_dataset/detection/images/image_001926.jpg: 480x640 1 potted plant, 150.0ms\n", "image 1928/2210 /content/smartvision_dataset/detection/images/image_001927.jpg: 480x640 1 potted plant, 150.3ms\n", "image 1929/2210 /content/smartvision_dataset/detection/images/image_001928.jpg: 480x640 (no detections), 149.6ms\n", "image 1930/2210 /content/smartvision_dataset/detection/images/image_001929.jpg: 480x640 (no detections), 148.8ms\n", "image 1931/2210 /content/smartvision_dataset/detection/images/image_001930.jpg: 480x640 (no detections), 159.5ms\n", "image 1932/2210 /content/smartvision_dataset/detection/images/image_001931.jpg: 480x640 (no detections), 154.1ms\n", "image 1933/2210 /content/smartvision_dataset/detection/images/image_001932.jpg: 480x640 (no detections), 148.0ms\n", "image 1934/2210 /content/smartvision_dataset/detection/images/image_001933.jpg: 480x640 3 persons, 150.5ms\n", "image 1935/2210 /content/smartvision_dataset/detection/images/image_001934.jpg: 480x640 3 persons, 154.6ms\n", "image 1936/2210 /content/smartvision_dataset/detection/images/image_001935.jpg: 480x640 3 persons, 150.4ms\n", "image 1937/2210 /content/smartvision_dataset/detection/images/image_001936.jpg: 480x640 3 persons, 163.0ms\n", "image 1938/2210 /content/smartvision_dataset/detection/images/image_001937.jpg: 480x640 3 persons, 150.3ms\n", "image 1939/2210 /content/smartvision_dataset/detection/images/image_001938.jpg: 480x640 3 persons, 148.7ms\n", "image 1940/2210 /content/smartvision_dataset/detection/images/image_001939.jpg: 480x640 3 persons, 156.6ms\n", "image 1941/2210 /content/smartvision_dataset/detection/images/image_001940.jpg: 448x640 (no detections), 143.4ms\n", "image 1942/2210 /content/smartvision_dataset/detection/images/image_001941.jpg: 448x640 (no detections), 142.6ms\n", "image 1943/2210 /content/smartvision_dataset/detection/images/image_001942.jpg: 448x640 (no detections), 144.4ms\n", "image 1944/2210 /content/smartvision_dataset/detection/images/image_001943.jpg: 448x640 (no detections), 155.4ms\n", "image 1945/2210 /content/smartvision_dataset/detection/images/image_001944.jpg: 448x640 (no detections), 138.9ms\n", "image 1946/2210 /content/smartvision_dataset/detection/images/image_001945.jpg: 640x448 3 persons, 141.4ms\n", "image 1947/2210 /content/smartvision_dataset/detection/images/image_001946.jpg: 640x448 3 persons, 141.5ms\n", "image 1948/2210 /content/smartvision_dataset/detection/images/image_001947.jpg: 640x448 3 persons, 141.5ms\n", "image 1949/2210 /content/smartvision_dataset/detection/images/image_001948.jpg: 640x448 3 persons, 145.1ms\n", "image 1950/2210 /content/smartvision_dataset/detection/images/image_001949.jpg: 640x448 3 persons, 160.0ms\n", "image 1951/2210 /content/smartvision_dataset/detection/images/image_001950.jpg: 640x448 3 persons, 143.7ms\n", "image 1952/2210 /content/smartvision_dataset/detection/images/image_001951.jpg: 544x640 1 person, 171.8ms\n", "image 1953/2210 /content/smartvision_dataset/detection/images/image_001952.jpg: 480x640 2 airplanes, 146.1ms\n", "image 1954/2210 /content/smartvision_dataset/detection/images/image_001953.jpg: 544x640 1 person, 171.5ms\n", "image 1955/2210 /content/smartvision_dataset/detection/images/image_001954.jpg: 448x640 1 person, 141.9ms\n", "image 1956/2210 /content/smartvision_dataset/detection/images/image_001955.jpg: 640x480 6 persons, 162.8ms\n", "image 1957/2210 /content/smartvision_dataset/detection/images/image_001956.jpg: 640x480 6 persons, 150.2ms\n", "image 1958/2210 /content/smartvision_dataset/detection/images/image_001957.jpg: 640x448 (no detections), 144.4ms\n", "image 1959/2210 /content/smartvision_dataset/detection/images/image_001958.jpg: 480x640 2 cats, 145.8ms\n", "image 1960/2210 /content/smartvision_dataset/detection/images/image_001959.jpg: 640x480 1 person, 204.3ms\n", "image 1961/2210 /content/smartvision_dataset/detection/images/image_001960.jpg: 416x640 1 cat, 208.3ms\n", "image 1962/2210 /content/smartvision_dataset/detection/images/image_001961.jpg: 480x640 1 bed, 235.6ms\n", "image 1963/2210 /content/smartvision_dataset/detection/images/image_001962.jpg: 480x640 1 bed, 237.6ms\n", "image 1964/2210 /content/smartvision_dataset/detection/images/image_001963.jpg: 448x640 (no detections), 213.9ms\n", "image 1965/2210 /content/smartvision_dataset/detection/images/image_001964.jpg: 448x640 (no detections), 215.2ms\n", "image 1966/2210 /content/smartvision_dataset/detection/images/image_001965.jpg: 384x640 2 cats, 1 couch, 191.8ms\n", "image 1967/2210 /content/smartvision_dataset/detection/images/image_001966.jpg: 480x640 1 potted plant, 244.2ms\n", "image 1968/2210 /content/smartvision_dataset/detection/images/image_001967.jpg: 480x640 1 potted plant, 228.8ms\n", "image 1969/2210 /content/smartvision_dataset/detection/images/image_001968.jpg: 480x640 3 persons, 225.7ms\n", "image 1970/2210 /content/smartvision_dataset/detection/images/image_001969.jpg: 448x640 (no detections), 219.1ms\n", "image 1971/2210 /content/smartvision_dataset/detection/images/image_001970.jpg: 640x480 2 persons, 1 couch, 247.3ms\n", "image 1972/2210 /content/smartvision_dataset/detection/images/image_001971.jpg: 480x640 1 person, 1 couch, 1 bed, 224.3ms\n", "image 1973/2210 /content/smartvision_dataset/detection/images/image_001972.jpg: 480x640 1 couch, 226.7ms\n", "image 1974/2210 /content/smartvision_dataset/detection/images/image_001973.jpg: 480x640 3 persons, 239.1ms\n", "image 1975/2210 /content/smartvision_dataset/detection/images/image_001974.jpg: 480x640 3 persons, 248.8ms\n", "image 1976/2210 /content/smartvision_dataset/detection/images/image_001975.jpg: 480x640 (no detections), 236.3ms\n", "image 1977/2210 /content/smartvision_dataset/detection/images/image_001976.jpg: 416x640 1 person, 181.7ms\n", "image 1978/2210 /content/smartvision_dataset/detection/images/image_001977.jpg: 448x640 1 bed, 138.2ms\n", "image 1979/2210 /content/smartvision_dataset/detection/images/image_001978.jpg: 448x640 1 bed, 141.4ms\n", "image 1980/2210 /content/smartvision_dataset/detection/images/image_001979.jpg: 480x640 1 person, 148.7ms\n", "image 1981/2210 /content/smartvision_dataset/detection/images/image_001980.jpg: 480x640 1 person, 161.7ms\n", "image 1982/2210 /content/smartvision_dataset/detection/images/image_001981.jpg: 448x640 (no detections), 137.7ms\n", "image 1983/2210 /content/smartvision_dataset/detection/images/image_001982.jpg: 448x640 1 couch, 145.3ms\n", "image 1984/2210 /content/smartvision_dataset/detection/images/image_001983.jpg: 480x640 (no detections), 149.6ms\n", "image 1985/2210 /content/smartvision_dataset/detection/images/image_001984.jpg: 480x640 (no detections), 156.6ms\n", "image 1986/2210 /content/smartvision_dataset/detection/images/image_001985.jpg: 480x640 1 person, 2 couchs, 147.6ms\n", "image 1987/2210 /content/smartvision_dataset/detection/images/image_001986.jpg: 480x640 (no detections), 163.9ms\n", "image 1988/2210 /content/smartvision_dataset/detection/images/image_001987.jpg: 480x640 4 persons, 154.4ms\n", "image 1989/2210 /content/smartvision_dataset/detection/images/image_001988.jpg: 480x640 4 persons, 153.2ms\n", "image 1990/2210 /content/smartvision_dataset/detection/images/image_001989.jpg: 480x640 3 persons, 148.3ms\n", "image 1991/2210 /content/smartvision_dataset/detection/images/image_001990.jpg: 448x640 (no detections), 144.3ms\n", "image 1992/2210 /content/smartvision_dataset/detection/images/image_001991.jpg: 448x640 (no detections), 140.3ms\n", "image 1993/2210 /content/smartvision_dataset/detection/images/image_001992.jpg: 384x640 4 persons, 126.6ms\n", "image 1994/2210 /content/smartvision_dataset/detection/images/image_001993.jpg: 448x640 1 bed, 150.6ms\n", "image 1995/2210 /content/smartvision_dataset/detection/images/image_001994.jpg: 480x640 1 person, 2 couchs, 152.7ms\n", "image 1996/2210 /content/smartvision_dataset/detection/images/image_001995.jpg: 480x640 1 couch, 1 bed, 147.8ms\n", "image 1997/2210 /content/smartvision_dataset/detection/images/image_001996.jpg: 448x640 (no detections), 163.7ms\n", "image 1998/2210 /content/smartvision_dataset/detection/images/image_001997.jpg: 480x640 (no detections), 152.0ms\n", "image 1999/2210 /content/smartvision_dataset/detection/images/image_001998.jpg: 448x640 1 cat, 140.9ms\n", "image 2000/2210 /content/smartvision_dataset/detection/images/image_001999.jpg: 480x640 (no detections), 163.9ms\n", "image 2001/2210 /content/smartvision_dataset/detection/images/image_002000.jpg: 448x640 (no detections), 138.4ms\n", "image 2002/2210 /content/smartvision_dataset/detection/images/image_002001.jpg: 480x640 (no detections), 148.2ms\n", "image 2003/2210 /content/smartvision_dataset/detection/images/image_002002.jpg: 640x480 (no detections), 157.5ms\n", "image 2004/2210 /content/smartvision_dataset/detection/images/image_002003.jpg: 480x640 1 couch, 153.8ms\n", "image 2005/2210 /content/smartvision_dataset/detection/images/image_002004.jpg: 480x640 (no detections), 151.7ms\n", "image 2006/2210 /content/smartvision_dataset/detection/images/image_002005.jpg: 480x640 1 couch, 164.3ms\n", "image 2007/2210 /content/smartvision_dataset/detection/images/image_002006.jpg: 480x640 1 couch, 153.9ms\n", "image 2008/2210 /content/smartvision_dataset/detection/images/image_002007.jpg: 480x640 1 couch, 152.6ms\n", "image 2009/2210 /content/smartvision_dataset/detection/images/image_002008.jpg: 448x640 (no detections), 141.4ms\n", "image 2010/2210 /content/smartvision_dataset/detection/images/image_002009.jpg: 480x640 5 persons, 154.1ms\n", "image 2011/2210 /content/smartvision_dataset/detection/images/image_002010.jpg: 480x640 5 persons, 156.4ms\n", "image 2012/2210 /content/smartvision_dataset/detection/images/image_002011.jpg: 480x640 1 bicycle, 149.9ms\n", "image 2013/2210 /content/smartvision_dataset/detection/images/image_002012.jpg: 480x640 5 persons, 1 couch, 160.3ms\n", "image 2014/2210 /content/smartvision_dataset/detection/images/image_002013.jpg: 480x640 2 beds, 148.3ms\n", "image 2015/2210 /content/smartvision_dataset/detection/images/image_002014.jpg: 480x640 2 dogs, 2 couchs, 151.8ms\n", "image 2016/2210 /content/smartvision_dataset/detection/images/image_002015.jpg: 640x640 (no detections), 199.6ms\n", "image 2017/2210 /content/smartvision_dataset/detection/images/image_002016.jpg: 640x640 1 person, 1 couch, 201.6ms\n", "image 2018/2210 /content/smartvision_dataset/detection/images/image_002017.jpg: 480x640 (no detections), 172.4ms\n", "image 2019/2210 /content/smartvision_dataset/detection/images/image_002018.jpg: 640x480 1 bed, 155.0ms\n", "image 2020/2210 /content/smartvision_dataset/detection/images/image_002019.jpg: 480x640 2 cats, 152.3ms\n", "image 2021/2210 /content/smartvision_dataset/detection/images/image_002020.jpg: 480x640 2 cats, 158.1ms\n", "image 2022/2210 /content/smartvision_dataset/detection/images/image_002021.jpg: 480x640 3 persons, 2 couchs, 156.3ms\n", "image 2023/2210 /content/smartvision_dataset/detection/images/image_002022.jpg: 384x640 1 person, 128.4ms\n", "image 2024/2210 /content/smartvision_dataset/detection/images/image_002023.jpg: 512x640 1 person, 177.5ms\n", "image 2025/2210 /content/smartvision_dataset/detection/images/image_002024.jpg: 448x640 (no detections), 145.3ms\n", "image 2026/2210 /content/smartvision_dataset/detection/images/image_002025.jpg: 480x640 1 person, 3 couchs, 1 bed, 166.6ms\n", "image 2027/2210 /content/smartvision_dataset/detection/images/image_002026.jpg: 576x640 2 persons, 1 couch, 183.8ms\n", "image 2028/2210 /content/smartvision_dataset/detection/images/image_002027.jpg: 384x640 4 persons, 129.6ms\n", "image 2029/2210 /content/smartvision_dataset/detection/images/image_002028.jpg: 480x640 (no detections), 152.6ms\n", "image 2030/2210 /content/smartvision_dataset/detection/images/image_002029.jpg: 448x640 (no detections), 141.3ms\n", "image 2031/2210 /content/smartvision_dataset/detection/images/image_002030.jpg: 640x640 1 potted plant, 209.3ms\n", "image 2032/2210 /content/smartvision_dataset/detection/images/image_002031.jpg: 640x640 1 potted plant, 201.4ms\n", "image 2033/2210 /content/smartvision_dataset/detection/images/image_002032.jpg: 448x640 1 person, 1 couch, 144.2ms\n", "image 2034/2210 /content/smartvision_dataset/detection/images/image_002033.jpg: 480x640 1 person, 155.6ms\n", "image 2035/2210 /content/smartvision_dataset/detection/images/image_002034.jpg: 448x640 1 bed, 148.0ms\n", "image 2036/2210 /content/smartvision_dataset/detection/images/image_002035.jpg: 448x640 (no detections), 156.1ms\n", "image 2037/2210 /content/smartvision_dataset/detection/images/image_002036.jpg: 480x640 1 bed, 243.1ms\n", "image 2038/2210 /content/smartvision_dataset/detection/images/image_002037.jpg: 448x640 2 persons, 221.1ms\n", "image 2039/2210 /content/smartvision_dataset/detection/images/image_002038.jpg: 448x640 (no detections), 214.1ms\n", "image 2040/2210 /content/smartvision_dataset/detection/images/image_002039.jpg: 480x640 5 persons, 232.7ms\n", "image 2041/2210 /content/smartvision_dataset/detection/images/image_002040.jpg: 448x640 (no detections), 218.0ms\n", "image 2042/2210 /content/smartvision_dataset/detection/images/image_002041.jpg: 640x512 (no detections), 246.7ms\n", "image 2043/2210 /content/smartvision_dataset/detection/images/image_002042.jpg: 512x640 (no detections), 249.4ms\n", "image 2044/2210 /content/smartvision_dataset/detection/images/image_002043.jpg: 576x640 (no detections), 275.7ms\n", "image 2045/2210 /content/smartvision_dataset/detection/images/image_002044.jpg: 640x480 6 persons, 240.1ms\n", "image 2046/2210 /content/smartvision_dataset/detection/images/image_002045.jpg: 640x480 6 persons, 234.2ms\n", "image 2047/2210 /content/smartvision_dataset/detection/images/image_002046.jpg: 480x640 1 train, 234.7ms\n", "image 2048/2210 /content/smartvision_dataset/detection/images/image_002047.jpg: 480x640 1 train, 233.1ms\n", "image 2049/2210 /content/smartvision_dataset/detection/images/image_002048.jpg: 480x640 1 train, 250.5ms\n", "image 2050/2210 /content/smartvision_dataset/detection/images/image_002049.jpg: 640x480 1 person, 248.2ms\n", "image 2051/2210 /content/smartvision_dataset/detection/images/image_002050.jpg: 640x480 1 person, 245.1ms\n", "image 2052/2210 /content/smartvision_dataset/detection/images/image_002051.jpg: 640x480 1 person, 238.3ms\n", "image 2053/2210 /content/smartvision_dataset/detection/images/image_002052.jpg: 448x640 (no detections), 216.7ms\n", "image 2054/2210 /content/smartvision_dataset/detection/images/image_002053.jpg: 416x640 1 cat, 131.3ms\n", "image 2055/2210 /content/smartvision_dataset/detection/images/image_002054.jpg: 480x640 1 bed, 151.9ms\n", "image 2056/2210 /content/smartvision_dataset/detection/images/image_002055.jpg: 480x640 1 bed, 155.6ms\n", "image 2057/2210 /content/smartvision_dataset/detection/images/image_002056.jpg: 480x640 1 bed, 149.5ms\n", "image 2058/2210 /content/smartvision_dataset/detection/images/image_002057.jpg: 480x640 1 bed, 147.1ms\n", "image 2059/2210 /content/smartvision_dataset/detection/images/image_002058.jpg: 480x640 1 bed, 145.7ms\n", "image 2060/2210 /content/smartvision_dataset/detection/images/image_002059.jpg: 448x640 6 persons, 1 potted plant, 149.6ms\n", "image 2061/2210 /content/smartvision_dataset/detection/images/image_002060.jpg: 448x640 6 persons, 1 potted plant, 139.2ms\n", "image 2062/2210 /content/smartvision_dataset/detection/images/image_002061.jpg: 480x640 1 potted plant, 149.6ms\n", "image 2063/2210 /content/smartvision_dataset/detection/images/image_002062.jpg: 480x640 1 potted plant, 151.8ms\n", "image 2064/2210 /content/smartvision_dataset/detection/images/image_002063.jpg: 480x640 1 potted plant, 148.3ms\n", "image 2065/2210 /content/smartvision_dataset/detection/images/image_002064.jpg: 480x640 3 persons, 149.9ms\n", "image 2066/2210 /content/smartvision_dataset/detection/images/image_002065.jpg: 480x640 3 persons, 164.2ms\n", "image 2067/2210 /content/smartvision_dataset/detection/images/image_002066.jpg: 448x640 (no detections), 142.4ms\n", "image 2068/2210 /content/smartvision_dataset/detection/images/image_002067.jpg: 640x640 2 persons, 200.6ms\n", "image 2069/2210 /content/smartvision_dataset/detection/images/image_002068.jpg: 640x448 1 potted plant, 142.9ms\n", "image 2070/2210 /content/smartvision_dataset/detection/images/image_002069.jpg: 640x448 1 potted plant, 141.9ms\n", "image 2071/2210 /content/smartvision_dataset/detection/images/image_002070.jpg: 480x640 3 persons, 148.6ms\n", "image 2072/2210 /content/smartvision_dataset/detection/images/image_002071.jpg: 480x640 3 persons, 163.0ms\n", "image 2073/2210 /content/smartvision_dataset/detection/images/image_002072.jpg: 480x640 3 persons, 145.4ms\n", "image 2074/2210 /content/smartvision_dataset/detection/images/image_002073.jpg: 448x640 2 persons, 140.7ms\n", "image 2075/2210 /content/smartvision_dataset/detection/images/image_002074.jpg: 480x640 (no detections), 148.1ms\n", "image 2076/2210 /content/smartvision_dataset/detection/images/image_002075.jpg: 480x640 (no detections), 149.3ms\n", "image 2077/2210 /content/smartvision_dataset/detection/images/image_002076.jpg: 480x640 (no detections), 146.9ms\n", "image 2078/2210 /content/smartvision_dataset/detection/images/image_002077.jpg: 384x640 2 persons, 127.5ms\n", "image 2079/2210 /content/smartvision_dataset/detection/images/image_002078.jpg: 384x640 2 persons, 140.9ms\n", "image 2080/2210 /content/smartvision_dataset/detection/images/image_002079.jpg: 384x640 2 persons, 123.9ms\n", "image 2081/2210 /content/smartvision_dataset/detection/images/image_002080.jpg: 384x640 2 persons, 128.8ms\n", "image 2082/2210 /content/smartvision_dataset/detection/images/image_002081.jpg: 384x640 2 persons, 123.5ms\n", "image 2083/2210 /content/smartvision_dataset/detection/images/image_002082.jpg: 480x640 1 person, 149.0ms\n", "image 2084/2210 /content/smartvision_dataset/detection/images/image_002083.jpg: 640x448 1 potted plant, 144.6ms\n", "image 2085/2210 /content/smartvision_dataset/detection/images/image_002084.jpg: 480x640 (no detections), 153.2ms\n", "image 2086/2210 /content/smartvision_dataset/detection/images/image_002085.jpg: 480x640 (no detections), 164.1ms\n", "image 2087/2210 /content/smartvision_dataset/detection/images/image_002086.jpg: 448x640 1 person, 142.0ms\n", "image 2088/2210 /content/smartvision_dataset/detection/images/image_002087.jpg: 448x640 1 potted plant, 146.5ms\n", "image 2089/2210 /content/smartvision_dataset/detection/images/image_002088.jpg: 480x640 (no detections), 153.2ms\n", "image 2090/2210 /content/smartvision_dataset/detection/images/image_002089.jpg: 480x640 (no detections), 150.9ms\n", "image 2091/2210 /content/smartvision_dataset/detection/images/image_002090.jpg: 448x640 1 cat, 140.0ms\n", "image 2092/2210 /content/smartvision_dataset/detection/images/image_002091.jpg: 480x640 1 car, 2 potted plants, 161.3ms\n", "image 2093/2210 /content/smartvision_dataset/detection/images/image_002092.jpg: 480x640 1 car, 2 potted plants, 149.7ms\n", "image 2094/2210 /content/smartvision_dataset/detection/images/image_002093.jpg: 640x480 2 persons, 147.6ms\n", "image 2095/2210 /content/smartvision_dataset/detection/images/image_002094.jpg: 640x480 2 persons, 152.0ms\n", "image 2096/2210 /content/smartvision_dataset/detection/images/image_002095.jpg: 640x480 2 persons, 150.8ms\n", "image 2097/2210 /content/smartvision_dataset/detection/images/image_002096.jpg: 640x480 2 persons, 151.5ms\n", "image 2098/2210 /content/smartvision_dataset/detection/images/image_002097.jpg: 640x480 2 persons, 162.7ms\n", "image 2099/2210 /content/smartvision_dataset/detection/images/image_002098.jpg: 480x640 (no detections), 149.6ms\n", "image 2100/2210 /content/smartvision_dataset/detection/images/image_002099.jpg: 640x640 1 potted plant, 195.6ms\n", "image 2101/2210 /content/smartvision_dataset/detection/images/image_002100.jpg: 640x640 1 potted plant, 195.5ms\n", "image 2102/2210 /content/smartvision_dataset/detection/images/image_002101.jpg: 512x640 3 persons, 158.1ms\n", "image 2103/2210 /content/smartvision_dataset/detection/images/image_002102.jpg: 480x640 4 persons, 144.9ms\n", "image 2104/2210 /content/smartvision_dataset/detection/images/image_002103.jpg: 352x640 (no detections), 126.1ms\n", "image 2105/2210 /content/smartvision_dataset/detection/images/image_002104.jpg: 480x640 1 couch, 155.3ms\n", "image 2106/2210 /content/smartvision_dataset/detection/images/image_002105.jpg: 640x480 2 potted plants, 148.1ms\n", "image 2107/2210 /content/smartvision_dataset/detection/images/image_002106.jpg: 640x480 2 potted plants, 152.7ms\n", "image 2108/2210 /content/smartvision_dataset/detection/images/image_002107.jpg: 448x640 (no detections), 141.6ms\n", "image 2109/2210 /content/smartvision_dataset/detection/images/image_002108.jpg: 448x640 (no detections), 142.1ms\n", "image 2110/2210 /content/smartvision_dataset/detection/images/image_002109.jpg: 480x640 5 persons, 161.6ms\n", "image 2111/2210 /content/smartvision_dataset/detection/images/image_002110.jpg: 480x640 1 bicycle, 149.0ms\n", "image 2112/2210 /content/smartvision_dataset/detection/images/image_002111.jpg: 640x480 (no detections), 147.8ms\n", "image 2113/2210 /content/smartvision_dataset/detection/images/image_002112.jpg: 640x640 8 persons, 198.7ms\n", "image 2114/2210 /content/smartvision_dataset/detection/images/image_002113.jpg: 640x640 8 persons, 269.6ms\n", "image 2115/2210 /content/smartvision_dataset/detection/images/image_002114.jpg: 640x480 (no detections), 239.3ms\n", "image 2116/2210 /content/smartvision_dataset/detection/images/image_002115.jpg: 480x640 1 motorcycle, 229.6ms\n", "image 2117/2210 /content/smartvision_dataset/detection/images/image_002116.jpg: 480x640 1 motorcycle, 225.4ms\n", "image 2118/2210 /content/smartvision_dataset/detection/images/image_002117.jpg: 480x640 1 motorcycle, 228.0ms\n", "image 2119/2210 /content/smartvision_dataset/detection/images/image_002118.jpg: 480x640 1 motorcycle, 219.5ms\n", "image 2120/2210 /content/smartvision_dataset/detection/images/image_002119.jpg: 480x640 1 motorcycle, 240.8ms\n", "image 2121/2210 /content/smartvision_dataset/detection/images/image_002120.jpg: 480x640 1 motorcycle, 233.7ms\n", "image 2122/2210 /content/smartvision_dataset/detection/images/image_002121.jpg: 448x640 29 persons, 1 bench, 25 potted plants, 220.9ms\n", "image 2123/2210 /content/smartvision_dataset/detection/images/image_002122.jpg: 448x640 29 persons, 1 bench, 25 potted plants, 218.2ms\n", "image 2124/2210 /content/smartvision_dataset/detection/images/image_002123.jpg: 448x640 29 persons, 1 bench, 25 potted plants, 240.3ms\n", "image 2125/2210 /content/smartvision_dataset/detection/images/image_002124.jpg: 448x640 29 persons, 1 bench, 25 potted plants, 216.8ms\n", "image 2126/2210 /content/smartvision_dataset/detection/images/image_002125.jpg: 480x640 1 bed, 231.9ms\n", "image 2127/2210 /content/smartvision_dataset/detection/images/image_002126.jpg: 640x384 (no detections), 195.3ms\n", "image 2128/2210 /content/smartvision_dataset/detection/images/image_002127.jpg: 480x640 (no detections), 244.5ms\n", "image 2129/2210 /content/smartvision_dataset/detection/images/image_002128.jpg: 480x640 1 cat, 237.9ms\n", "image 2130/2210 /content/smartvision_dataset/detection/images/image_002129.jpg: 480x640 2 cats, 1 bed, 231.8ms\n", "image 2131/2210 /content/smartvision_dataset/detection/images/image_002130.jpg: 480x640 (no detections), 148.3ms\n", "image 2132/2210 /content/smartvision_dataset/detection/images/image_002131.jpg: 448x640 1 bed, 141.9ms\n", "image 2133/2210 /content/smartvision_dataset/detection/images/image_002132.jpg: 640x576 1 bed, 178.7ms\n", "image 2134/2210 /content/smartvision_dataset/detection/images/image_002133.jpg: 448x640 1 bicycle, 1 bed, 166.5ms\n", "image 2135/2210 /content/smartvision_dataset/detection/images/image_002134.jpg: 448x640 1 bed, 139.4ms\n", "image 2136/2210 /content/smartvision_dataset/detection/images/image_002135.jpg: 640x448 2 persons, 144.1ms\n", "image 2137/2210 /content/smartvision_dataset/detection/images/image_002136.jpg: 448x640 1 bed, 141.1ms\n", "image 2138/2210 /content/smartvision_dataset/detection/images/image_002137.jpg: 448x640 1 bed, 140.6ms\n", "image 2139/2210 /content/smartvision_dataset/detection/images/image_002138.jpg: 480x640 1 couch, 1 bed, 153.6ms\n", "image 2140/2210 /content/smartvision_dataset/detection/images/image_002139.jpg: 480x640 (no detections), 164.9ms\n", "image 2141/2210 /content/smartvision_dataset/detection/images/image_002140.jpg: 480x640 (no detections), 151.7ms\n", "image 2142/2210 /content/smartvision_dataset/detection/images/image_002141.jpg: 640x480 3 persons, 153.9ms\n", "image 2143/2210 /content/smartvision_dataset/detection/images/image_002142.jpg: 480x640 (no detections), 148.0ms\n", "image 2144/2210 /content/smartvision_dataset/detection/images/image_002143.jpg: 480x640 (no detections), 146.7ms\n", "image 2145/2210 /content/smartvision_dataset/detection/images/image_002144.jpg: 320x640 (no detections), 111.1ms\n", "image 2146/2210 /content/smartvision_dataset/detection/images/image_002145.jpg: 448x640 2 persons, 2 beds, 142.9ms\n", "image 2147/2210 /content/smartvision_dataset/detection/images/image_002146.jpg: 480x640 1 bed, 160.8ms\n", "image 2148/2210 /content/smartvision_dataset/detection/images/image_002147.jpg: 640x448 2 persons, 1 couch, 1 bed, 145.9ms\n", "image 2149/2210 /content/smartvision_dataset/detection/images/image_002148.jpg: 480x640 1 bed, 148.9ms\n", "image 2150/2210 /content/smartvision_dataset/detection/images/image_002149.jpg: 480x640 1 cat, 1 couch, 1 bed, 145.2ms\n", "image 2151/2210 /content/smartvision_dataset/detection/images/image_002150.jpg: 512x640 3 persons, 162.2ms\n", "image 2152/2210 /content/smartvision_dataset/detection/images/image_002151.jpg: 448x640 1 bed, 136.9ms\n", "image 2153/2210 /content/smartvision_dataset/detection/images/image_002152.jpg: 480x640 1 person, 1 bed, 160.4ms\n", "image 2154/2210 /content/smartvision_dataset/detection/images/image_002153.jpg: 512x640 2 cats, 161.3ms\n", "image 2155/2210 /content/smartvision_dataset/detection/images/image_002154.jpg: 480x640 1 bed, 150.4ms\n", "image 2156/2210 /content/smartvision_dataset/detection/images/image_002155.jpg: 640x448 1 person, 1 bed, 142.3ms\n", "image 2157/2210 /content/smartvision_dataset/detection/images/image_002156.jpg: 640x480 (no detections), 154.4ms\n", "image 2158/2210 /content/smartvision_dataset/detection/images/image_002157.jpg: 480x640 (no detections), 146.4ms\n", "image 2159/2210 /content/smartvision_dataset/detection/images/image_002158.jpg: 480x640 (no detections), 159.4ms\n", "image 2160/2210 /content/smartvision_dataset/detection/images/image_002159.jpg: 480x640 2 persons, 148.7ms\n", "image 2161/2210 /content/smartvision_dataset/detection/images/image_002160.jpg: 480x640 3 beds, 167.8ms\n", "image 2162/2210 /content/smartvision_dataset/detection/images/image_002161.jpg: 480x640 3 beds, 146.4ms\n", "image 2163/2210 /content/smartvision_dataset/detection/images/image_002162.jpg: 480x640 (no detections), 150.0ms\n", "image 2164/2210 /content/smartvision_dataset/detection/images/image_002163.jpg: 480x640 2 beds, 149.3ms\n", "image 2165/2210 /content/smartvision_dataset/detection/images/image_002164.jpg: 480x640 1 bed, 155.2ms\n", "image 2166/2210 /content/smartvision_dataset/detection/images/image_002165.jpg: 448x640 (no detections), 145.8ms\n", "image 2167/2210 /content/smartvision_dataset/detection/images/image_002166.jpg: 640x480 1 bed, 148.1ms\n", "image 2168/2210 /content/smartvision_dataset/detection/images/image_002167.jpg: 448x640 2 persons, 1 bed, 140.2ms\n", "image 2169/2210 /content/smartvision_dataset/detection/images/image_002168.jpg: 480x640 12 persons, 1 car, 147.7ms\n", "image 2170/2210 /content/smartvision_dataset/detection/images/image_002169.jpg: 640x448 1 person, 1 bed, 143.6ms\n", "image 2171/2210 /content/smartvision_dataset/detection/images/image_002170.jpg: 448x640 (no detections), 139.5ms\n", "image 2172/2210 /content/smartvision_dataset/detection/images/image_002171.jpg: 480x640 1 bed, 159.8ms\n", "image 2173/2210 /content/smartvision_dataset/detection/images/image_002172.jpg: 480x640 1 person, 154.2ms\n", "image 2174/2210 /content/smartvision_dataset/detection/images/image_002173.jpg: 448x640 3 persons, 2 beds, 137.2ms\n", "image 2175/2210 /content/smartvision_dataset/detection/images/image_002174.jpg: 480x640 1 cat, 1 bed, 148.8ms\n", "image 2176/2210 /content/smartvision_dataset/detection/images/image_002175.jpg: 640x448 (no detections), 142.6ms\n", "image 2177/2210 /content/smartvision_dataset/detection/images/image_002176.jpg: 640x480 1 person, 151.7ms\n", "image 2178/2210 /content/smartvision_dataset/detection/images/image_002177.jpg: 448x640 1 bed, 143.7ms\n", "image 2179/2210 /content/smartvision_dataset/detection/images/image_002178.jpg: 480x640 3 beds, 153.5ms\n", "image 2180/2210 /content/smartvision_dataset/detection/images/image_002179.jpg: 480x640 3 beds, 149.2ms\n", "image 2181/2210 /content/smartvision_dataset/detection/images/image_002180.jpg: 448x640 1 bed, 141.6ms\n", "image 2182/2210 /content/smartvision_dataset/detection/images/image_002181.jpg: 480x640 1 bed, 149.2ms\n", "image 2183/2210 /content/smartvision_dataset/detection/images/image_002182.jpg: 448x640 (no detections), 140.5ms\n", "image 2184/2210 /content/smartvision_dataset/detection/images/image_002183.jpg: 480x640 1 bed, 149.0ms\n", "image 2185/2210 /content/smartvision_dataset/detection/images/image_002184.jpg: 480x640 1 bed, 161.0ms\n", "image 2186/2210 /content/smartvision_dataset/detection/images/image_002185.jpg: 480x640 (no detections), 148.5ms\n", "image 2187/2210 /content/smartvision_dataset/detection/images/image_002186.jpg: 480x640 1 bed, 146.8ms\n", "image 2188/2210 /content/smartvision_dataset/detection/images/image_002187.jpg: 480x640 1 bed, 151.1ms\n", "image 2189/2210 /content/smartvision_dataset/detection/images/image_002188.jpg: 448x640 1 bed, 139.8ms\n", "image 2190/2210 /content/smartvision_dataset/detection/images/image_002189.jpg: 448x640 1 bed, 138.5ms\n", "image 2191/2210 /content/smartvision_dataset/detection/images/image_002190.jpg: 448x640 1 bed, 136.0ms\n", "image 2192/2210 /content/smartvision_dataset/detection/images/image_002191.jpg: 640x640 1 bed, 270.7ms\n", "image 2193/2210 /content/smartvision_dataset/detection/images/image_002192.jpg: 416x640 1 bed, 197.8ms\n", "image 2194/2210 /content/smartvision_dataset/detection/images/image_002193.jpg: 640x480 1 person, 235.6ms\n", "image 2195/2210 /content/smartvision_dataset/detection/images/image_002194.jpg: 480x640 (no detections), 224.7ms\n", "image 2196/2210 /content/smartvision_dataset/detection/images/image_002195.jpg: 448x640 1 person, 1 bed, 225.1ms\n", "image 2197/2210 /content/smartvision_dataset/detection/images/image_002196.jpg: 448x640 (no detections), 206.7ms\n", "image 2198/2210 /content/smartvision_dataset/detection/images/image_002197.jpg: 480x640 1 bed, 226.7ms\n", "image 2199/2210 /content/smartvision_dataset/detection/images/image_002198.jpg: 480x640 1 cat, 1 bed, 227.7ms\n", "image 2200/2210 /content/smartvision_dataset/detection/images/image_002199.jpg: 480x640 2 beds, 237.4ms\n", "image 2201/2210 /content/smartvision_dataset/detection/images/image_002200.jpg: 480x640 2 beds, 236.8ms\n", "image 2202/2210 /content/smartvision_dataset/detection/images/image_002201.jpg: 640x480 2 beds, 229.5ms\n", "image 2203/2210 /content/smartvision_dataset/detection/images/image_002202.jpg: 480x640 1 cat, 1 dog, 1 bed, 224.0ms\n", "image 2204/2210 /content/smartvision_dataset/detection/images/image_002203.jpg: 448x640 (no detections), 212.2ms\n", "image 2205/2210 /content/smartvision_dataset/detection/images/image_002204.jpg: 480x640 (no detections), 241.5ms\n", "image 2206/2210 /content/smartvision_dataset/detection/images/image_002205.jpg: 480x640 1 person, 1 couch, 241.3ms\n", "image 2207/2210 /content/smartvision_dataset/detection/images/image_002206.jpg: 640x480 2 beds, 239.5ms\n", "image 2208/2210 /content/smartvision_dataset/detection/images/image_002207.jpg: 640x480 2 beds, 242.9ms\n", "image 2209/2210 /content/smartvision_dataset/detection/images/image_002208.jpg: 480x640 1 cat, 168.2ms\n", "image 2210/2210 /content/smartvision_dataset/detection/images/image_002209.jpg: 640x480 1 cat, 159.0ms\n", "Speed: 2.9ms preprocess, 171.0ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)\n", "Results saved to \u001b[1m/content/runs/detect/predict\u001b[0m\n" ] } ], "source": [ "# Predict on one image\n", "results = model.predict(\n", " source=\"smartvision_dataset/detection/images\",\n", " save=True,\n", " conf=0.25\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "id": "-Dntd13dBrGW", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-Dntd13dBrGW", "outputId": "1854e087-0c17-4ca2-9978-33426341ae3c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", "updating: smartvision_dataset/classification/train/airplane/airplane_train_0067.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/airplane/airplane_train_0023.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/airplane/airplane_train_0052.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/airplane/airplane_train_0009.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/airplane/airplane_train_0036.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/airplane/airplane_train_0043.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/airplane/airplane_train_0045.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/airplane/airplane_train_0053.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/airplane/airplane_train_0029.jpg (deflated 8%)\n", "updating: smartvision_dataset/classification/train/airplane/airplane_train_0047.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/airplane/airplane_train_0031.jpg (deflated 8%)\n", "updating: smartvision_dataset/classification/train/airplane/airplane_train_0065.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/airplane/airplane_train_0058.jpg (deflated 5%)\n", "updating: smartvision_dataset/classification/train/airplane/airplane_train_0057.jpg (deflated 5%)\n", "updating: smartvision_dataset/classification/train/airplane/airplane_train_0013.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/airplane/airplane_train_0030.jpg (deflated 8%)\n", "updating: smartvision_dataset/classification/train/horse/ (stored 0%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0048.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0066.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0023.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0036.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0017.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0034.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0007.jpg (deflated 23%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0021.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0056.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0025.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0011.jpg (deflated 16%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0032.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0015.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0050.jpg (deflated 7%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0008.jpg (deflated 23%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0013.jpg (deflated 16%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0000.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0006.jpg (deflated 23%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0012.jpg (deflated 16%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0053.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0055.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0003.jpg (deflated 5%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0019.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0039.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0069.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0051.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0067.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0063.jpg (deflated 8%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0043.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0026.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0068.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0052.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0030.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0010.jpg (deflated 16%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0002.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0022.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0038.jpg (deflated 7%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0041.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0059.jpg (deflated 8%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0005.jpg (deflated 23%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0062.jpg (deflated 8%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0047.jpg (deflated 18%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0058.jpg (deflated 8%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0031.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0004.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0064.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0035.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0054.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0029.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0020.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0057.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0044.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0024.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0065.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0001.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0046.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0060.jpg (deflated 8%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0016.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0009.jpg (deflated 16%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0042.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0040.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0028.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0027.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0045.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0014.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0049.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0018.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0033.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0037.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/train/horse/horse_train_0061.jpg (deflated 8%)\n", "updating: smartvision_dataset/classification/train/motorcycle/ (stored 0%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0025.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0016.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0023.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0006.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0064.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0008.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0046.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0054.jpg (deflated 5%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0028.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0001.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0011.jpg (deflated 17%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0068.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0015.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0014.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0036.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0044.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0024.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0012.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0027.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0065.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0047.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0009.jpg (deflated 13%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0052.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0022.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0000.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0049.jpg (deflated 42%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0003.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0029.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0031.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0033.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0067.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0042.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0019.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0032.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0038.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0050.jpg (deflated 42%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0062.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0058.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0063.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0051.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0045.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0043.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0069.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0020.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0034.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0056.jpg (deflated 5%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0048.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0059.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0010.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0030.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0026.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0005.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0017.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0018.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0061.jpg (deflated 23%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0060.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0035.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0066.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0037.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0002.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0007.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0021.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0055.jpg (deflated 5%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0053.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0004.jpg (deflated 7%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0057.jpg (deflated 5%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0041.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0040.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0039.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/train/motorcycle/motorcycle_train_0013.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/cake/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/cake/cake_test_0004.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cake/cake_test_0008.jpg (deflated 5%)\n", "updating: smartvision_dataset/classification/test/cake/cake_test_0010.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cake/cake_test_0002.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cake/cake_test_0014.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/cake/cake_test_0012.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/cake/cake_test_0000.jpg (deflated 7%)\n", "updating: smartvision_dataset/classification/test/cake/cake_test_0011.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/cake/cake_test_0003.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cake/cake_test_0005.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/cake/cake_test_0001.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cake/cake_test_0009.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/cake/cake_test_0006.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/cake/cake_test_0007.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cake/cake_test_0013.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/bus/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/bus/bus_test_0008.jpg (deflated 12%)\n", "updating: smartvision_dataset/classification/test/bus/bus_test_0004.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bus/bus_test_0001.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/bus/bus_test_0003.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bus/bus_test_0007.jpg (deflated 8%)\n", "updating: smartvision_dataset/classification/test/bus/bus_test_0014.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bus/bus_test_0005.jpg (deflated 12%)\n", "updating: smartvision_dataset/classification/test/bus/bus_test_0006.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/bus/bus_test_0013.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bus/bus_test_0011.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bus/bus_test_0009.jpg (deflated 5%)\n", "updating: smartvision_dataset/classification/test/bus/bus_test_0002.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bus/bus_test_0000.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/bus/bus_test_0012.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bus/bus_test_0010.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/bicycle/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/bicycle/bicycle_test_0012.jpg (deflated 21%)\n", "updating: smartvision_dataset/classification/test/bicycle/bicycle_test_0004.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/bicycle/bicycle_test_0005.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/bicycle/bicycle_test_0008.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/bicycle/bicycle_test_0006.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/bicycle/bicycle_test_0003.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/bicycle/bicycle_test_0001.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/bicycle/bicycle_test_0010.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/bicycle/bicycle_test_0014.jpg (deflated 5%)\n", "updating: smartvision_dataset/classification/test/bicycle/bicycle_test_0002.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/bicycle/bicycle_test_0011.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/bicycle/bicycle_test_0000.jpg (deflated 8%)\n", "updating: smartvision_dataset/classification/test/bicycle/bicycle_test_0009.jpg (deflated 10%)\n", "updating: smartvision_dataset/classification/test/bicycle/bicycle_test_0013.jpg (deflated 21%)\n", "updating: smartvision_dataset/classification/test/bicycle/bicycle_test_0007.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/cat/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/cat/cat_test_0014.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cat/cat_test_0002.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cat/cat_test_0007.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/cat/cat_test_0003.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cat/cat_test_0006.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cat/cat_test_0008.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cat/cat_test_0005.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cat/cat_test_0012.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/cat/cat_test_0009.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cat/cat_test_0013.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/cat/cat_test_0004.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cat/cat_test_0011.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cat/cat_test_0001.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cat/cat_test_0000.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/cat/cat_test_0010.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/stop sign/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/stop sign/stop sign_test_0013.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/stop sign/stop sign_test_0004.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/stop sign/stop sign_test_0006.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/stop sign/stop sign_test_0009.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/stop sign/stop sign_test_0007.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/stop sign/stop sign_test_0011.jpg (deflated 16%)\n", "updating: smartvision_dataset/classification/test/stop sign/stop sign_test_0008.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/stop sign/stop sign_test_0002.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/stop sign/stop sign_test_0000.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/stop sign/stop sign_test_0001.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/stop sign/stop sign_test_0010.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/stop sign/stop sign_test_0014.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/stop sign/stop sign_test_0012.jpg (deflated 11%)\n", "updating: smartvision_dataset/classification/test/stop sign/stop sign_test_0003.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/stop sign/stop sign_test_0005.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/truck/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/truck/truck_test_0013.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/truck/truck_test_0014.jpg (deflated 8%)\n", "updating: smartvision_dataset/classification/test/truck/truck_test_0006.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/truck/truck_test_0010.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/truck/truck_test_0007.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/truck/truck_test_0008.jpg (deflated 38%)\n", "updating: smartvision_dataset/classification/test/truck/truck_test_0005.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/truck/truck_test_0001.jpg (deflated 9%)\n", "updating: smartvision_dataset/classification/test/truck/truck_test_0004.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/truck/truck_test_0000.jpg (deflated 9%)\n", "updating: smartvision_dataset/classification/test/truck/truck_test_0003.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/truck/truck_test_0002.jpg (deflated 9%)\n", "updating: smartvision_dataset/classification/test/truck/truck_test_0011.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/truck/truck_test_0012.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/truck/truck_test_0009.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/bowl/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/bowl/bowl_test_0004.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/bowl/bowl_test_0012.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/bowl/bowl_test_0010.jpg (deflated 5%)\n", "updating: smartvision_dataset/classification/test/bowl/bowl_test_0011.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/bowl/bowl_test_0007.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bowl/bowl_test_0002.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/bowl/bowl_test_0008.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bowl/bowl_test_0014.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/bowl/bowl_test_0000.jpg (deflated 5%)\n", "updating: smartvision_dataset/classification/test/bowl/bowl_test_0001.jpg (deflated 12%)\n", "updating: smartvision_dataset/classification/test/bowl/bowl_test_0003.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/bowl/bowl_test_0005.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/bowl/bowl_test_0006.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bowl/bowl_test_0013.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/bowl/bowl_test_0009.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/dog/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/dog/dog_test_0007.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/dog/dog_test_0005.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/dog/dog_test_0003.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/dog/dog_test_0012.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/dog/dog_test_0008.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/dog/dog_test_0000.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/dog/dog_test_0001.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/dog/dog_test_0009.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/dog/dog_test_0014.jpg (deflated 7%)\n", "updating: smartvision_dataset/classification/test/dog/dog_test_0011.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/dog/dog_test_0002.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/dog/dog_test_0013.jpg (deflated 7%)\n", "updating: smartvision_dataset/classification/test/dog/dog_test_0010.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/dog/dog_test_0004.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/dog/dog_test_0006.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/chair/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/chair/chair_test_0000.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/chair/chair_test_0013.jpg (deflated 9%)\n", "updating: smartvision_dataset/classification/test/chair/chair_test_0014.jpg (deflated 20%)\n", "updating: smartvision_dataset/classification/test/chair/chair_test_0011.jpg (deflated 12%)\n", "updating: smartvision_dataset/classification/test/chair/chair_test_0006.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/chair/chair_test_0007.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/chair/chair_test_0008.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/chair/chair_test_0005.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/chair/chair_test_0001.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/chair/chair_test_0004.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/chair/chair_test_0009.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/chair/chair_test_0002.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/chair/chair_test_0012.jpg (deflated 12%)\n", "updating: smartvision_dataset/classification/test/chair/chair_test_0003.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/chair/chair_test_0010.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/bed/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/bed/bed_test_0012.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/bed/bed_test_0010.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/bed/bed_test_0006.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/bed/bed_test_0009.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/bed/bed_test_0014.jpg (deflated 8%)\n", "updating: smartvision_dataset/classification/test/bed/bed_test_0007.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/bed/bed_test_0005.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bed/bed_test_0004.jpg (deflated 14%)\n", "updating: smartvision_dataset/classification/test/bed/bed_test_0013.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bed/bed_test_0011.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bed/bed_test_0008.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bed/bed_test_0000.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bed/bed_test_0002.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/bed/bed_test_0001.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bed/bed_test_0003.jpg (deflated 13%)\n", "updating: smartvision_dataset/classification/test/couch/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/couch/couch_test_0006.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/couch/couch_test_0014.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/couch/couch_test_0002.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/couch/couch_test_0008.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/couch/couch_test_0000.jpg (deflated 7%)\n", "updating: smartvision_dataset/classification/test/couch/couch_test_0003.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/couch/couch_test_0011.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/couch/couch_test_0009.jpg (deflated 9%)\n", "updating: smartvision_dataset/classification/test/couch/couch_test_0007.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/couch/couch_test_0001.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/couch/couch_test_0005.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/couch/couch_test_0010.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/couch/couch_test_0004.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/couch/couch_test_0012.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/couch/couch_test_0013.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/person/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/person/person_test_0002.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/person/person_test_0013.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/person/person_test_0009.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/person/person_test_0012.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/person/person_test_0006.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/person/person_test_0001.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/person/person_test_0004.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/person/person_test_0010.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/person/person_test_0007.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/person/person_test_0003.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/person/person_test_0011.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/person/person_test_0000.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/person/person_test_0014.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/person/person_test_0005.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/person/person_test_0008.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/train/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/train/train_test_0012.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/train/train_test_0010.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/train/train_test_0007.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/train/train_test_0013.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/train/train_test_0009.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/train/train_test_0003.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/train/train_test_0005.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/train/train_test_0014.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/train/train_test_0001.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/train/train_test_0000.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/train/train_test_0011.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/train/train_test_0004.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/train/train_test_0006.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/train/train_test_0002.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/train/train_test_0008.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/potted plant/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/potted plant/potted plant_test_0010.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/potted plant/potted plant_test_0008.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/potted plant/potted plant_test_0011.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/potted plant/potted plant_test_0003.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/potted plant/potted plant_test_0014.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/potted plant/potted plant_test_0002.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/potted plant/potted plant_test_0004.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/potted plant/potted plant_test_0000.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/potted plant/potted plant_test_0005.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/potted plant/potted plant_test_0013.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/potted plant/potted plant_test_0012.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/potted plant/potted plant_test_0009.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/potted plant/potted plant_test_0001.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/potted plant/potted plant_test_0007.jpg (deflated 5%)\n", "updating: smartvision_dataset/classification/test/potted plant/potted plant_test_0006.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bottle/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/bottle/bottle_test_0008.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/bottle/bottle_test_0011.