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b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..c5eb436103c289d83c4d6dd6aa84a2a3c672b05e --- /dev/null +++ b/.gitignore @@ -0,0 +1,218 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[codz] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py.cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +# Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +# poetry.lock +# poetry.toml + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +# pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python. +# https://pdm-project.org/en/latest/usage/project/#working-with-version-control +# pdm.lock +# pdm.toml +.pdm-python +.pdm-build/ + +# pixi +# Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control. +# pixi.lock +# Pixi creates a virtual environment in the .pixi directory, just like venv module creates one +# in the .venv directory. It is recommended not to include this directory in version control. +.pixi + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# Redis +*.rdb +*.aof +*.pid + +# RabbitMQ +mnesia/ +rabbitmq/ +rabbitmq-data/ + +# ActiveMQ +activemq-data/ + +# SageMath parsed files +*.sage.py + +# Environments +.env +.envrc +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +# .idea/ + +# Abstra +# Abstra is an AI-powered process automation framework. +# Ignore directories containing user credentials, local state, and settings. +# Learn more at https://abstra.io/docs +.abstra/ + +# Visual Studio Code +# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore +# that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore +# and can be added to the global gitignore or merged into this file. However, if you prefer, +# you could uncomment the following to ignore the entire vscode folder +# .vscode/ +# Temporary file for partial code execution +tempCodeRunnerFile.py + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc + +# Marimo +marimo/_static/ +marimo/_lsp/ +__marimo__/ + +# Streamlit +.streamlit/secrets.toml diff --git a/README.md b/README.md index 32897cd3e640101ba184f8c4ccd896981de3804a..a9d56b0f81acc4989431e28080035991882296a5 100644 --- a/README.md +++ b/README.md @@ -1,3 +1 @@ ---- -license: mit ---- +# Road-Damage-Object-Detection \ No newline at end of file diff --git a/Road_Damage.ipynb b/Road_Damage.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..82c03652031ccf7e8323ee692a298ad85d8b78fd --- /dev/null +++ b/Road_Damage.ipynb @@ -0,0 +1,2191 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "**Read DataSet**" + ], + "metadata": { + "id": "a9Few82ghBaB" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jaATqbTjmCT8", + "outputId": "cb48b6fc-5a6b-4b5a-e22e-4468d05cdf2f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting roboflow\n", + " Downloading roboflow-1.3.13-py3-none-any.whl.metadata (11 kB)\n", + "Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from roboflow) (2026.6.17)\n", + "Requirement already satisfied: idna>=3.7 in /usr/local/lib/python3.12/dist-packages (from roboflow) (3.18)\n", + "Requirement already satisfied: cycler in /usr/local/lib/python3.12/dist-packages (from roboflow) (0.12.1)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in 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"text": [ + "\n", + "Extracting Dataset Version Zip to road-damage-1 in yolov8:: 100%|██████████| 13518/13518 [00:02<00:00, 5780.12it/s]\n" + ] + } + ], + "source": [ + "!pip install roboflow\n", + "\n", + "from roboflow import Roboflow\n", + "rf = Roboflow(api_key=\"7KCopT5IpJrC1a962tSL\")\n", + "project = rf.workspace(\"mobicas-workspace\").project(\"road-damage-l1ju7-dzsab\")\n", + "version = project.version(1)\n", + "dataset = version.download(\"yolov8\")\n", + "" + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Library**" + ], + "metadata": { + "id": "RQRb-MbfhKrl" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install ultralytics -q" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eOfTXMqjmL6a", + "outputId": "9e63a0d0-b558-458c-8efe-6216fbd81e99" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K 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'/root/.config/Ultralytics/settings.json'\n", + "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**YOLO8**" + ], + "metadata": { + "id": "3dG0aWRjhPAg" + } + }, + { + "cell_type": "code", + "source": [ + "model_v8 = YOLO(\"yolov8n.pt\")\n", + "model_v8.train(\n", + " data=data_path,\n", + " epochs=50,\n", + " imgsz=640,\n", + " batch=16,\n", + " patience=10,\n", + " name=\"yolov8_road\"\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pgbEcSsHpLYs", + "outputId": "dd8e67ee-9c95-4261-a574-5eb96ae634b9" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Ultralytics 