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/bottle/bottle_test_0002.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bottle/bottle_test_0004.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bottle/bottle_test_0000.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bottle/bottle_test_0014.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/bottle/bottle_test_0005.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bottle/bottle_test_0010.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/bottle/bottle_test_0007.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bottle/bottle_test_0001.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bottle/bottle_test_0012.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/bottle/bottle_test_0009.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/bottle/bottle_test_0003.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bottle/bottle_test_0013.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/bottle/bottle_test_0006.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cow/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/cow/cow_test_0013.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cow/cow_test_0009.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/cow/cow_test_0004.jpg (deflated 5%)\n", "updating: smartvision_dataset/classification/test/cow/cow_test_0007.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/cow/cow_test_0000.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/cow/cow_test_0010.jpg (deflated 0%)\n", "updating: smartvision_dataset/classification/test/cow/cow_test_0011.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cow/cow_test_0008.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/cow/cow_test_0005.jpg (deflated 5%)\n", "updating: smartvision_dataset/classification/test/cow/cow_test_0003.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/cow/cow_test_0002.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/cow/cow_test_0006.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/cow/cow_test_0014.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cow/cow_test_0012.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cow/cow_test_0001.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/pizza/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/pizza/pizza_test_0002.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/pizza/pizza_test_0011.jpg (deflated 18%)\n", "updating: smartvision_dataset/classification/test/pizza/pizza_test_0010.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/pizza/pizza_test_0014.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/pizza/pizza_test_0007.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/pizza/pizza_test_0001.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/pizza/pizza_test_0008.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/pizza/pizza_test_0012.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/pizza/pizza_test_0003.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/pizza/pizza_test_0013.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/pizza/pizza_test_0005.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/pizza/pizza_test_0009.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/pizza/pizza_test_0004.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/pizza/pizza_test_0006.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/pizza/pizza_test_0000.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cup/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/cup/cup_test_0002.jpg (deflated 49%)\n", "updating: smartvision_dataset/classification/test/cup/cup_test_0014.jpg (deflated 5%)\n", "updating: smartvision_dataset/classification/test/cup/cup_test_0001.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cup/cup_test_0007.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cup/cup_test_0011.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/cup/cup_test_0003.jpg (deflated 49%)\n", "updating: smartvision_dataset/classification/test/cup/cup_test_0009.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cup/cup_test_0012.jpg (deflated 5%)\n", "updating: smartvision_dataset/classification/test/cup/cup_test_0005.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/cup/cup_test_0010.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cup/cup_test_0008.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/cup/cup_test_0004.jpg (deflated 36%)\n", "updating: smartvision_dataset/classification/test/cup/cup_test_0006.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/cup/cup_test_0013.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/cup/cup_test_0000.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/elephant/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/elephant/elephant_test_0007.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/elephant/elephant_test_0008.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/elephant/elephant_test_0004.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/elephant/elephant_test_0010.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/elephant/elephant_test_0002.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/elephant/elephant_test_0013.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/elephant/elephant_test_0003.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/elephant/elephant_test_0014.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/elephant/elephant_test_0005.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/elephant/elephant_test_0000.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/elephant/elephant_test_0001.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/elephant/elephant_test_0011.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/elephant/elephant_test_0006.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/elephant/elephant_test_0012.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/elephant/elephant_test_0009.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bench/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/bench/bench_test_0010.jpg (deflated 15%)\n", "updating: smartvision_dataset/classification/test/bench/bench_test_0005.jpg (deflated 15%)\n", "updating: smartvision_dataset/classification/test/bench/bench_test_0000.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/bench/bench_test_0007.jpg (deflated 7%)\n", "updating: smartvision_dataset/classification/test/bench/bench_test_0001.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bench/bench_test_0003.jpg (deflated 15%)\n", "updating: smartvision_dataset/classification/test/bench/bench_test_0013.jpg (deflated 15%)\n", "updating: smartvision_dataset/classification/test/bench/bench_test_0009.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bench/bench_test_0002.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/bench/bench_test_0011.jpg (deflated 15%)\n", "updating: smartvision_dataset/classification/test/bench/bench_test_0012.jpg (deflated 15%)\n", "updating: smartvision_dataset/classification/test/bench/bench_test_0014.jpg (deflated 4%)\n", "updating: smartvision_dataset/classification/test/bench/bench_test_0008.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bench/bench_test_0004.jpg (deflated 15%)\n", "updating: smartvision_dataset/classification/test/bench/bench_test_0006.jpg (deflated 7%)\n", "updating: smartvision_dataset/classification/test/traffic light/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/traffic light/traffic light_test_0006.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/traffic light/traffic light_test_0013.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/traffic light/traffic light_test_0012.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/traffic light/traffic light_test_0011.jpg (deflated 7%)\n", "updating: smartvision_dataset/classification/test/traffic light/traffic light_test_0008.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/traffic light/traffic light_test_0010.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/traffic light/traffic light_test_0014.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/traffic light/traffic light_test_0002.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/traffic light/traffic light_test_0004.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/traffic light/traffic light_test_0009.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/traffic light/traffic light_test_0000.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/traffic light/traffic light_test_0001.jpg (deflated 9%)\n", "updating: smartvision_dataset/classification/test/traffic light/traffic light_test_0007.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/traffic light/traffic light_test_0005.jpg (deflated 2%)\n", "updating: smartvision_dataset/classification/test/traffic light/traffic light_test_0003.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bird/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/bird/bird_test_0006.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bird/bird_test_0000.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bird/bird_test_0010.jpg (deflated 7%)\n", "updating: smartvision_dataset/classification/test/bird/bird_test_0012.jpg (deflated 7%)\n", "updating: smartvision_dataset/classification/test/bird/bird_test_0007.jpg (deflated 7%)\n", "updating: smartvision_dataset/classification/test/bird/bird_test_0001.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bird/bird_test_0008.jpg (deflated 7%)\n", "updating: smartvision_dataset/classification/test/bird/bird_test_0005.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/bird/bird_test_0003.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/bird/bird_test_0013.jpg (deflated 7%)\n", "updating: smartvision_dataset/classification/test/bird/bird_test_0014.jpg (deflated 7%)\n", "updating: smartvision_dataset/classification/test/bird/bird_test_0002.jpg (deflated 3%)\n", "updating: smartvision_dataset/classification/test/bird/bird_test_0009.jpg (deflated 7%)\n", "updating: smartvision_dataset/classification/test/bird/bird_test_0011.jpg (deflated 7%)\n", "updating: smartvision_dataset/classification/test/bird/bird_test_0004.jpg (deflated 17%)\n", "updating: smartvision_dataset/classification/test/car/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/car/car_test_0014.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/car/car_test_0001.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/car/car_test_0013.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/car/car_test_0006.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/car/car_test_0005.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/car/car_test_0004.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/car/car_test_0000.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/car/car_test_0009.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/car/car_test_0010.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/car/car_test_0012.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/car/car_test_0002.jpg (deflated 8%)\n", "updating: smartvision_dataset/classification/test/car/car_test_0008.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/car/car_test_0011.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/car/car_test_0007.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/car/car_test_0003.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/airplane/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/airplane/airplane_test_0002.jpg (deflated 10%)\n", "updating: smartvision_dataset/classification/test/airplane/airplane_test_0010.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/airplane/airplane_test_0001.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/airplane/airplane_test_0011.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/airplane/airplane_test_0000.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/airplane/airplane_test_0003.jpg (deflated 10%)\n", "updating: smartvision_dataset/classification/test/airplane/airplane_test_0013.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/airplane/airplane_test_0009.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/airplane/airplane_test_0006.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/airplane/airplane_test_0005.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/airplane/airplane_test_0004.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/airplane/airplane_test_0014.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/airplane/airplane_test_0012.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/airplane/airplane_test_0008.jpg (deflated 6%)\n", "updating: smartvision_dataset/classification/test/airplane/airplane_test_0007.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/horse/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/horse/horse_test_0014.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/horse/horse_test_0004.jpg (deflated 13%)\n", "updating: smartvision_dataset/classification/test/horse/horse_test_0001.jpg (deflated 13%)\n", "updating: smartvision_dataset/classification/test/horse/horse_test_0013.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/horse/horse_test_0000.jpg (deflated 13%)\n", "updating: smartvision_dataset/classification/test/horse/horse_test_0008.jpg (deflated 13%)\n", "updating: smartvision_dataset/classification/test/horse/horse_test_0012.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/horse/horse_test_0002.jpg (deflated 13%)\n", "updating: smartvision_dataset/classification/test/horse/horse_test_0010.jpg (deflated 13%)\n", "updating: smartvision_dataset/classification/test/horse/horse_test_0006.jpg (deflated 13%)\n", "updating: smartvision_dataset/classification/test/horse/horse_test_0003.jpg (deflated 13%)\n", "updating: smartvision_dataset/classification/test/horse/horse_test_0005.jpg (deflated 13%)\n", "updating: smartvision_dataset/classification/test/horse/horse_test_0007.jpg (deflated 13%)\n", "updating: smartvision_dataset/classification/test/horse/horse_test_0009.jpg (deflated 13%)\n", "updating: smartvision_dataset/classification/test/horse/horse_test_0011.jpg (deflated 13%)\n", "updating: smartvision_dataset/classification/test/motorcycle/ (stored 0%)\n", "updating: smartvision_dataset/classification/test/motorcycle/motorcycle_test_0006.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/motorcycle/motorcycle_test_0001.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/motorcycle/motorcycle_test_0004.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/motorcycle/motorcycle_test_0008.jpg (deflated 19%)\n", "updating: smartvision_dataset/classification/test/motorcycle/motorcycle_test_0010.jpg (deflated 19%)\n", "updating: smartvision_dataset/classification/test/motorcycle/motorcycle_test_0007.jpg (deflated 19%)\n", "updating: smartvision_dataset/classification/test/motorcycle/motorcycle_test_0002.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/motorcycle/motorcycle_test_0005.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/motorcycle/motorcycle_test_0012.jpg (deflated 19%)\n", "updating: smartvision_dataset/classification/test/motorcycle/motorcycle_test_0009.jpg (deflated 19%)\n", "updating: smartvision_dataset/classification/test/motorcycle/motorcycle_test_0003.jpg (deflated 27%)\n", "updating: smartvision_dataset/classification/test/motorcycle/motorcycle_test_0011.jpg (deflated 19%)\n", "updating: smartvision_dataset/classification/test/motorcycle/motorcycle_test_0000.jpg (deflated 8%)\n", "updating: smartvision_dataset/classification/test/motorcycle/motorcycle_test_0014.jpg (deflated 1%)\n", "updating: smartvision_dataset/classification/test/motorcycle/motorcycle_test_0013.jpg (deflated 19%)\n", "updating: smartvision_dataset/detection/ (stored 0%)\n", "updating: smartvision_dataset/detection/images/ (stored 0%)\n", "updating: smartvision_dataset/detection/images/image_001642.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000711.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001576.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000871.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000748.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002013.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001072.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001846.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000608.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000357.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002015.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002128.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001338.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001980.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002090.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000197.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_002141.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000215.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001725.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000957.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001817.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001105.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001085.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000503.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002209.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000603.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001929.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001141.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001065.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000293.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001541.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001249.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000061.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001208.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002074.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001795.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000773.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001519.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001155.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001906.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000593.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000920.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001569.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001213.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001573.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001978.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000646.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000637.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002110.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001866.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000762.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000009.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001945.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001624.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000859.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000968.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000355.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001214.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001009.jpg (deflated 5%)\n", "updating: smartvision_dataset/detection/images/image_000394.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000389.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001604.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001184.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000347.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000386.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001391.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001147.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000509.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000683.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000496.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000798.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001262.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001116.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000631.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001058.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000003.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000169.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001910.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002054.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001371.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000804.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001986.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000651.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000830.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001701.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001871.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000478.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001645.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001025.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000674.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000801.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001828.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000307.jpg (deflated 6%)\n", "updating: smartvision_dataset/detection/images/image_000949.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000959.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000613.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000292.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000532.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001869.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001686.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001667.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000927.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001566.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000000.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001279.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000230.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000896.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002048.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001937.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001079.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000245.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000940.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001290.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002003.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000835.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000845.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001486.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000777.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000691.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000218.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000286.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000019.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001776.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000298.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000315.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001616.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001349.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000719.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001761.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000895.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000456.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000176.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000655.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001278.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001356.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002150.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002194.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000506.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000150.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000999.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001583.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000093.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001138.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000291.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001054.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001959.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001955.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000025.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001134.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001331.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001982.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001342.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000664.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001430.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002135.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001296.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000515.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001905.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000621.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000249.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001737.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001128.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000909.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001232.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001923.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001900.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000886.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000629.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000107.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000158.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001317.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000462.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001780.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000072.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000488.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001274.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001460.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000055.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001865.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001534.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001765.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001674.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000063.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001515.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000111.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000630.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001717.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000807.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000343.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000354.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000841.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000739.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001298.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001076.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000141.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000012.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001435.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001210.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002067.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001198.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001455.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000296.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001257.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000713.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000185.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000672.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002085.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001286.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000740.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001853.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001047.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001463.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000870.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000381.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000635.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001950.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001523.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001944.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000106.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000247.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000224.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002136.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001860.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001267.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000774.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_002166.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000251.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001718.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002129.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001527.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001222.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000126.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001403.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000910.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001724.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001889.jpg (deflated 5%)\n", "updating: smartvision_dataset/detection/images/image_000614.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002101.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000639.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001774.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_002088.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001143.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000024.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000648.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000662.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002100.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000988.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001368.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000868.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000338.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001872.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000124.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001883.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000239.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001069.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002121.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000501.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000842.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000884.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000453.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000571.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001549.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000543.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000166.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001858.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001283.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001165.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000360.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001670.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000745.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000457.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001708.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001084.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001035.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001300.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001282.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001130.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001461.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000932.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001705.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001360.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000926.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001757.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001123.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000650.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001187.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000761.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001954.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001297.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000399.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000183.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000956.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000971.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000027.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001169.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001062.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001402.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001011.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001659.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000074.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000133.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002017.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001779.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000182.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001102.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001746.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001636.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000869.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001251.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000824.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001696.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000045.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000094.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002089.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000377.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001229.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001193.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000423.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001965.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001498.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000333.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001070.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000060.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001336.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001237.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000494.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002144.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002076.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001398.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001739.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000725.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001803.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001287.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001321.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001836.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001240.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000788.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000652.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000643.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001158.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002035.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000473.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000101.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000531.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001327.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000619.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000578.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001075.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002086.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000521.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001584.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002152.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000410.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001540.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001647.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000493.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002187.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000523.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002057.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000257.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001129.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000280.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001966.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001894.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000935.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001985.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000979.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000260.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000590.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001471.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000356.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001984.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001827.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000513.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000427.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001677.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002081.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001045.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001269.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001150.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002189.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000445.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000685.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000747.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001936.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002112.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000518.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001082.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001216.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000544.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000752.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001553.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001244.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000666.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002012.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000435.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000160.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001043.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001152.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001862.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000465.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001629.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001445.jpg (deflated 5%)\n", "updating: smartvision_dataset/detection/images/image_001660.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002179.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000098.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001412.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002032.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001308.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001236.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000718.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002099.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000499.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001234.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000703.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000152.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000889.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002083.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001437.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000766.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000208.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001485.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001031.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000128.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001092.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001346.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001156.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001427.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001913.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001107.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000892.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000484.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001389.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000091.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001172.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001880.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000306.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000514.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002114.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000434.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000893.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000044.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001292.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001595.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001528.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001028.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001341.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001723.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000342.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001260.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000311.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000814.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001345.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000540.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002038.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001087.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000694.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_002151.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001046.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000534.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001377.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001771.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002007.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000154.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001952.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000569.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000771.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002113.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001053.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000634.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001098.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001235.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000856.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000243.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001972.jpg (deflated 5%)\n", "updating: smartvision_dataset/detection/images/image_001925.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000780.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000928.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001029.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001590.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001922.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002163.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001217.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001005.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001378.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001537.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001245.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000329.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000077.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001715.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002126.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001535.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001017.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001264.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000273.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000398.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000782.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002047.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001892.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000967.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000980.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000382.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001196.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001531.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000214.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001974.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001221.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001288.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001927.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000001.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000806.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001751.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000888.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000362.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001436.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000084.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000839.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000728.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000029.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001622.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000549.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000951.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001547.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000978.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001509.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000827.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000625.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001750.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001538.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000400.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001431.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000813.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000963.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000638.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000232.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001024.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000919.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000359.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001077.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001864.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002053.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001261.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000525.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002120.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001799.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001976.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001036.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_002193.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000219.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000816.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001206.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001063.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001589.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000057.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001223.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001276.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000117.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001103.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002142.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002138.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001167.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001136.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000125.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001418.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001874.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000820.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001018.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000452.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000287.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000557.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000724.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000372.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001713.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001399.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001796.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002184.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001475.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000030.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001877.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000742.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000519.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000542.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000497.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001057.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001973.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000109.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001709.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001325.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001313.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000304.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002087.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001477.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001066.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001375.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000455.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001935.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001768.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001182.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000048.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002084.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000592.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000717.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000851.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001174.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000181.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000516.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001476.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002140.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001562.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001468.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001781.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002063.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000476.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001643.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000159.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001241.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000620.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000108.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001467.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002153.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000881.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000793.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001991.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001743.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000309.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000948.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002161.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000526.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_002109.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_002095.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002046.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000405.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001004.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001081.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001246.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001448.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000480.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002115.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000805.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001145.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000970.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002077.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000168.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002065.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000772.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000313.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001914.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001495.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001933.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000611.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001328.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000865.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001669.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001772.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001766.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001638.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000676.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001572.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001397.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000537.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000085.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001536.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000140.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001127.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000225.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001979.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001464.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000486.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001121.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001756.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000708.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000924.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001748.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001020.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000113.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000945.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000153.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001095.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001934.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001634.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001912.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001688.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001626.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001621.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000529.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001571.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001526.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000803.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001056.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001311.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000485.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000950.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000875.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000746.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000026.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002051.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002080.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000588.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001706.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000902.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000034.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000162.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000290.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000601.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001682.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000447.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001597.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000240.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001561.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000671.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001815.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001909.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000649.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001545.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001778.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001289.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000326.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000114.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001915.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000698.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001598.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001019.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000317.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000786.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000942.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000446.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000349.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000574.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001100.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000749.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001885.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001316.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002021.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001609.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002049.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001591.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002107.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001006.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001015.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001110.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001592.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002026.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001164.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000262.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001805.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000013.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001720.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000068.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000736.jpg (deflated 5%)\n", "updating: smartvision_dataset/detection/images/image_001067.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001227.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001879.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000217.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000444.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001833.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000015.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001963.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001808.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001382.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000174.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001939.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001996.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001452.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000020.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001494.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001329.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000222.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001785.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001568.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000670.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001614.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000763.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000090.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001146.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000962.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001654.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000082.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000058.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001804.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000144.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002070.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000730.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000882.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000209.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001697.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001685.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001492.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001168.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001419.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001661.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002199.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001201.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000617.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002169.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000430.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000155.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000690.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000137.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000220.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001762.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001333.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000041.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000693.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001272.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001983.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000344.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001474.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000380.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002060.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000838.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001178.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001083.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000781.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001563.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001521.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001770.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002170.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000616.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000431.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000393.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000067.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001396.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002052.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002204.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002018.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000872.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000947.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001837.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000843.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001848.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000769.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001126.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000741.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000083.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000695.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000589.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001215.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000016.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001652.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001620.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001049.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002175.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001440.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001977.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000834.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002059.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000050.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001782.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001383.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001942.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000297.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002157.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001921.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000684.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001362.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000028.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001106.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000564.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000078.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001655.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001735.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001859.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001224.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001956.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001520.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000587.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002183.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001002.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001699.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000522.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001139.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001489.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001447.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001332.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000439.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000139.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000022.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000586.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002134.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002031.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000376.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001425.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000914.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002091.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000905.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000192.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001845.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000023.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000891.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000982.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001064.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000517.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001060.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000731.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000056.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001401.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000127.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002106.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001394.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002158.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001940.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_002075.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000669.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000421.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001443.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001850.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000164.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000328.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001462.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_002154.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001503.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000341.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001416.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002200.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000272.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001700.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002042.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000829.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000946.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001385.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001648.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001951.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000759.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002145.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000953.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000369.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_002133.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000550.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000039.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000119.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001722.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001556.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001710.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001280.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000612.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001740.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001093.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001323.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001033.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000415.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000699.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001326.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001558.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001395.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001374.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002025.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001340.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000475.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001016.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001090.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001363.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000267.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000121.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001695.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000388.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000861.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000118.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002205.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000874.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000384.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001038.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000712.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000211.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001373.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000112.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001271.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000375.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000373.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000371.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001898.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000716.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000913.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002196.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000481.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000878.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000252.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001694.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001631.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000866.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001355.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000596.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001581.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000035.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001968.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001344.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002177.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000784.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001843.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000314.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001358.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001702.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000483.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000624.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000732.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002203.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000367.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_002045.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001649.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001672.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000645.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001663.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000627.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001078.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000903.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000661.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000528.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000867.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000352.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001731.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000442.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000701.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000822.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001830.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001727.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000351.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001505.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001580.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000931.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001281.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000263.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001068.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001612.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001975.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002041.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000641.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001275.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000583.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001557.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000837.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001228.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001539.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000005.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001981.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000990.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000051.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000675.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000032.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000374.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000921.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001841.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001190.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002069.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000551.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000840.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001472.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001125.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000477.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000660.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000539.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002064.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000010.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001657.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001496.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000507.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000335.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000092.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001601.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000250.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001988.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001424.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000274.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000760.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001086.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000387.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000710.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000080.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001788.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001802.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000186.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001151.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001839.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001856.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000938.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001238.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000105.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000103.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000558.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002027.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001202.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000179.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000584.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000633.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001831.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000776.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002149.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000605.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001309.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000855.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000193.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000008.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000673.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000785.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002050.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001411.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001379.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000401.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002092.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000379.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001352.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000811.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000180.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002125.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001511.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002061.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000756.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000269.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000277.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002098.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000325.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001747.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001753.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000767.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001690.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001175.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000190.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001873.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000066.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001199.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001361.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001044.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001132.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001729.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001393.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002171.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001890.jpg (deflated 5%)\n", "updating: smartvision_dataset/detection/images/image_001619.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000429.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000097.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001607.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001381.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001844.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001577.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001502.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000944.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002123.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001295.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000567.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000901.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001552.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001703.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000302.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001048.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002172.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000833.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001736.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000599.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000464.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001832.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001406.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000657.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002156.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000323.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000757.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000151.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001930.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000131.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001482.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001588.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001226.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002056.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000043.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002002.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001041.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001926.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001868.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000229.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002176.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001971.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000248.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001824.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001191.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000687.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000688.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000541.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000200.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000156.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000451.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001651.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001119.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000233.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002178.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000468.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000850.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001037.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000751.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000862.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001347.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001826.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000815.jpg (deflated 6%)\n", "updating: smartvision_dataset/detection/images/image_001042.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001630.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000904.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002033.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000095.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000799.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000533.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001039.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000985.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000037.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001559.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002122.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000402.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000308.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001160.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000408.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002079.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001758.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000755.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000436.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000212.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001305.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001919.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002062.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001270.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001997.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000796.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002208.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000653.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000361.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001337.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000284.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001330.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001181.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001784.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001120.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000370.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000954.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_002078.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001212.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001438.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001197.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002000.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000582.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000321.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000568.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000104.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000448.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001546.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001680.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001319.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000110.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000320.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001099.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000079.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000327.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001769.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000707.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001285.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002201.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001564.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000294.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001050.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001071.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001487.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000418.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001602.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002009.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000977.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000952.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000819.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000758.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000353.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001104.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000566.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000906.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001501.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000122.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001122.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001764.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000743.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000912.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001026.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001314.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000577.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000213.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000563.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002071.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000993.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001109.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001728.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000795.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000331.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000460.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001752.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000130.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000147.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001579.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001392.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001734.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_002068.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002066.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_002160.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000336.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000505.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001506.jpg (deflated 5%)\n", "updating: smartvision_dataset/detection/images/image_000242.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000198.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001613.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001646.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001689.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001256.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001886.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001801.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000014.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000581.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001807.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002103.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000964.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001967.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002006.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001442.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001565.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000424.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001263.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000177.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000764.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000466.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002181.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000007.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000210.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002001.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001504.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000640.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002005.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001180.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000936.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000259.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001231.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001080.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001491.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001989.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001516.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000562.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001507.jpg (deflated 5%)\n", "updating: smartvision_dataset/detection/images/image_001153.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000622.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001800.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000227.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001266.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001745.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001684.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002198.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001738.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000033.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000199.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001343.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000723.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001818.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000188.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000450.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001387.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001384.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001789.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000591.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000358.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001775.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000734.jpg (deflated 5%)\n", "updating: smartvision_dataset/detection/images/image_001357.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000642.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001255.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001294.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001364.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000419.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000714.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000237.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001875.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001650.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001439.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001192.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000823.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001148.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000828.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000536.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000238.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001454.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001627.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000658.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000681.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001608.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000679.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001797.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002130.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001786.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001247.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001478.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001533.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000572.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001252.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002010.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001687.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001367.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000170.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000955.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001615.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002148.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000702.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000346.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000831.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001301.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001353.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001413.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001777.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001664.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001707.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000498.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000305.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000579.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000668.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001653.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001842.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001204.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002188.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_002207.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001633.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001790.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001692.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000322.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001258.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000413.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000576.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000420.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001798.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000937.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001811.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000894.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000715.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002024.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000062.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000512.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000791.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001010.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000264.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002127.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000383.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001806.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000397.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001760.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000654.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000744.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002022.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002143.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000508.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001744.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001185.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001673.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000552.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000721.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000312.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000206.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000417.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001683.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001473.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001162.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000255.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001726.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000283.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001543.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001299.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000915.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001112.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001481.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000330.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001171.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001822.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001907.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000602.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000973.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000459.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001372.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002117.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001882.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001030.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001635.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000378.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000986.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002072.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000556.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000877.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000279.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000510.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000363.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000966.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000809.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000070.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000554.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001441.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000792.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001586.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001135.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001400.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001159.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001469.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000266.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002192.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_002040.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001719.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001931.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000997.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000925.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002159.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000511.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000812.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000426.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000680.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000463.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000205.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001259.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001366.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001812.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000880.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001891.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_002190.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000606.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001712.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002029.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001594.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001273.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002191.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001947.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000490.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002023.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002173.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000006.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000011.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000407.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001637.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001544.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001470.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000432.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000145.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000610.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002097.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000923.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000271.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000441.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000364.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002028.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001220.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000575.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000667.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000520.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000704.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001920.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001508.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000981.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002105.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001932.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001315.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000403.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001908.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000998.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001960.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002111.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002036.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001825.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000059.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000234.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001676.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000123.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001426.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001681.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001365.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001585.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000929.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001088.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001176.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000890.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001835.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000031.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000350.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000161.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001820.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001887.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000395.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001115.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000849.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001962.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002186.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001902.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001632.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001421.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000810.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001901.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001821.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000991.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000750.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002102.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001524.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000545.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001404.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000201.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000167.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001994.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001140.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000765.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001946.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001428.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001250.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001704.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000319.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001596.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000989.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002030.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001303.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000907.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000366.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000081.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000974.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000797.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002108.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000191.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000879.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001759.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000738.jpg (deflated 5%)\n", "updating: smartvision_dataset/detection/images/image_001518.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002162.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000623.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_002020.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000310.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000994.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001867.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000524.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001195.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001990.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001312.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000258.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001575.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000474.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000228.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001434.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000802.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001034.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001480.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001714.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000768.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001334.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001000.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001423.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001500.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001410.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000116.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000735.jpg (deflated 5%)\n", "updating: smartvision_dataset/detection/images/image_001514.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000972.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001854.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000202.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001610.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000787.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001183.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000689.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000099.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000908.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001101.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001388.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001943.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000832.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001881.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001118.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000132.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001348.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002202.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000700.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001293.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000454.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001218.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000129.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001032.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000301.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001335.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000053.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000073.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002124.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001999.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000194.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000087.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001911.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000897.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000726.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001205.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000052.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000089.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000546.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000256.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001698.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000917.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000502.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000656.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000898.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000632.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000663.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000253.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001754.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001265.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001656.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000047.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000021.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001582.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000173.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000686.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000470.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001449.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001578.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000705.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000600.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001903.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000281.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001243.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000696.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001639.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000852.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000337.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000789.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001555.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000636.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001773.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000275.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000876.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002174.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001003.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001970.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000216.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000922.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002014.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001587.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000530.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002185.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001662.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001665.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000075.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000163.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000836.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000609.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001013.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001322.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001767.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000885.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001277.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000939.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001961.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001755.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001532.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001186.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000157.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001995.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001284.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_002195.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000196.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000149.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001453.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001928.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001096.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000873.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000391.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000175.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001074.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001451.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001499.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000071.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000853.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001291.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000479.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001916.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002165.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000597.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001339.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002137.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001179.