8.4.92 🚀 Python-3.12.13 torch-2.11.0+cu128 CUDA:0 (Tesla T4, 14913MiB)\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, cls_pw=0.0, cls_remap=True, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/content/road-damage-1/data.yaml, degrees=0.0, deterministic=True, device=, dfl=1.5, dis=6.0, distill_model=None, dnn=False, dropout=0.0, dynamic=False, embed=None, end2end=None, epochs=50, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, 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=yolov8_road-2, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=10, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, quantize=None, rect=False, resume=False, retina_masks=False, rle=1.0, save=True, save_conf=False, save_crop=False, save_dir=/content/runs/detect/yolov8_road-2, 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=tracktrack.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=7\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 752677 ultralytics.nn.modules.head.Detect [7, 16, None, [64, 128, 256]] \n", + "Model summary: 130 layers, 3,012,213 parameters, 3,012,197 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[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1880.9±665.0 MB/s, size: 50.1 KB)\n", + "\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/road-damage-1/train/labels.cache... 4930 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 4930/4930 1.7Git/s 0.0s\n", + "WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 82, len(boxes) = 15024. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\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 ✅ (ping: 0.0±0.0 ms, read: 610.5±30.4 MB/s, size: 88.7 KB)\n", + "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /content/road-damage-1/valid/labels.cache... 1146 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 1146/1146 60.8Mit/s 0.0s\n", + "WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 33, len(boxes) = 3588. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\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=0.000909, 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/yolov8_road-2/labels.jpg... \n", + "Image sizes 640 train, 640 val\n", + "Using 2 dataloader workers\n", + "Logging results to \u001b[1m/content/runs/detect/yolov8_road-2\u001b[0m\n", + "Starting training for 50 epochs...\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 1/50 2.55G 1.94 3.255 1.893 9 640: 100% ━━━━━━━━━━━━ 309/309 3.2it/s 1:37\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.4it/s 10.6s\n", + " all 1146 3588 0.309 0.178 0.116 0.057\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 2/50 2.55G 1.896 2.742 1.859 16 640: 100% ━━━━━━━━━━━━ 309/309 3.7it/s 1:22\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.4it/s 10.5s\n", + " all 1146 3588 0.334 0.182 0.13 0.061\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 3/50 2.55G 1.882 2.622 1.854 4 640: 100% ━━━━━━━━━━━━ 309/309 3.8it/s 1:22\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.8it/s 9.6s\n", + " all 1146 3588 0.292 0.161 0.125 0.0577\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 4/50 2.55G 1.856 2.525 1.841 13 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.4it/s 10.7s\n", + " all 1146 3588 0.143 0.216 0.125 0.0604\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 5/50 2.55G 1.813 2.424 1.805 6 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.8s\n", + " all 1146 3588 0.266 0.195 0.175 0.0915\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 6/50 2.55G 1.811 2.374 1.798 7 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.1it/s 8.8s\n", + " all 1146 3588 0.294 0.22 0.187 0.103\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 7/50 2.55G 1.767 2.336 1.77 8 640: 100% ━━━━━━━━━━━━ 309/309 3.8it/s 1:21\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.8it/s 9.5s\n", + " all 1146 3588 0.206 0.277 0.201 0.103\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 8/50 2.55G 1.757 2.279 1.753 19 640: 100% ━━━━━━━━━━━━ 309/309 3.8it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.6it/s 10.0s\n", + " all 1146 3588 0.254 0.233 0.183 0.0896\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 9/50 2.55G 1.743 2.248 1.743 11 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.3it/s 10.9s\n", + " all 1146 3588 0.338 0.242 0.229 0.124\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 10/50 2.55G 1.719 2.172 1.713 15 640: 100% ━━━━━━━━━━━━ 309/309 3.8it/s 1:21\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.5it/s 10.1s\n", + " all 1146 3588 0.279 0.265 0.215 0.116\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 11/50 2.55G 1.711 2.174 1.717 8 640: 100% ━━━━━━━━━━━━ 309/309 3.7it/s 