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000983.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000076.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001350.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000916.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001207.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001863.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001787.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000204.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001091.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000899.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000548.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000783.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000644.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001783.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001248.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001059.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000324.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001405.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000054.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001429.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000647.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001640.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001829.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002008.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002034.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001791.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001567.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001415.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000847.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001918.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000489.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001446.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000428.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000469.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001351.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000808.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000100.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002132.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001603.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000416.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001878.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001730.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001668.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001813.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000040.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002131.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001307.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000244.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000818.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001268.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001161.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000134.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000817.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001007.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000607.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000727.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001370.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000270.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001819.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000598.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001458.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000065.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001852.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000547.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001117.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001149.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000794.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000223.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001422.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000883.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001359.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002167.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001513.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000553.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000064.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000626.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001369.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000821.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001320.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001658.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000618.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000236.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001450.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000984.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000433.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002016.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002104.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001574.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000207.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001014.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000854.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000778.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001958.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001433.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000385.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001466.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001993.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000987.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001897.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001310.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000340.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001809.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000682.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001253.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001225.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001022.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001073.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000770.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000560.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001239.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001948.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001917.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000733.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000844.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002164.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001493.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001666.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001884.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001055.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001157.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002082.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001742.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001548.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001512.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001605.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001899.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000729.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000102.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000943.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001711.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001194.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001876.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001380.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001233.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001133.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001324.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002093.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000800.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001953.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001987.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000002.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001721.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001154.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000339.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000570.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001628.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000585.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000268.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001895.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000933.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001625.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001675.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000226.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001052.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000825.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002118.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000790.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001888.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001304.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001097.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001870.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000038.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000857.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000437.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000148.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000677.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002147.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001600.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001386.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001457.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000975.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000203.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001242.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000282.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000409.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000449.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001570.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001550.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001834.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001529.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001318.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000887.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001021.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000900.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001938.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000692.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001998.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000049.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000261.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001969.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_002011.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001691.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001483.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000492.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001623.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001390.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000969.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001851.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002182.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002058.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000471.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000042.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001302.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000265.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001163.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001551.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002044.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001111.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001484.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000918.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000300.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000965.jpg (deflated 5%)\n", "updating: smartvision_dataset/detection/images/image_001094.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001051.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001479.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001949.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002139.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000235.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001741.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000709.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001023.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000958.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000934.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000289.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001173.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001376.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001924.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001189.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002180.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002019.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001814.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000189.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001904.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000288.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002073.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000345.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002039.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000115.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000004.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001061.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001840.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002197.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000538.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001012.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001177.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001792.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001124.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000390.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000461.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001749.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000120.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001209.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001823.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000960.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000737.jpg (deflated 5%)\n", "updating: smartvision_dataset/detection/images/image_001488.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000018.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002155.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001679.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001896.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000411.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001166.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000404.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000318.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000425.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000221.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000604.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000136.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001992.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000961.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001137.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001861.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001611.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002055.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002206.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001838.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000826.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001144.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000846.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000930.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000392.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000088.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000142.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000527.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001027.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000565.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001542.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000482.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001254.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_002116.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000187.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001040.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001230.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001849.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000561.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000254.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000241.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000495.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000779.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000491.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001417.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002094.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000316.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000458.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000172.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001211.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000594.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000096.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001432.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001763.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000848.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000754.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000697.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000595.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001114.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000365.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001893.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000334.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001857.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000017.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000299.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000438.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000659.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000722.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001444.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001517.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000467.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000440.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000995.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000278.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000143.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000858.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000487.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001420.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000628.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001793.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000069.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001456.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001170.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001816.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001200.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000135.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001560.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001525.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001593.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001847.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000086.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000580.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000911.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000285.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002146.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001617.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001465.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000992.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001644.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000195.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000178.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000422.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002168.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001306.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001219.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001554.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000276.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001733.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002096.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000231.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000406.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000348.jpg (deflated 6%)\n", "updating: smartvision_dataset/detection/images/image_000996.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001716.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000500.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001606.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000863.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001354.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001522.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000246.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000146.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000678.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_001497.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_002043.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001490.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000165.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002004.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000295.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000555.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001131.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001964.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000753.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000941.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001459.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001409.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001203.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001001.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000504.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000412.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000775.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/images/image_000615.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000184.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000665.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002037.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000559.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001008.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001108.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001618.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001957.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000332.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000573.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000976.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001407.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001530.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001599.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001810.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000368.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001678.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000036.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001732.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000472.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001671.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000171.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001113.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001408.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000535.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001794.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000396.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000706.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000303.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001188.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000046.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_000443.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001641.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_000138.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000414.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_001510.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001142.jpg (deflated 4%)\n", "updating: smartvision_dataset/detection/images/image_001941.jpg (deflated 1%)\n", "updating: smartvision_dataset/detection/images/image_000720.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000864.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_002119.jpg (deflated 3%)\n", "updating: smartvision_dataset/detection/images/image_001693.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001855.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001414.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_001089.jpg (deflated 0%)\n", "updating: smartvision_dataset/detection/images/image_000860.jpg (deflated 2%)\n", "updating: smartvision_dataset/detection/labels.cache (deflated 92%)\n", "updating: smartvision_dataset/detection/labels/ (stored 0%)\n", "updating: smartvision_dataset/detection/labels/image_001492.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_002092.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000333.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001186.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000316.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001372.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000028.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_002009.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001969.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000795.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001774.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001844.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_001632.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001476.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000478.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000783.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001289.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_002186.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000567.txt (deflated 14%)\n", "updating: smartvision_dataset/detection/labels/image_000800.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000176.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001007.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001734.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001109.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001842.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000741.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000082.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_002114.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000602.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000231.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_001645.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000418.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000311.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000892.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001575.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000170.txt (deflated 35%)\n", "updating: smartvision_dataset/detection/labels/image_001888.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001585.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000623.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001395.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001699.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_001321.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001620.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_001447.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_002123.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_001853.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001167.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000603.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001902.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000672.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001051.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001881.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000941.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000819.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_002180.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000396.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001723.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001690.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001024.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000874.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001273.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000126.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000433.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000379.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000022.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000856.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000727.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000842.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000955.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001319.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_002152.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_001218.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000566.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000980.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000030.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000179.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000187.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001675.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000026.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000005.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000451.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000460.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000757.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000394.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001708.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000158.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001544.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001410.txt (deflated 13%)\n", "updating: smartvision_dataset/detection/labels/image_000177.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001350.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000967.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000247.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001484.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_002146.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000125.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000612.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001240.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000050.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001290.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000793.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001605.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001598.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001506.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001098.txt (deflated 13%)\n", "updating: smartvision_dataset/detection/labels/image_000771.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000145.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001770.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_002038.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_000190.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001115.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000400.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001627.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000518.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_000364.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_002005.txt (deflated 40%)\n", "updating: smartvision_dataset/detection/labels/image_000344.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001393.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001260.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000224.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001895.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001570.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000206.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002191.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_002037.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_002061.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001517.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001716.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000979.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000960.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002169.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000843.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001929.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001672.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001820.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001312.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001263.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_002053.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_000469.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000452.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000440.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001421.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001836.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001212.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001309.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001095.txt (deflated 13%)\n", "updating: smartvision_dataset/detection/labels/image_000998.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000947.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_002099.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000512.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001768.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001742.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000436.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000205.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001701.txt (deflated 16%)\n", "updating: smartvision_dataset/detection/labels/image_000256.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_001536.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001746.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001735.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000536.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_002109.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000663.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_002194.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_001682.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001228.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001070.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_002042.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001348.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001553.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_002151.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001991.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001600.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001220.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000560.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000004.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001979.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000456.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001333.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000018.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001384.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000057.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001759.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000916.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001409.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002124.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000235.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001663.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000959.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001721.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000467.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_002110.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001596.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001150.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001833.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000431.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001158.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_000281.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001558.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001689.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000514.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000382.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001674.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001446.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_001293.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001090.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001500.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001566.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001062.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_002084.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_001601.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_001277.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000666.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001574.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002058.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001217.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001496.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001359.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000833.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000526.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001136.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001414.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001958.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_002071.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001817.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001872.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_002039.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000355.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000367.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_001810.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000498.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000485.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000489.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001340.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_002157.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001840.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000542.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000762.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000687.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000492.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000422.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000726.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000257.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_001057.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_001306.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001471.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000524.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000912.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_001526.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000029.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001236.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001412.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_001437.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000284.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000470.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001987.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000996.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000342.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_002095.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000564.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_002147.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000389.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001771.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000556.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001229.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_001300.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001738.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001922.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001243.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000709.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_000230.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001258.txt (deflated 13%)\n", "updating: smartvision_dataset/detection/labels/image_000378.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_002141.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000823.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000733.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000351.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001177.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_000042.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_002075.txt (deflated 41%)\n", "updating: smartvision_dataset/detection/labels/image_002041.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001775.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000371.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000046.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001710.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001549.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000658.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001745.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001751.txt (deflated 13%)\n", "updating: smartvision_dataset/detection/labels/image_001417.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000015.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001878.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001025.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_000806.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_002120.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001153.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000121.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000381.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001729.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001898.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000172.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000930.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001677.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_001685.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_000732.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001023.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000677.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000442.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001245.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000605.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001204.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_002012.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000102.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000761.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001652.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_001274.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_002105.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_000594.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_002130.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000363.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_001648.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000665.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000091.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000858.txt (deflated 41%)\n", "updating: smartvision_dataset/detection/labels/image_000776.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001756.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000098.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_002100.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000287.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000417.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_001959.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000918.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_000262.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001030.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001641.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000534.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002196.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001852.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000878.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000746.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001249.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000817.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001128.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000593.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000931.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000171.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001256.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001684.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_000781.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_002006.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001374.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000356.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001519.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001039.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000578.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000768.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001564.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_000845.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000011.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000089.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000053.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000076.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000000.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000001.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000203.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001750.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000481.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001230.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000883.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000516.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000197.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001451.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000220.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001879.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001056.txt (deflated 40%)\n", "updating: smartvision_dataset/detection/labels/image_002031.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001477.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_002073.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001404.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001344.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001697.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001944.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000511.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000222.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000347.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001892.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001027.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_002087.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_001050.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_001266.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001311.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_002049.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001896.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000409.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001955.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000326.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001580.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001270.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000313.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000807.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000647.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000185.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001416.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001345.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001151.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000704.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000052.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000065.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001444.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001233.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_002159.txt (deflated 41%)\n", "updating: smartvision_dataset/detection/labels/image_000679.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000259.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_001548.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000844.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000999.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001413.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000904.txt (deflated 41%)\n", "updating: smartvision_dataset/detection/labels/image_001326.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000085.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000619.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001009.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001636.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001434.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000866.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001021.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_000706.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000937.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000935.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000166.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001561.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000724.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001743.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000134.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_002068.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000062.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000246.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001916.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000488.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000721.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_002206.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000261.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000661.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001223.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001134.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001315.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000034.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000907.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001424.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000693.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001855.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001044.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001577.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001445.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_000286.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000178.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001191.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000390.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000735.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000646.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001894.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001886.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000698.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001919.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000915.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000368.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000509.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001059.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_001238.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001377.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000181.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000453.txt (deflated 35%)\n", "updating: smartvision_dataset/detection/labels/image_001657.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000139.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001760.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_002143.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000790.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000820.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001918.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001974.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000853.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000215.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000568.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000850.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001541.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001920.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_002063.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001316.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000081.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001472.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001140.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001155.txt (deflated 40%)\n", "updating: smartvision_dataset/detection/labels/image_000365.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000652.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001903.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001786.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002149.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001426.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000349.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000614.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001442.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001063.txt (deflated 30%)\n", "updating: smartvision_dataset/detection/labels/image_001777.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001389.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000251.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001624.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_002153.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000494.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001250.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001198.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001687.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_000945.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_002209.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000810.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001785.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_001671.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001355.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001637.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000437.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000523.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_001127.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000484.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001639.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001099.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001679.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000586.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000777.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001691.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001953.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_002171.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_001088.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_001512.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000066.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000415.txt (deflated 14%)\n", "updating: smartvision_dataset/detection/labels/image_000278.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000128.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000211.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002135.txt (deflated 30%)\n", "updating: smartvision_dataset/detection/labels/image_000376.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000562.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001105.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_001368.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001713.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000331.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_002089.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000669.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000763.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001702.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001329.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001502.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001117.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000310.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001819.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001065.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_002020.txt (deflated 40%)\n", "updating: smartvision_dataset/detection/labels/image_001650.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000105.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_001398.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000131.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_000135.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000580.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001016.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001900.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001171.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001457.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000964.txt (deflated 18%)\n", "updating: smartvision_dataset/detection/labels/image_000696.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_001465.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001462.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000970.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_002007.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000229.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001829.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001435.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000864.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001411.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001943.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000527.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001815.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001397.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001850.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000399.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_000366.txt (deflated 14%)\n", "updating: smartvision_dataset/detection/labels/image_000927.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001406.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000595.txt (deflated 35%)\n", "updating: smartvision_dataset/detection/labels/image_001662.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001436.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000961.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001532.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001176.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000234.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_002102.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_000579.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001114.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001978.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001327.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001676.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001168.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000193.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001288.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_001005.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001269.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000301.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001194.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_001504.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000922.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000143.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001172.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_002013.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000625.txt (deflated 35%)\n", "updating: smartvision_dataset/detection/labels/image_000705.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_002080.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000048.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000397.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_000127.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001284.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000074.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000909.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001046.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_002117.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000520.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000167.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001183.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000120.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001762.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_002189.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001019.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000448.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000573.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001301.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000238.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_000546.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001252.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000588.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002193.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001076.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000472.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000902.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000712.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_001812.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001479.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000083.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000552.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001353.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001611.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001049.txt (deflated 32%)\n", "updating: smartvision_dataset/detection/labels/image_002116.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002018.txt (deflated 21%)\n", "updating: smartvision_dataset/detection/labels/image_001882.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001154.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001972.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000063.txt (deflated 24%)\n", "updating: smartvision_dataset/detection/labels/image_002079.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001994.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000106.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000225.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001002.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001510.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_002035.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000946.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001704.txt (deflated 13%)\n", "updating: smartvision_dataset/detection/labels/image_000728.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001932.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001089.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000009.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001265.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000383.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000092.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001622.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000799.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000075.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000900.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001887.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001259.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000321.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000204.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000836.txt (deflated 35%)\n", "updating: smartvision_dataset/detection/labels/image_000141.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001781.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_002156.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_000877.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001013.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000080.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001538.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001680.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000832.txt (deflated 14%)\n", "updating: smartvision_dataset/detection/labels/image_001552.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_001530.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001753.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000049.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000110.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000769.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000882.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002085.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000942.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000434.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001646.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_002199.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000420.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000610.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000639.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_002070.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000519.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001324.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000375.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001453.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000966.txt (deflated 3%)\n", "updating: smartvision_dataset/detection/labels/image_000101.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_002188.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001970.txt (deflated 33%)\n", "updating: smartvision_dataset/detection/labels/image_001793.txt (deflated 16%)\n", "updating: smartvision_dataset/detection/labels/image_002148.txt (deflated 13%)\n", "updating: smartvision_dataset/detection/labels/image_000555.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_001468.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001795.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_001913.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000513.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000634.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_002057.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001082.txt (deflated 16%)\n", "updating: smartvision_dataset/detection/labels/image_001291.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001607.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_000700.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_001917.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000540.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001364.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000929.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001143.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001219.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_002208.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_001209.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001928.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000748.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_000019.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000008.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001572.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000758.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001482.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001809.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001077.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001295.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_000090.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000037.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000097.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000306.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001857.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001731.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_001096.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_000322.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_002127.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000606.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000401.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000374.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001525.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000608.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_000482.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000406.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001287.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001280.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001613.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001618.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_000837.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001638.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_000764.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000398.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_000975.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001592.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000630.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001849.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_001275.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001681.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000615.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000938.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001351.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_001452.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_001696.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_002192.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000716.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001055.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000341.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000786.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001028.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001107.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000851.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001108.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_001200.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000659.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_002040.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000893.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000644.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001654.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001906.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000273.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001382.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000479.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001148.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001361.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001531.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001041.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000702.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000633.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000785.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001780.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001371.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000577.txt (deflated 24%)\n", "updating: smartvision_dataset/detection/labels/image_001226.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000025.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000506.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001112.txt (deflated 18%)\n", "updating: smartvision_dataset/detection/labels/image_000710.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001031.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000297.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001669.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001433.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001724.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001347.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_000622.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000834.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000872.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001797.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001455.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_001373.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000271.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001993.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001074.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000686.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001811.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001221.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001338.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001080.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001257.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000447.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001945.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001508.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001085.txt (deflated 30%)\n", "updating: smartvision_dataset/detection/labels/image_001087.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_001996.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000430.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000288.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000936.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000787.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_000370.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000369.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001017.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001876.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000643.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001563.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000137.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001392.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000194.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001034.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001609.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000337.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000416.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001225.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000550.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001897.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000094.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001281.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000840.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000228.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001073.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_001166.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001846.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_001755.txt (deflated 13%)\n", "updating: smartvision_dataset/detection/labels/image_000532.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000184.txt (deflated 24%)\n", "updating: smartvision_dataset/detection/labels/image_000544.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000557.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001396.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_001706.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000077.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001346.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000804.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001394.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_002150.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000572.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001386.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_002077.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001965.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001122.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001586.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_000410.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000212.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002054.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000954.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002195.txt (deflated 33%)\n", "updating: smartvision_dataset/detection/labels/image_002177.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001308.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_002029.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001642.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001299.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001113.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_000088.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_002064.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001921.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001884.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001951.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000574.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001909.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001533.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000662.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000236.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_002161.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001910.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001761.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001313.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000237.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001867.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000829.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_001796.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001962.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_002113.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000319.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001175.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001628.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000160.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001205.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001001.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001307.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000674.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001473.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000655.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000887.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001349.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_000888.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000163.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_002162.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000305.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000263.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_001227.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001547.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000906.txt (deflated 3%)\n", "updating: smartvision_dataset/detection/labels/image_000798.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000006.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000148.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_002052.txt (deflated 18%)\n", "updating: smartvision_dataset/detection/labels/image_001985.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001278.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_001527.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001425.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001448.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000474.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001015.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_000808.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001135.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001501.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000182.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001788.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000201.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000067.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001651.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000911.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_002121.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000910.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000232.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000107.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001782.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001387.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000292.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000963.txt (deflated 13%)\n", "updating: smartvision_dataset/detection/labels/image_001317.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000223.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001779.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001838.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001858.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001792.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000086.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000338.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001747.txt (deflated 21%)\n", "updating: smartvision_dataset/detection/labels/image_001694.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_001612.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_002107.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001764.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000036.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000753.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001871.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_002028.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_002174.txt (deflated 30%)\n", "updating: smartvision_dataset/detection/labels/image_002045.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001915.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000865.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001356.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_002198.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000289.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_002164.txt (deflated 30%)\n", "updating: smartvision_dataset/detection/labels/image_000346.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001807.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001147.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001213.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000688.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_002016.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_001870.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000984.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000192.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000723.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000426.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000013.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001141.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_000357.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000886.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000003.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001124.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001516.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000692.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000425.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001086.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000876.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001271.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001695.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001772.txt (deflated 16%)\n", "updating: smartvision_dataset/detection/labels/image_000636.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000770.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000191.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_002178.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_001441.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000118.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001935.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_002030.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000350.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000328.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000372.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000039.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000302.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001156.txt (deflated 40%)\n", "updating: smartvision_dataset/detection/labels/image_001614.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001583.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001358.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000792.txt (deflated 3%)\n", "updating: smartvision_dataset/detection/labels/image_001125.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000624.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001503.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001047.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000751.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001791.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001911.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000241.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001599.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001202.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000791.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_002140.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_002136.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_001982.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000880.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001159.txt (deflated 30%)\n", "updating: smartvision_dataset/detection/labels/image_001733.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_001443.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001029.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000895.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000538.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000738.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001305.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000293.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001914.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001145.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000136.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001208.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_002118.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000718.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002044.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001528.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000250.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001649.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000017.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001818.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000616.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000760.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001977.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001981.txt (deflated 13%)\n", "updating: smartvision_dataset/detection/labels/image_000859.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001264.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000953.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000164.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001363.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001045.txt (deflated 13%)\n", "updating: smartvision_dataset/detection/labels/image_000814.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000875.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_001880.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002027.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001610.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001179.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001239.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001466.txt (deflated 3%)\n", "updating: smartvision_dataset/detection/labels/image_002060.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001092.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000596.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001146.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001776.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_001336.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001845.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_001604.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_002055.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001067.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000879.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000604.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001765.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001907.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001937.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000841.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000265.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001010.txt (deflated 13%)\n", "updating: smartvision_dataset/detection/labels/image_000934.txt (deflated 16%)\n", "updating: smartvision_dataset/detection/labels/image_002097.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000897.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001066.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_001925.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001806.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000260.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_000972.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_002026.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000788.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000031.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001129.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001719.txt (deflated 16%)\n", "updating: smartvision_dataset/detection/labels/image_001843.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_001144.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001957.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_000730.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001524.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001286.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000854.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001705.txt (deflated 24%)\n", "updating: smartvision_dataset/detection/labels/image_001891.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000561.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_002115.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001110.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000863.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000424.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000264.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_002204.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001330.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000714.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000267.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_002091.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001942.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001490.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000480.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000142.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000973.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000589.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001588.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001251.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000309.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001320.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000926.txt (deflated 30%)\n", "updating: smartvision_dataset/detection/labels/image_000277.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000962.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000651.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001801.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000318.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001602.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_001440.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_002197.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000317.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000789.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_000500.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001773.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001693.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001581.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001763.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001666.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_001499.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000673.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001584.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000117.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000032.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001864.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000670.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_000099.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001478.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_002131.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_000958.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000939.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001839.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001033.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001787.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_002047.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_002048.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000517.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_000285.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000387.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_000391.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000884.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000175.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000582.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000920.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002160.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000642.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000462.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000483.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_002103.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001244.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_002098.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_001823.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001678.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001072.txt (deflated 40%)\n", "updating: smartvision_dataset/detection/labels/image_001633.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001428.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001712.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001064.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001567.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001673.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000521.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001123.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000592.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001160.txt (deflated 30%)\n", "updating: smartvision_dataset/detection/labels/image_001727.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001483.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000943.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001018.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000258.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000818.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000671.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_002154.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001933.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000784.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_001480.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_002014.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000554.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_000778.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001181.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_002024.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001766.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001629.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001707.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_002086.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002001.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_000903.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000078.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001119.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_001126.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001660.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000419.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001619.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000132.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001653.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000570.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001799.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001555.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001192.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_000515.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000159.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000925.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000773.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000413.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000461.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000348.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000949.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000087.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001589.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001805.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001276.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001665.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000881.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001954.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000463.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000138.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000745.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000831.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001485.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001385.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001215.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000835.txt (deflated 35%)\n", "updating: smartvision_dataset/detection/labels/image_000631.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001608.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001334.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001470.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001279.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001272.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001235.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000719.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_002179.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_001237.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_002167.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001730.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001968.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_002200.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001459.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001139.txt (deflated 41%)\n", "updating: smartvision_dataset/detection/labels/image_002015.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_001936.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001535.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000782.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001515.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000404.txt (deflated 16%)\n", "updating: smartvision_dataset/detection/labels/image_000501.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000685.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001828.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000027.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_002175.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_001493.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000186.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001522.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000565.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001827.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_002066.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001739.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_000607.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001491.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_002122.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000395.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000847.txt (deflated 22%)\n", "updating: smartvision_dataset/detection/labels/image_001934.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001617.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000044.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001644.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001464.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000502.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000352.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001834.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001261.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001118.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001335.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000213.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001950.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000291.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_001339.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001560.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001975.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000377.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001758.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001365.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000114.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000539.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_001851.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000779.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001060.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000944.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001752.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000982.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001722.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001407.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001069.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000152.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001523.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001728.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001133.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000270.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000919.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000694.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_000064.txt (deflated 24%)\n", "updating: smartvision_dataset/detection/labels/image_001131.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001989.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000689.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000041.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_001224.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001328.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000682.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001370.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_002202.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001460.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000815.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000571.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000816.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000496.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001474.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001331.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_002096.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001210.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000458.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001670.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001190.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001808.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001998.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000899.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_002043.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_000950.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000928.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001101.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000325.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001556.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_002145.txt (deflated 32%)\n", "updating: smartvision_dataset/detection/labels/image_000824.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000956.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001557.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_000070.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_002119.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000611.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000323.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001825.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001899.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001187.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000701.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_000361.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000335.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001405.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001992.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000813.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_001170.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001456.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000522.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001980.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001199.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001083.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000549.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_001794.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001268.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000601.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001569.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001006.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001008.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001201.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000901.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001869.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001450.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_001667.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001540.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001022.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_002074.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001184.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001863.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000438.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000803.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001740.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_000839.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000597.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000991.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001551.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000388.txt (deflated 41%)\n", "updating: smartvision_dataset/detection/labels/image_001130.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001000.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001803.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000510.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001578.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001182.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001388.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000989.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000968.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_002023.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000890.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_001732.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_002083.txt (deflated 30%)\n", "updating: smartvision_dataset/detection/labels/image_001582.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001926.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001912.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000675.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000282.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001961.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000917.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_002036.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_001822.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000055.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000360.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000871.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001079.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_000109.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000676.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000096.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001431.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001534.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001737.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001554.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000146.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000889.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001714.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000627.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000449.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000759.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000441.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000432.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001423.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001659.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001253.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001246.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000068.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001877.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001463.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001427.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000988.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000869.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000740.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_002126.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001058.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000969.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001035.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001174.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_002106.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_001173.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001847.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_001655.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001621.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_001783.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000330.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000497.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_002034.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_001106.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001904.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000924.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001521.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001304.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001294.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001664.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000629.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_000957.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000200.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000848.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000825.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000750.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000638.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001231.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000891.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_000609.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001014.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001185.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001162.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001454.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000852.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000266.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000563.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001802.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001988.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001964.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000324.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000744.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001189.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000084.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001111.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_002094.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000749.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000965.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000529.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001963.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000299.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001188.txt (deflated 35%)\n", "updating: smartvision_dataset/detection/labels/image_001940.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_002125.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000626.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000115.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001341.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000300.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000038.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000060.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000327.txt (deflated 14%)\n", "updating: smartvision_dataset/detection/labels/image_001094.txt (deflated 30%)\n", "updating: smartvision_dataset/detection/labels/image_000923.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000978.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001138.txt (deflated 41%)\n", "updating: smartvision_dataset/detection/labels/image_001635.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001688.txt (deflated 16%)\n", "updating: smartvision_dataset/detection/labels/image_000618.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001868.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001590.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000168.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000248.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001837.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001262.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000072.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000499.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000729.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000491.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_000590.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001986.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_000797.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001469.