1:24\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.8s\n", + " all 1146 3588 0.464 0.261 0.231 0.125\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 12/50 2.55G 1.675 2.121 1.692 4 640: 100% ━━━━━━━━━━━━ 309/309 3.8it/s 1:21\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.6s\n", + " all 1146 3588 0.334 0.244 0.229 0.127\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 13/50 2.55G 1.688 2.134 1.701 9 640: 100% ━━━━━━━━━━━━ 309/309 3.8it/s 1:21\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.3s\n", + " all 1146 3588 0.314 0.288 0.265 0.146\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 14/50 2.55G 1.68 2.101 1.699 8 640: 100% ━━━━━━━━━━━━ 309/309 3.8it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.0it/s 9.1s\n", + " all 1146 3588 0.295 0.286 0.263 0.148\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 15/50 2.55G 1.662 2.058 1.683 11 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:19\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.8s\n", + " all 1146 3588 0.296 0.28 0.26 0.14\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 16/50 2.55G 1.643 2.042 1.664 33 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.6it/s 10.0s\n", + " all 1146 3588 0.478 0.302 0.27 0.147\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 17/50 2.55G 1.66 2.025 1.672 34 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.6it/s 9.9s\n", + " all 1146 3588 0.488 0.31 0.28 0.157\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 18/50 2.55G 1.632 1.98 1.648 7 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.6it/s 10.0s\n", + " all 1146 3588 0.495 0.314 0.292 0.163\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 19/50 2.55G 1.612 1.976 1.642 12 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.0it/s 9.1s\n", + " all 1146 3588 0.487 0.307 0.291 0.162\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 20/50 2.55G 1.619 1.952 1.643 10 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.1it/s 11.7s\n", + " all 1146 3588 0.365 0.337 0.306 0.177\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 21/50 2.55G 1.61 1.962 1.629 21 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.5it/s 10.2s\n", + " all 1146 3588 0.506 0.331 0.304 0.172\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 22/50 2.55G 1.611 1.931 1.628 19 640: 100% ━━━━━━━━━━━━ 309/309 3.8it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.6it/s 10.0s\n", + " all 1146 3588 0.494 0.331 0.304 0.172\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 23/50 2.55G 1.594 1.914 1.624 16 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.8s\n", + " all 1146 3588 0.516 0.336 0.319 0.178\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 24/50 2.55G 1.59 1.897 1.621 8 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.0it/s 9.0s\n", + " all 1146 3588 0.533 0.332 0.324 0.184\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 25/50 2.55G 1.577 1.886 1.608 15 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.8it/s 9.4s\n", + " all 1146 3588 0.537 0.331 0.322 0.183\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 26/50 2.55G 1.564 1.87 1.608 5 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.8s\n", + " all 1146 3588 0.509 0.339 0.33 0.189\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 27/50 2.55G 1.564 1.833 1.59 8 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.6it/s 9.9s\n", + " all 1146 3588 0.499 0.343 0.325 0.185\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 28/50 2.55G 1.548 1.834 1.59 7 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.6it/s 9.9s\n", + " all 1146 3588 0.542 0.312 0.316 0.178\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 29/50 2.55G 1.542 1.828 1.583 9 640: 100% ━━━━━━━━━━━━ 309/309 3.8it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.1it/s 8.8s\n", + " all 1146 3588 0.519 0.329 0.316 0.181\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 30/50 2.55G 1.525 1.805 1.58 5 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.6it/s 10.1s\n", + " all 1146 3588 0.559 0.342 0.343 0.191\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 31/50 2.55G 1.533 1.78 1.578 6 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.9s\n", + " all 1146 3588 0.547 0.348 0.334 0.189\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 32/50 2.55G 1.52 1.765 1.56 13 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.8it/s 9.5s\n", + " all 1146 3588 0.549 0.352 0.34 0.197\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 33/50 2.55G 1.521 1.768 1.561 8 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.2it/s 8.6s\n", + " all 1146 3588 0.587 0.348 0.354 0.203\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 34/50 2.55G 1.508 1.748 1.552 9 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.8s\n", + " all 1146 3588 0.557 0.354 0.35 0.198\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 35/50 2.55G 1.511 1.743 1.552 35 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:19\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.8s\n", + " all 1146 3588 0.556 0.34 0.344 0.2\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 36/50 2.55G 1.502 1.732 1.544 9 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.8s\n", + " all 1146 3588 0.566 0.349 0.352 0.204\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 37/50 2.55G 1.491 1.704 1.535 8 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:19\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.7s\n", + " all 1146 3588 0.563 0.365 0.361 0.207\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 38/50 2.55G 1.494 1.687 1.533 12 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:19\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.3s\n", + " all 1146 3588 0.56 0.371 0.362 0.209\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 39/50 2.55G 1.495 1.692 1.542 14 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.5it/s 10.2s\n", + " all 1146 3588 0.569 0.358 0.361 0.208\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 40/50 2.55G 1.482 1.674 1.528 13 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:20\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.6s\n", + " all 1146 3588 0.586 0.352 0.356 0.205\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 41/50 2.55G 1.563 1.695 1.601 7 640: 100% ━━━━━━━━━━━━ 309/309 3.9it/s 1:19\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.7s\n", + " all 1146 3588 0.564 0.372 0.371 0.214\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 42/50 2.55G 1.544 1.652 1.596 3 640: 100% ━━━━━━━━━━━━ 309/309 4.1it/s 1:15\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.8s\n", + " all 1146 3588 0.59 0.373 0.371 0.216\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 43/50 2.55G 1.529 1.618 1.582 3 640: 100% ━━━━━━━━━━━━ 309/309 4.0it/s 1:16\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.6s\n", + " all 1146 3588 0.585 0.371 0.381 0.222\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 44/50 2.55G 1.524 1.601 1.581 9 640: 100% ━━━━━━━━━━━━ 309/309 4.1it/s 1:15\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.8it/s 9.6s\n", + " all 1146 3588 0.587 0.387 0.385 0.223\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 45/50 2.55G 1.515 1.583 1.575 8 640: 100% ━━━━━━━━━━━━ 309/309 4.1it/s 1:16\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.1it/s 8.7s\n", + " all 1146 3588 0.582 0.379 0.383 0.222\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 46/50 2.55G 1.496 1.564 1.564 9 640: 100% ━━━━━━━━━━━━ 309/309 4.1it/s 1:15\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.8it/s 9.4s\n", + " all 1146 3588 0.595 0.386 0.387 0.225\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 47/50 2.55G 1.494 1.558 1.561 5 640: 100% ━━━━━━━━━━━━ 309/309 4.0it/s 1:16\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.1it/s 8.8s\n", + " all 1146 3588 0.607 0.378 0.392 0.227\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 48/50 2.55G 1.496 1.559 1.564 4 640: 100% ━━━━━━━━━━━━ 309/309 4.0it/s 1:17\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.6s\n", + " all 1146 3588 0.573 0.401 0.39 0.227\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 49/50 2.55G 1.48 1.528 1.541 4 640: 100% ━━━━━━━━━━━━ 309/309 4.1it/s 1:15\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.8it/s 9.4s\n", + " all 1146 3588 0.583 0.396 0.394 0.229\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 50/50 2.55G 1.478 1.521 1.546 3 640: 100% ━━━━━━━━━━━━ 309/309 4.1it/s 1:16\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.8s\n", + " all 1146 3588 0.588 0.391 0.393 0.227\n", + "\n", + "50 epochs completed in 1.250 hours.\n", + "Optimizer stripped from /content/runs/detect/yolov8_road-2/weights/last.pt, 6.3MB\n", + "Optimizer stripped from /content/runs/detect/yolov8_road-2/weights/best.pt, 6.3MB\n", + "\n", + "Validating /content/runs/detect/yolov8_road-2/weights/best.pt...