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001726.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001595.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001631.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001254.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001495.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000386.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001826.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000774.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001438.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_002129.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000058.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001011.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000411.txt (deflated 14%)\n", "updating: smartvision_dataset/detection/labels/image_001169.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000202.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002144.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000279.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000862.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000533.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000846.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001282.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001661.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001343.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001207.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000613.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001529.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000952.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001161.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001399.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000108.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000490.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001668.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000016.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001984.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001658.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_001248.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000199.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000951.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_002051.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001946.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_002184.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000149.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001054.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000537.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_002082.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001332.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000493.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000392.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000993.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001357.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000743.txt (deflated 41%)\n", "updating: smartvision_dataset/detection/labels/image_000553.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001686.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001367.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000648.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001873.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001509.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001769.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000715.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000905.txt (deflated 40%)\n", "updating: smartvision_dataset/detection/labels/image_001203.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000559.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001513.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001966.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000195.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001514.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000358.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001232.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001571.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000254.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001741.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000983.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000583.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001211.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002201.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_001432.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000632.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000290.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001003.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001630.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000014.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000780.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000772.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000345.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000873.txt (deflated 41%)\n", "updating: smartvision_dataset/detection/labels/image_001310.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000274.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000173.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000428.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000059.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000454.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000503.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_001283.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_002088.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001429.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001923.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000805.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001298.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001736.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_002182.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001692.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001325.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000457.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_001337.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000446.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000898.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000196.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_001142.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001754.txt (deflated 13%)\n", "updating: smartvision_dataset/detection/labels/image_000021.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_000153.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001376.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001683.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001952.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_002142.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001947.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001835.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000828.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000354.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000547.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000079.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000830.txt (deflated 3%)\n", "updating: smartvision_dataset/detection/labels/image_001790.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001908.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000093.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001400.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000755.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001489.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000545.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000641.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000269.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001100.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_000073.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000575.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000024.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000035.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000507.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_002017.txt (deflated 30%)\n", "updating: smartvision_dataset/detection/labels/image_000913.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000239.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000794.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001132.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001486.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000314.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000722.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000838.txt (deflated 40%)\n", "updating: smartvision_dataset/detection/labels/image_000855.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001720.txt (deflated 16%)\n", "updating: smartvision_dataset/detection/labels/image_000155.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000312.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001700.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001814.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000276.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000403.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000708.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001120.txt (deflated 16%)\n", "updating: smartvision_dataset/detection/labels/image_001757.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000487.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000033.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000620.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001593.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001647.txt (deflated 3%)\n", "updating: smartvision_dataset/detection/labels/image_000455.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_002008.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000860.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002081.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000775.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000295.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000308.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000116.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_002101.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_002112.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000280.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001505.txt (deflated 13%)\n", "updating: smartvision_dataset/detection/labels/image_000255.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_001390.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001042.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000699.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001999.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001061.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001222.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000123.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001718.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001594.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001939.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000986.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_002128.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_002093.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000585.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001841.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_002032.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000796.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_002185.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_000384.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000315.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_001813.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_002173.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000429.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000530.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000971.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001004.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000867.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_000450.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001383.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000617.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000124.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001832.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001391.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000174.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002069.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001430.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001091.txt (deflated 21%)\n", "updating: smartvision_dataset/detection/labels/image_001255.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000435.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000043.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000894.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001930.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000061.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000992.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000154.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001020.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_002168.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000373.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_002062.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000684.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000362.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001861.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000129.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001625.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000188.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000653.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002170.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_000598.txt (deflated 24%)\n", "updating: smartvision_dataset/detection/labels/image_000161.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001778.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_002076.txt (deflated 41%)\n", "updating: smartvision_dataset/detection/labels/image_001744.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000707.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_002205.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001967.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_002011.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000405.txt (deflated 14%)\n", "updating: smartvision_dataset/detection/labels/image_002111.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000587.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000103.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001615.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001997.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000767.txt (deflated 35%)\n", "updating: smartvision_dataset/detection/labels/image_001116.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001749.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000208.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000334.txt (deflated 22%)\n", "updating: smartvision_dataset/detection/labels/image_000713.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_001418.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001379.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000040.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000711.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_000150.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000445.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000690.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001511.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001302.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000997.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001859.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000703.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000857.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000189.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001579.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001214.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000268.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001402.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000245.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001983.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001616.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000691.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000981.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001458.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000581.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000476.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000353.txt (deflated 14%)\n", "updating: smartvision_dataset/detection/labels/image_002181.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001848.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000885.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001973.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000119.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000914.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001591.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_000933.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000940.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001634.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001545.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_001032.txt (deflated 13%)\n", "updating: smartvision_dataset/detection/labels/image_000558.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_002172.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_002203.txt (deflated 31%)\n", "updating: smartvision_dataset/detection/labels/image_001890.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001196.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000156.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000272.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001824.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000133.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000742.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001603.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000635.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000948.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001422.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000473.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001656.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001711.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001097.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_000664.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000359.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_001084.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001197.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000678.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_002025.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001303.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001874.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001242.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001026.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001976.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000827.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000717.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002134.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_001798.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_002010.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002207.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_001297.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000380.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000147.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000697.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_002090.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001234.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000754.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001784.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000656.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_002072.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000504.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_002067.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_002059.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001866.txt (deflated 18%)\n", "updating: smartvision_dataset/detection/labels/image_000739.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000681.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001539.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000144.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000657.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000576.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000976.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000122.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001467.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001439.txt (deflated 16%)\n", "updating: smartvision_dataset/detection/labels/image_001559.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001854.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001889.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_002166.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000737.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000826.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001163.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001369.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001071.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_001036.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000221.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000336.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001543.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001038.txt (deflated 32%)\n", "updating: smartvision_dataset/detection/labels/image_001856.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000402.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_002078.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000849.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001165.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_000660.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_002137.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000667.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000528.txt (deflated 24%)\n", "updating: smartvision_dataset/detection/labels/image_000543.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000551.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_000169.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000599.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000720.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_001948.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001475.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001104.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001487.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001075.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_000747.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001375.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_002004.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000412.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001178.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_002108.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001865.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001562.txt (deflated 30%)\n", "updating: smartvision_dataset/detection/labels/image_001040.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000045.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001354.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001520.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001971.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001342.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000296.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001053.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000183.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000868.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001164.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001408.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001318.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000985.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001267.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000465.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001292.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_002165.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_000821.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000811.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000600.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000162.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_002133.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_000214.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001901.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000548.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_002003.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/labels/image_001093.txt (deflated 30%)\n", "updating: smartvision_dataset/detection/labels/image_001052.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000486.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000466.txt (deflated 41%)\n", "updating: smartvision_dataset/detection/labels/image_000725.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001875.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002000.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_000932.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000393.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001960.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_000332.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000242.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000340.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000151.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002176.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001804.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_001037.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000861.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001380.txt (deflated 31%)\n", "updating: smartvision_dataset/detection/labels/image_000227.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001488.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000320.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001990.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000541.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_001518.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000112.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_000668.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_002158.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000385.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001995.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_001381.txt (deflated 31%)\n", "updating: smartvision_dataset/detection/labels/image_000054.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000423.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_000414.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000294.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000303.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001360.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000439.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000007.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001048.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_002033.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000010.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000650.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001565.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_001449.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_001597.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001831.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000535.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000459.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000307.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_000002.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_002132.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001497.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000802.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001767.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_002046.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_002056.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001941.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000637.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000252.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_000896.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000591.txt (deflated 8%)\n", "updating: smartvision_dataset/detection/labels/image_001322.txt (deflated 13%)\n", "updating: smartvision_dataset/detection/labels/image_001862.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001498.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_001537.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_002139.txt (deflated 32%)\n", "updating: smartvision_dataset/detection/labels/image_000908.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000683.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_002155.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_001296.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000628.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001725.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000209.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001149.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000645.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000071.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001956.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001568.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001401.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001789.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001883.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001893.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001703.txt (deflated 30%)\n", "updating: smartvision_dataset/detection/labels/image_002187.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_000275.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000304.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001216.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000974.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001103.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000219.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000339.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001043.txt (deflated 30%)\n", "updating: smartvision_dataset/detection/labels/image_000111.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001285.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001821.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_000056.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_002104.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_002138.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000640.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001102.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001180.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000243.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000012.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001949.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_000216.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_000207.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001419.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000253.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000249.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001576.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000217.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_002163.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000343.txt (deflated 14%)\n", "updating: smartvision_dataset/detection/labels/image_000464.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000531.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001640.txt (deflated 24%)\n", "updating: smartvision_dataset/detection/labels/image_001626.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001247.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_002050.txt (deflated 45%)\n", "updating: smartvision_dataset/detection/labels/image_001362.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_002183.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_002065.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000240.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000408.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001643.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_002190.txt (deflated 30%)\n", "updating: smartvision_dataset/detection/labels/image_000736.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000157.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001715.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000444.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000809.txt (deflated 16%)\n", "updating: smartvision_dataset/detection/labels/image_001481.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000226.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001157.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_001546.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000505.txt (deflated 31%)\n", "updating: smartvision_dataset/detection/labels/image_000695.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_001193.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001587.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_000680.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_002021.txt (deflated 28%)\n", "updating: smartvision_dataset/detection/labels/image_001352.txt (deflated 29%)\n", "updating: smartvision_dataset/detection/labels/image_001461.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_002002.txt (deflated 25%)\n", "updating: smartvision_dataset/detection/labels/image_000994.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000801.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001378.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000987.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_002019.txt (deflated 40%)\n", "updating: smartvision_dataset/detection/labels/image_001623.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000210.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000218.txt (deflated 36%)\n", "updating: smartvision_dataset/detection/labels/image_001366.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000654.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_000990.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001195.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001078.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000298.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001206.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001573.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001420.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000113.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000475.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000233.txt (deflated 49%)\n", "updating: smartvision_dataset/detection/labels/image_001698.txt (deflated 24%)\n", "updating: smartvision_dataset/detection/labels/image_000812.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_000165.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000130.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000329.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000283.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001748.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001860.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000198.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_000104.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000584.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_000047.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000471.txt (deflated 46%)\n", "updating: smartvision_dataset/detection/labels/image_001415.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000766.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000870.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000765.txt (deflated 35%)\n", "updating: smartvision_dataset/detection/labels/image_000244.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001494.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000921.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000100.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001152.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001931.txt (deflated 52%)\n", "updating: smartvision_dataset/detection/labels/image_001507.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_001081.txt (deflated 21%)\n", "updating: smartvision_dataset/detection/labels/image_001323.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000407.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001717.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000180.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000095.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_001830.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000495.txt (deflated 42%)\n", "updating: smartvision_dataset/detection/labels/image_001121.txt (deflated 5%)\n", "updating: smartvision_dataset/detection/labels/image_000569.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_000621.txt (deflated 44%)\n", "updating: smartvision_dataset/detection/labels/image_001550.txt (deflated 58%)\n", "updating: smartvision_dataset/detection/labels/image_000020.txt (deflated 47%)\n", "updating: smartvision_dataset/detection/labels/image_001709.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001924.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000421.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_000977.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000468.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000023.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_000756.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001241.txt (deflated 32%)\n", "updating: smartvision_dataset/detection/labels/image_002022.txt (deflated 37%)\n", "updating: smartvision_dataset/detection/labels/image_000734.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001068.txt (deflated 26%)\n", "updating: smartvision_dataset/detection/labels/image_001816.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000731.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001885.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001800.txt (deflated 50%)\n", "updating: smartvision_dataset/detection/labels/image_001927.txt (deflated 51%)\n", "updating: smartvision_dataset/detection/labels/image_001403.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_001905.txt (deflated 41%)\n", "updating: smartvision_dataset/detection/labels/image_000822.txt (deflated 53%)\n", "updating: smartvision_dataset/detection/labels/image_001137.txt (deflated 39%)\n", "updating: smartvision_dataset/detection/labels/image_000140.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000525.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_000649.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000752.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_001314.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_001012.txt (deflated 27%)\n", "updating: smartvision_dataset/detection/labels/image_000477.txt (deflated 11%)\n", "updating: smartvision_dataset/detection/labels/image_001542.txt (deflated 55%)\n", "updating: smartvision_dataset/detection/labels/image_000508.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000443.txt (deflated 38%)\n", "updating: smartvision_dataset/detection/labels/image_000069.txt (deflated 54%)\n", "updating: smartvision_dataset/detection/labels/image_000051.txt (deflated 56%)\n", "updating: smartvision_dataset/detection/labels/image_000427.txt (deflated 48%)\n", "updating: smartvision_dataset/detection/labels/image_001938.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_000995.txt (deflated 57%)\n", "updating: smartvision_dataset/detection/labels/image_001606.txt (deflated 43%)\n", "updating: smartvision_dataset/detection/data.yaml (deflated 38%)\n" ] } ], "source": [ "!zip -r smartvision_dataset.zip smartvision_dataset" ] }, { "cell_type": "code", "execution_count": 5, "id": "QtNw1rzYCH1d", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QtNw1rzYCH1d", "outputId": "b8886cfb-1623-42d3-d480-042c27a1c242" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " adding: runs/ (stored 0%)\n", " adding: runs/detect/ (stored 0%)\n", " adding: runs/detect/smartvision_yolov82/ (stored 0%)\n", " adding: runs/detect/smartvision_yolov82/labels.jpg (deflated 19%)\n", " adding: runs/detect/smartvision_yolov82/args.yaml (deflated 53%)\n", " adding: runs/detect/smartvision_yolov82/weights/ (stored 0%)\n", " adding: runs/detect/smartvision_yolov82/train_batch2.jpg (deflated 4%)\n", " adding: runs/detect/smartvision_yolov82/train_batch1.jpg (deflated 4%)\n", " adding: runs/detect/smartvision_yolov82/train_batch0.jpg (deflated 2%)\n", " adding: runs/detect/val/ (stored 0%)\n", " adding: runs/detect/val/val_batch0_pred.jpg (deflated 6%)\n", " adding: runs/detect/val/val_batch1_labels.jpg (deflated 7%)\n", " adding: runs/detect/val/confusion_matrix.png (deflated 14%)\n", " adding: runs/detect/val/BoxPR_curve.png (deflated 9%)\n", " adding: runs/detect/val/val_batch2_pred.jpg (deflated 17%)\n", " adding: runs/detect/val/val_batch0_labels.jpg (deflated 6%)\n", " adding: runs/detect/val/BoxP_curve.png (deflated 10%)\n", " adding: runs/detect/val/BoxR_curve.png (deflated 10%)\n", " adding: runs/detect/val/val_batch2_labels.jpg (deflated 9%)\n", " adding: runs/detect/val/confusion_matrix_normalized.png (deflated 12%)\n", " adding: runs/detect/val/val_batch1_pred.jpg (deflated 7%)\n", " adding: runs/detect/val/BoxF1_curve.png (deflated 10%)\n", " adding: runs/detect/predict/ (stored 0%)\n", " adding: runs/detect/predict/image_001642.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000711.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001576.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000871.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000748.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002013.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001072.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001846.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000608.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000357.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002015.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002128.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001338.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001980.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002090.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000197.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002141.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000215.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001725.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000957.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001817.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001105.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001085.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000503.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002209.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000603.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001929.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001141.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001065.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000293.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001541.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001249.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000061.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001208.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002074.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001795.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000773.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001519.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001155.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001906.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000593.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000920.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001569.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001213.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001573.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001978.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000646.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000637.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002110.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001866.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000762.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000009.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001945.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001624.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000859.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000968.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000355.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001214.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001009.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_000394.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000389.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001604.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001184.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000347.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000386.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001391.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001147.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000509.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000683.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000496.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000798.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001262.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001116.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000631.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001058.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000003.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000169.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001910.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002054.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001371.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000804.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001986.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000651.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000830.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001701.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001871.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000478.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001645.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001025.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000674.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000801.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001828.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000307.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_000949.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000959.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000613.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000292.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000532.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001869.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001686.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001667.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000927.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001566.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000000.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001279.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000230.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000896.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002048.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001937.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001079.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000245.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000940.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_001290.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002003.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000835.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000845.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001486.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000777.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000691.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000218.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000286.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000019.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001776.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000298.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000315.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001616.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001349.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000719.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001761.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000895.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000456.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000176.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000655.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001278.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001356.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002150.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002194.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000506.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000150.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000999.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001583.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000093.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001138.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000291.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001054.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001959.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001955.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000025.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001134.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001331.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001982.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001342.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000664.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001430.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002135.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001296.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000515.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001905.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000621.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000249.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001737.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001128.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000909.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001232.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001923.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001900.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000886.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000629.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000107.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000158.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001317.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000462.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001780.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000072.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000488.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001274.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001460.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000055.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001865.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001534.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001765.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001674.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000063.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001515.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000111.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000630.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001717.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000807.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000343.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000354.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000841.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000739.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001298.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001076.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000141.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000012.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001435.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001210.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002067.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001198.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001455.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000296.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001257.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000713.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000185.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000672.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002085.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001286.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000740.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001853.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001047.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001463.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000870.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000381.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000635.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001950.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001523.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001944.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000106.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000247.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000224.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002136.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001860.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001267.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000774.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_002166.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000251.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001718.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002129.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001527.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001222.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000126.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001403.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000910.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001724.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001889.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000614.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002101.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000639.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001774.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_002088.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001143.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000024.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000648.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000662.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002100.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000988.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001368.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000868.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000338.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001872.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000124.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001883.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000239.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001069.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002121.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000501.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000842.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000884.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000453.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000571.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001549.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000543.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000166.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001858.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001283.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001165.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000360.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001670.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000745.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000457.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001708.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001084.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001035.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001300.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001282.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001130.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001461.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000932.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001705.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001360.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000926.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001757.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001123.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000650.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001187.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000761.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001954.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001297.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000399.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000183.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000956.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000971.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000027.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001169.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001062.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001402.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001011.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001659.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000074.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000133.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002017.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001779.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000182.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001102.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001746.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001636.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000869.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001251.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000824.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001696.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000045.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000094.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002089.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000377.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001229.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001193.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000423.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_001965.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001498.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000333.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001070.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000060.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001336.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001237.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000494.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002144.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002076.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001398.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001739.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000725.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001803.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001287.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001321.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001836.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001240.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000788.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000652.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000643.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001158.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002035.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000473.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000101.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000531.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001327.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000619.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000578.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001075.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002086.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000521.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001584.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002152.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000410.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001540.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001647.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000493.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002187.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000523.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002057.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000257.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001129.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000280.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001966.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001894.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000935.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_001985.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000979.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000260.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000590.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001471.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000356.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001984.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001827.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000513.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000427.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001677.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002081.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001045.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001269.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001150.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002189.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000445.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000685.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000747.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001936.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002112.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000518.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001082.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001216.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000544.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000752.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001553.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001244.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000666.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002012.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000435.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000160.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001043.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001152.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001862.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000465.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001629.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001445.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_001660.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002179.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000098.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001412.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002032.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001308.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001236.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000718.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002099.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000499.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001234.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000703.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000152.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000889.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002083.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001437.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000766.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000208.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001485.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001031.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000128.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001092.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001346.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001156.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001427.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001913.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001107.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000892.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000484.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001389.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000091.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001172.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001880.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000306.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_000514.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002114.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000434.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000893.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000044.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001292.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001595.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001528.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001028.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001341.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001723.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000342.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001260.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000311.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000814.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001345.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000540.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002038.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001087.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000694.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002151.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001046.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000534.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001377.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001771.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002007.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000154.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001952.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000569.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000771.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002113.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001053.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000634.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001098.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001235.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000856.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000243.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001972.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001925.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000780.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000928.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001029.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001590.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001922.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002163.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001217.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001005.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001378.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001537.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001245.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000329.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000077.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001715.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002126.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001535.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001017.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001264.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000273.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000398.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000782.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_002047.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001892.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000967.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000980.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000382.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001196.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001531.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000214.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001974.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001221.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001288.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001927.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000001.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000806.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001751.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000888.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000362.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001436.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000084.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000839.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000728.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000029.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001622.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000549.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000951.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001547.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000978.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001509.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000827.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000625.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001750.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001538.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000400.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001431.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000813.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000963.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000638.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000232.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001024.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000919.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000359.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001077.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001864.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002053.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001261.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000525.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002120.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001799.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001976.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001036.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002193.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000219.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000816.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001206.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001063.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001589.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000057.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001223.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001276.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000117.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001103.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002142.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002138.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001167.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001136.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000125.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001418.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001874.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000820.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001018.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000452.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000287.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000557.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000724.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000372.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001713.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001399.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001796.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002184.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001475.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000030.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001877.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000742.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000519.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000542.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000497.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001057.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001973.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000109.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_001709.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001325.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001313.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000304.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002087.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001477.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001066.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001375.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000455.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001935.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001768.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001182.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000048.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002084.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000592.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000717.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000851.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001174.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000181.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000516.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001476.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002140.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001562.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001468.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001781.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002063.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000476.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001643.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000159.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001241.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000620.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000108.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_001467.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002153.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000881.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000793.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001991.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001743.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000309.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000948.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002161.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000526.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_002109.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002095.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002046.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000405.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001004.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001081.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001246.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001448.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000480.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002115.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000805.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001145.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000970.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002077.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000168.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002065.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000772.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000313.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001914.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001495.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001933.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000611.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001328.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000865.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001669.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001772.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001766.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001638.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000676.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001572.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001397.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000537.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000085.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001536.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000140.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001127.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000225.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001979.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001464.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000486.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001121.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001756.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000708.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000924.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001748.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001020.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000113.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000945.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000153.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001095.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001934.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001634.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001912.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001688.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001626.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001621.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000529.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001571.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001526.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000803.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001056.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001311.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000485.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000950.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000875.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000746.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000026.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002051.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002080.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000588.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001706.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000902.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000034.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000162.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000290.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000601.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001682.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000447.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001597.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000240.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001561.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000671.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001815.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001909.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000649.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001545.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001778.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001289.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000326.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000114.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001915.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000698.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001598.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001019.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000317.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000786.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000942.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_000446.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000349.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000574.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001100.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000749.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001885.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001316.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002021.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001609.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002049.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001591.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002107.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001006.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001015.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001110.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001592.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002026.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001164.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000262.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001805.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000013.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001720.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000068.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000736.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001067.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001227.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001879.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000217.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000444.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001833.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000015.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001963.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001808.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001382.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000174.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001939.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001996.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001452.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000020.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001494.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001329.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000222.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001785.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001568.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000670.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001614.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000763.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000090.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001146.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000962.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001654.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000082.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000058.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001804.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000144.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002070.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000730.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000882.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000209.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001697.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001685.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001492.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001168.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001419.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001661.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002199.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001201.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_000617.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002169.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000430.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000155.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000690.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000137.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000220.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001762.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001333.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000041.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000693.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001272.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001983.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000344.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001474.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000380.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002060.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000838.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001178.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001083.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000781.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001563.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001521.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001770.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002170.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000616.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000431.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000393.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000067.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001396.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002052.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002204.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002018.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000872.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000947.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001837.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000843.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001848.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000769.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001126.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000741.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000083.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000695.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000589.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001215.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000016.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001652.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001620.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001049.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002175.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001440.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001977.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000834.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002059.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000050.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001782.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001383.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001942.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000297.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002157.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001921.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000684.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001362.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000028.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001106.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000564.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000078.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001655.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001735.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001859.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001224.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001956.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001520.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000587.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002183.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001002.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001699.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000522.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001139.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001489.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001447.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001332.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000439.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000139.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000022.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000586.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002134.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002031.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000376.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001425.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000914.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002091.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000905.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000192.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001845.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000023.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000891.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000982.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001064.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000517.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001060.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000731.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000056.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001401.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000127.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002106.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001394.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002158.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001940.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002075.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000669.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000421.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001443.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001850.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000164.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000328.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001462.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_002154.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001503.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000341.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001416.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002200.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000272.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001700.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002042.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000829.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000946.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001385.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001648.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001951.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000759.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002145.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000953.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000369.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002133.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000550.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000039.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000119.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001722.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001556.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001710.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001280.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000612.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001740.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001093.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001323.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001033.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000415.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000699.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001326.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001558.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001395.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001374.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002025.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001340.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000475.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001016.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001090.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001363.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000267.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000121.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001695.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000388.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000861.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000118.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002205.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000874.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000384.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001038.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000712.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000211.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001373.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000112.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001271.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000375.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000373.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000371.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001898.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000716.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000913.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002196.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000481.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000878.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000252.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001694.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001631.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000866.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001355.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000596.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001581.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000035.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001968.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001344.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002177.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000784.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001843.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000314.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001358.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001702.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000483.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000624.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000732.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002203.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000367.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002045.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001649.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001672.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000645.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001663.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000627.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001078.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000903.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000661.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000528.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000867.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000352.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001731.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000442.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000701.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000822.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001830.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001727.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000351.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001505.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001580.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000931.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001281.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000263.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001068.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001612.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001975.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002041.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000641.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001275.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000583.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001557.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000837.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001228.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001539.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000005.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001981.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000990.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000051.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000675.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000032.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000374.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000921.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001841.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001190.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002069.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000551.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000840.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001472.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001125.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000477.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000660.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000539.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002064.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000010.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001657.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001496.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000507.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000335.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000092.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001601.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000250.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001988.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001424.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000274.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000760.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001086.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000387.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000710.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000080.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001788.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001802.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000186.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001151.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001839.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001856.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000938.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_001238.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000105.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000103.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000558.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002027.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001202.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_000179.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000584.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000633.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001831.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000776.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_002149.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000605.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001309.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000855.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000193.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000008.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000673.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000785.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002050.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001411.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001379.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000401.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002092.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000379.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001352.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000811.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000180.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002125.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001511.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002061.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000756.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000269.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000277.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002098.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000325.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001747.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001753.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000767.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001690.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001175.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000190.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001873.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000066.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001199.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_001361.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001044.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001132.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001729.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001393.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002171.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001890.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001619.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000429.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000097.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001607.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001381.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001844.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001577.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001502.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000944.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002123.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001295.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000567.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000901.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001552.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001703.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000302.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001048.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002172.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000833.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001736.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000599.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000464.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001832.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001406.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000657.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002156.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000323.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000757.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000151.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001930.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000131.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001482.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001588.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001226.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002056.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000043.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002002.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001041.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001926.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001868.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000229.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002176.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001971.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000248.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001824.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001191.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000687.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000688.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000541.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000200.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000156.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000451.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001651.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001119.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000233.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002178.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000468.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000850.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001037.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000751.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000862.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001347.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001826.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000815.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_001042.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001630.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000904.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002033.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000095.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000799.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000533.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001039.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000985.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000037.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001559.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002122.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000402.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000308.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001160.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000408.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002079.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001758.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000755.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000436.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000212.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001305.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001919.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002062.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001270.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001997.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000796.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002208.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000653.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000361.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001337.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000284.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001330.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001181.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001784.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001120.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000370.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000954.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002078.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001212.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001438.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001197.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002000.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000582.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000321.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000568.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000104.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000448.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001546.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001680.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001319.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000110.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000320.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001099.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000079.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000327.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001769.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000707.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001285.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002201.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001564.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000294.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001050.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001071.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001487.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000418.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001602.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002009.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000977.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000952.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000819.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000758.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000353.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001104.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000566.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000906.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001501.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000122.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001122.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001764.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000743.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000912.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001026.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001314.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000577.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000213.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000563.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002071.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000993.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001109.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001728.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000795.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000331.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000460.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001752.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000130.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000147.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001579.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001392.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001734.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002068.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002066.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002160.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000336.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000505.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001506.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000242.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000198.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001613.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001646.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001689.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001256.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001886.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001801.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000014.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000581.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001807.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002103.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000964.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001967.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002006.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001442.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001565.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000424.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001263.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000177.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000764.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000466.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002181.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000007.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000210.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002001.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001504.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000640.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002005.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001180.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000936.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_000259.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001231.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001080.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001491.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001989.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001516.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000562.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001507.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001153.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000622.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001800.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000227.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001266.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001745.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001684.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002198.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001738.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000033.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000199.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001343.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000723.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001818.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000188.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000450.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001387.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001384.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001789.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000591.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000358.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001775.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000734.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001357.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000642.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001255.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001294.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001364.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000419.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000714.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000237.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001875.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001650.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001439.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001192.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000823.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001148.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000828.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000536.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000238.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001454.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001627.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000658.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000681.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001608.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000679.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001797.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002130.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001786.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001247.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001478.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001533.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000572.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001252.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002010.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001687.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001367.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000170.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000955.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001615.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002148.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000702.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000346.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000831.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001301.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001353.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001413.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001777.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001664.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001707.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000498.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000305.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000579.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000668.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001653.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001842.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001204.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002188.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002207.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001633.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001790.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001692.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000322.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001258.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000413.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000576.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000420.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001798.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000937.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_001811.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000894.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000715.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002024.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000062.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000512.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000791.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001010.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000264.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002127.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000383.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001806.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000397.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001760.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000654.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000744.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002022.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002143.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000508.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001744.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001185.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001673.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000552.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000721.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000312.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000206.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000417.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001683.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001473.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001162.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000255.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001726.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000283.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001543.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001299.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000915.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001112.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001481.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000330.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001171.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001822.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001907.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000602.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000973.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000459.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001372.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002117.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001882.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001030.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001635.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000378.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000986.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002072.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000556.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000877.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000279.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000510.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000363.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000966.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000809.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000070.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000554.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001441.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000792.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001586.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001135.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001400.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001159.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001469.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000266.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002192.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002040.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001719.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001931.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000997.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000925.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002159.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000511.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000812.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000426.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000680.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000463.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000205.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001259.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001366.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001812.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000880.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001891.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_002190.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000606.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001712.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002029.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001594.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001273.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002191.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001947.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000490.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002023.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002173.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000006.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000011.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000407.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001637.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001544.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001470.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000432.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000145.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000610.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002097.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000923.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000271.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000441.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000364.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002028.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001220.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000575.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000667.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000520.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000704.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001920.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001508.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000981.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002105.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001932.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001315.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000403.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001908.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000998.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001960.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002111.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002036.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001825.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000059.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000234.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001676.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000123.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001426.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001681.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001365.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001585.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000929.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001088.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001176.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000890.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001835.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000031.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000350.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000161.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001820.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001887.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000395.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001115.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000849.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001962.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002186.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001902.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001632.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001421.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000810.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001901.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001821.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000991.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000750.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002102.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001524.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000545.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001404.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000201.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000167.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001994.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001140.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000765.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001946.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001428.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001250.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001704.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000319.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001596.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000989.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002030.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001303.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000907.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000366.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000081.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000974.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000797.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002108.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000191.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000879.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001759.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000738.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001518.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002162.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000623.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002020.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000310.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000994.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001867.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000524.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001195.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001990.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001312.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000258.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001575.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000474.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000228.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001434.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000802.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001034.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001480.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001714.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000768.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001334.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001000.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001423.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001500.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001410.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000116.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000735.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001514.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000972.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001854.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000202.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001610.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000787.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001183.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000689.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000099.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000908.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001101.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001388.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001943.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000832.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001881.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001118.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000132.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001348.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002202.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000700.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001293.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000454.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001218.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000129.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001032.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000301.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001335.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000053.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000073.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002124.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001999.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000194.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000087.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001911.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000897.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000726.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001205.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000052.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000089.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000546.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000256.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001698.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000917.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000502.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000656.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000898.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000632.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000663.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000253.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001754.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001265.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001656.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000047.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000021.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001582.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000173.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000686.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000470.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001449.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001578.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000705.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000600.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001903.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000281.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001243.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000696.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001639.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000852.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000337.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000789.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001555.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000636.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001773.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000275.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000876.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002174.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001003.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001970.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000216.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000922.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002014.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001587.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000530.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002185.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001662.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001665.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000075.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000163.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000836.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000609.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001013.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001322.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001767.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000885.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001277.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000939.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_001961.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001755.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001532.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001186.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000157.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001995.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001284.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_002195.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000196.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000149.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001453.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001928.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001096.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000873.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000391.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000175.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001074.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001451.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001499.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000071.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000853.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001291.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000479.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001916.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002165.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000597.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001339.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002137.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001179.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000983.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000076.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001350.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000916.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001207.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001863.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001787.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000204.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001091.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000899.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000548.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000783.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000644.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001783.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001248.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001059.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000324.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001405.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000054.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001429.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000647.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001640.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001829.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002008.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002034.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001791.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001567.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001415.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000847.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001918.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000489.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001446.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000428.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000469.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001351.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000808.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000100.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002132.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001603.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000416.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001878.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001730.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001668.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001813.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000040.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002131.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001307.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000244.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000818.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001268.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001161.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000134.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000817.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001007.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000607.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000727.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001370.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000270.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001819.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000598.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001458.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000065.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001852.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000547.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001117.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001149.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000794.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000223.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001422.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000883.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001359.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002167.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001513.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000553.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000064.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000626.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001369.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000821.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001320.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001658.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000618.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000236.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001450.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000984.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000433.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002016.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002104.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001574.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000207.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001014.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_000854.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000778.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001958.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001433.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000385.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001466.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001993.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000987.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001897.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001310.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000340.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001809.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000682.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001253.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001225.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001022.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001073.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000770.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000560.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001239.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001948.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001917.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000733.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000844.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002164.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001493.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001666.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001884.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001055.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001157.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002082.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001742.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001548.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001512.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001605.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001899.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000729.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000102.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000943.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001711.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001194.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001876.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001380.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001233.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001133.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001324.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002093.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000800.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001953.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001987.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000002.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001721.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001154.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000339.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000570.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001628.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000585.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000268.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001895.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000933.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001625.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001675.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000226.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001052.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000825.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002118.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000790.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001888.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001304.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001097.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001870.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000038.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000857.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000437.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000148.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000677.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002147.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001600.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001386.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001457.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000975.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000203.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001242.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000282.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000409.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000449.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001570.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001550.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001834.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001529.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001318.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000887.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001021.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000900.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001938.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000692.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001998.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000049.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000261.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001969.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002011.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001691.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001483.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000492.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001623.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001390.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000969.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001851.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002182.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002058.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000471.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000042.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001302.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000265.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001163.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001551.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002044.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001111.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001484.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000918.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000300.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000965.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001094.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001051.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001479.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001949.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_002139.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000235.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001741.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000709.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001023.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000958.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000934.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000289.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001173.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001376.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001924.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001189.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002180.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002019.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001814.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000189.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001904.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000288.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002073.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000345.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002039.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000115.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000004.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001061.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001840.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002197.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000538.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001012.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001177.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001792.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001124.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000390.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000461.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001749.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000120.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001209.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001823.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000960.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000737.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001488.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000018.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002155.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001679.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001896.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000411.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001166.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000404.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000318.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000425.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000221.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000604.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000136.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001992.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000961.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001137.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001861.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001611.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002055.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002206.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001838.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000826.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001144.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000846.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000930.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000392.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000088.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000142.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000527.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001027.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000565.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001542.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000482.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001254.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002116.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000187.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001040.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001230.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001849.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000561.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000254.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000241.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000495.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000779.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000491.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001417.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002094.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000316.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000458.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000172.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001211.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000594.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000096.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001432.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001763.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000848.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000754.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000697.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000595.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001114.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000365.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001893.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_000334.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001857.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000017.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000299.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000438.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000659.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000722.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001444.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001517.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000467.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000440.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000995.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000278.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000143.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000858.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000487.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001420.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000628.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001793.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000069.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001456.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001170.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001816.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001200.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_000135.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001560.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001525.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001593.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001847.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000086.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000580.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000911.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000285.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002146.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001617.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001465.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000992.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001644.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000195.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000178.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000422.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002168.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001306.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001219.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001554.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000276.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001733.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002096.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000231.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000406.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000348.jpg (deflated 6%)\n", " adding: runs/detect/predict/image_000996.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001716.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000500.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001606.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000863.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001354.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001522.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000246.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000146.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000678.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001497.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_002043.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001490.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000165.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_002004.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000295.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000555.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001131.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001964.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000753.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000941.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_001459.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001409.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001203.jpg (deflated 5%)\n", " adding: runs/detect/predict/image_001001.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000504.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000412.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000775.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000615.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000184.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000665.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002037.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000559.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001008.jpg (deflated 4%)\n", " adding: runs/detect/predict/image_001108.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001618.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001957.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000332.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000573.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000976.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001407.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001530.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001599.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001810.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000368.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001678.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000036.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001732.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000472.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001671.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000171.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001113.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001408.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000535.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001794.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000396.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000706.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000303.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001188.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000046.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000443.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001641.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_000138.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_000414.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_001510.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001142.jpg (deflated 3%)\n", " adding: runs/detect/predict/image_001941.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_000720.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000864.jpg (deflated 1%)\n", " adding: runs/detect/predict/image_002119.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001693.jpg (deflated 2%)\n", " adding: runs/detect/predict/image_001855.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001414.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_001089.jpg (deflated 0%)\n", " adding: runs/detect/predict/image_000860.jpg (deflated 2%)\n", " adding: runs/detect/smartvision_yolov83/ (stored 0%)\n", " adding: runs/detect/smartvision_yolov83/val_batch0_pred.jpg (deflated 7%)\n", " adding: runs/detect/smartvision_yolov83/val_batch1_labels.jpg (deflated 9%)\n", " adding: runs/detect/smartvision_yolov83/confusion_matrix.png (deflated 14%)\n", " adding: runs/detect/smartvision_yolov83/labels.jpg (deflated 19%)\n", " adding: runs/detect/smartvision_yolov83/BoxPR_curve.png (deflated 9%)\n", " adding: runs/detect/smartvision_yolov83/results.png (deflated 7%)\n", " adding: runs/detect/smartvision_yolov83/val_batch2_pred.jpg (deflated 7%)\n", " adding: runs/detect/smartvision_yolov83/val_batch0_labels.jpg (deflated 7%)\n", " adding: runs/detect/smartvision_yolov83/args.yaml (deflated 53%)\n", " adding: runs/detect/smartvision_yolov83/BoxP_curve.png (deflated 10%)\n", " adding: runs/detect/smartvision_yolov83/BoxR_curve.png (deflated 10%)\n", " adding: runs/detect/smartvision_yolov83/val_batch2_labels.jpg (deflated 7%)\n", " adding: runs/detect/smartvision_yolov83/confusion_matrix_normalized.png (deflated 12%)\n", " adding: runs/detect/smartvision_yolov83/weights/ (stored 0%)\n", " adding: runs/detect/smartvision_yolov83/weights/last.pt (deflated 9%)\n", " adding: runs/detect/smartvision_yolov83/weights/best.pt (deflated 9%)\n", " adding: runs/detect/smartvision_yolov83/results.csv (deflated 57%)\n", " adding: runs/detect/smartvision_yolov83/train_batch2.jpg (deflated 8%)\n", " adding: runs/detect/smartvision_yolov83/train_batch1.jpg (deflated 11%)\n", " adding: runs/detect/smartvision_yolov83/train_batch0.jpg (deflated 10%)\n", " adding: runs/detect/smartvision_yolov83/val_batch1_pred.jpg (deflated 17%)\n", " adding: runs/detect/smartvision_yolov83/BoxF1_curve.png (deflated 10%)\n", " adding: runs/detect/smartvision_yolov8/ (stored 0%)\n", " adding: runs/detect/smartvision_yolov8/labels.jpg (deflated 19%)\n", " adding: runs/detect/smartvision_yolov8/args.yaml (deflated 53%)\n", " adding: runs/detect/smartvision_yolov8/weights/ (stored 0%)\n", " adding: runs/detect/smartvision_yolov8/train_batch2.jpg (deflated 3%)\n", " adding: runs/detect/smartvision_yolov8/train_batch1.jpg (deflated 3%)\n", " adding: runs/detect/smartvision_yolov8/train_batch0.jpg (deflated 4%)\n" ] } ], "source": [ "!zip -r smartvision_runs.zip runs" ] } ], "metadata": { "accelerator": "TPU", "colab": { "gpuType": "V5E1", "provenance": [] }, "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.0" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "07a41735942b4874b1e7bff31b83f1e0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "15829b9700c349faa4357a53f0c8ff69": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "18af9e645acb47aabcb76c946fb888eb": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": "20px" } }, "18c700bfd12b47d8beb50d96d91298be": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "1bcf3e94f36b4141bf8faf1bf3504bcf": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "44595592c7644cedbccf51cef0c28380": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "468b72f09f78420ca743c8082ba44cea": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_9f6868d254474d3a8aaf555ba68335b9", "placeholder": "​", "style": "IPY_MODEL_aad289a5723647b080452b4709e4d106", "value": " 2.10k/? [00:00<00:00, 180kB/s]" } }, "4bf4421b698649898418baf1a75c1fe9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_9027ff7547ad4145bddb0422dff717b1", "placeholder": "​", "style": "IPY_MODEL_18c700bfd12b47d8beb50d96d91298be", "value": " 40/40 [00:00<00:00, 1996.34it/s]" } }, "57bacff4917c409cbf2383a82d932143": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_fc0dd30888034a5bbcb688cc933589bf", "IPY_MODEL_6be3a48252e1474eb0a518f28368e952", "IPY_MODEL_4bf4421b698649898418baf1a75c1fe9" ], "layout": "IPY_MODEL_7e0f8f1830954e80afa4b4ae6c6cf4f5" } }, "68e535db3c974d0785750d1c54dc26b0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "6be3a48252e1474eb0a518f28368e952": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_a19e39c93dd942abaf8330d78a747f8e", "max": 40, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_e60ef01a3fdd4e598c310a68aa05d8f4", "value": 40 } }, "7e0f8f1830954e80afa4b4ae6c6cf4f5": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "8d55cec8dd7b4f0e82947de951e2089a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "8e6ecd6d126e48d98c79f1c3ddacca7f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_1bcf3e94f36b4141bf8faf1bf3504bcf", "max": 58, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_07a41735942b4874b1e7bff31b83f1e0", "value": 58 } }, "9027ff7547ad4145bddb0422dff717b1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "995f2f9dcddd4a0780af48863baecf6b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_c1172a5798d1462c885f51b2ec93ee0e", "placeholder": "​", "style": "IPY_MODEL_44595592c7644cedbccf51cef0c28380", "value": "README.md: 100%" } }, "9ad3d91590d4476bbbfc5906d6d53886": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_ef8a462adb1f48738c15f751d6ce8c04", "IPY_MODEL_d9ea75c165424ed1a778528ddebc4b6c", "IPY_MODEL_468b72f09f78420ca743c8082ba44cea" ], "layout": "IPY_MODEL_d062883e96524e698727a84de0872b5d" } }, "9f6868d254474d3a8aaf555ba68335b9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "a0302ec4177c48a0b9ae0fa9a943d7a6": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "a19e39c93dd942abaf8330d78a747f8e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "aad289a5723647b080452b4709e4d106": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "ae8c7182f5354de9994c6dabb8833ac5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "c1172a5798d1462c885f51b2ec93ee0e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "c25a23066ef14c76bd541dd0fdbd1985": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "d062883e96524e698727a84de0872b5d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "d9ea75c165424ed1a778528ddebc4b6c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_18af9e645acb47aabcb76c946fb888eb", "max": 1, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_ae8c7182f5354de9994c6dabb8833ac5", "value": 1 } }, "da4a5f14edc44558aa14a47483fa39da": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_dfaee4f75b6b45b8b27cabd834072fc1", "placeholder": "​", "style": "IPY_MODEL_eb54b29b0cee4cd38bc3a9127bbddd13", "value": " 58.0/58.0 [00:00<00:00, 1.70kB/s]" } }, "dfaee4f75b6b45b8b27cabd834072fc1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e60ef01a3fdd4e598c310a68aa05d8f4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "ea4ca1e8ff404a5582e1e3539156e241": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_995f2f9dcddd4a0780af48863baecf6b", "IPY_MODEL_8e6ecd6d126e48d98c79f1c3ddacca7f", "IPY_MODEL_da4a5f14edc44558aa14a47483fa39da" ], "layout": "IPY_MODEL_a0302ec4177c48a0b9ae0fa9a943d7a6" } }, "eb54b29b0cee4cd38bc3a9127bbddd13": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "ef8a462adb1f48738c15f751d6ce8c04": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_68e535db3c974d0785750d1c54dc26b0", "placeholder": "​", "style": "IPY_MODEL_15829b9700c349faa4357a53f0c8ff69", "value": "dataset_infos.json: " } }, "fc0dd30888034a5bbcb688cc933589bf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_c25a23066ef14c76bd541dd0fdbd1985", "placeholder": "​", "style": "IPY_MODEL_8d55cec8dd7b4f0e82947de951e2089a", "value": "Resolving data files: 100%" } } } } }, "nbformat": 4, "nbformat_minor": 5 }