\n", + "Ultralytics 8.4.92 🚀 Python-3.12.13 torch-2.11.0+cu128 CUDA:0 (Tesla T4, 14913MiB)\n", + "Model summary (fused): 73 layers, 3,007,013 parameters, 0 gradients, 8.1 GFLOPs\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.1it/s 11.6s\n", + " all 1146 3588 0.583 0.395 0.394 0.23\n", + " alligator 502 1001 0.555 0.269 0.36 0.182\n", + " block 81 86 0.701 0.763 0.802 0.618\n", + " crack 113 226 0.342 0.276 0.215 0.109\n", + " edge 1 1 1 0 0 0\n", + " longitudinal 247 548 0.363 0.264 0.237 0.0955\n", + " pothole 576 1600 0.589 0.612 0.602 0.307\n", + " transverse 91 126 0.529 0.579 0.539 0.297\n", + "Speed: 0.3ms preprocess, 2.2ms inference, 0.0ms loss, 2.1ms postprocess per image\n", + "Results saved to \u001b[1m/content/runs/detect/yolov8_road-2\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "ultralytics.utils.metrics.DetMetrics object with attributes:\n", + "\n", + "ap_class_index: array([0, 1, 2, 3, 4, 5, 6])\n", + "box: ultralytics.utils.metrics.Metric object\n", + "confusion_matrix: \n", + 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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([[ 0.90609, 0.90609, 0.8971, ..., 0, 0, 0],\n", + " [ 0.97674, 0.97674, 0.97674, ..., 0, 0, 0],\n", + " [ 0.81416, 0.81416, 0.77434, ..., 0, 0, 0],\n", + " ...,\n", + " [ 0.88139, 0.88139, 0.85401, ..., 0, 0, 0],\n", + " [ 0.8925, 0.8925, 0.88313, ..., 0, 0, 0],\n", + " [ 0.88095, 0.88095, 0.87302, ..., 0, 0, 0]]), 'Confidence', 'Recall']]\n", + "fitness: 0.22962098149105933\n", + "keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']\n", + "maps: array([ 0.18166, 0.6176, 0.10859, 0, 0.09549, 0.30688, 0.29712])\n", + "names: {0: 'alligator', 1: 'block', 2: 'crack', 3: 'edge', 4: 'longitudinal', 5: 'pothole', 6: 'transverse'}\n", + "nt_per_class: array([1001, 86, 226, 1, 548, 1600, 126])\n", + "nt_per_image: array([502, 81, 113, 1, 247, 576, 91])\n", + "results_dict: {'metrics/precision(B)': 0.582676936275727, 'metrics/recall(B)': 0.3947123892316416, 'metrics/mAP50(B)': 0.39357312974335407, 'metrics/mAP50-95(B)': 0.22962098149105933, 'fitness': 0.22962098149105933}\n", + "save_dir: PosixPath('/content/runs/detect/yolov8_road-2')\n", + "speed: {'preprocess': 0.25370932460957246, 'inference': 2.1557022312371266, 'loss': 0.00042353926713998555, 'postprocess': 2.0760460148333824}\n", + "stats: {'tp': [], 'conf': [], 'pred_cls': [], 'target_cls': [], 'target_img': []}" + ] + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**YOLO10**" + ], + "metadata": { + "id": "Gg_yU6COhVN9" + } + }, + { + "cell_type": "code", + "source": [ + "model_v10 = YOLO(\"yolov10n.pt\")\n", + "model_v10.train(\n", + " data=data_path,\n", + " epochs=50,\n", + " imgsz=640,\n", + " batch=16,\n", + " patience=3,\n", + " name=\"yolov10_road\"\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "azW1e_JppQyh", + "outputId": "3f52d542-e5fb-4cac-eb68-2ffd5e2c090f" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\r\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.4.0/yolov10n.pt to 'yolov10n.pt': 100% ━━━━━━━━━━━━ 5.6MB 99.7MB/s 0.1s\n", + "Ultralytics 8.4.92 🚀 Python-3.12.13 torch-2.11.0+cu128 CUDA:0 (Tesla T4, 14913MiB)\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, cls_pw=0.0, cls_remap=True, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/content/road-damage-1/data.yaml, degrees=0.0, deterministic=True, device=, dfl=1.5, dis=6.0, distill_model=None, dnn=False, dropout=0.0, dynamic=False, embed=None, end2end=None, epochs=50, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, 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=yolov10n.pt, momentum=0.937, mosaic=1.0, multi_scale=0.0, name=yolov10_road, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=3, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, quantize=None, rect=False, resume=False, retina_masks=False, rle=1.0, save=True, save_conf=False, save_crop=False, save_dir=/content/runs/detect/yolov10_road, 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=tracktrack.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=7\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 9856 ultralytics.nn.modules.block.SCDown [64, 128, 3, 2] \n", + " 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] \n", + " 7 -1 1 36096 ultralytics.nn.modules.block.SCDown [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 249728 ultralytics.nn.modules.block.PSA [256, 256] \n", + " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 13 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] \n", + " 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 16 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] \n", + " 17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n", + " 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 19 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] \n", + " 20 -1 1 18048 ultralytics.nn.modules.block.SCDown [128, 128, 3, 2] \n", + " 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 22 -1 1 282624 ultralytics.nn.modules.block.C2fCIB [384, 256, 1, True, True] \n", + " 23 [16, 19, 22] 1 864058 ultralytics.nn.modules.head.v10Detect [7, [64, 128, 256]] \n", + "YOLOv10n summary: 224 layers, 2,709,770 parameters, 2,709,754 gradients, 8.4 GFLOPs\n", + "\n", + "Transferred 493/595 items from pretrained weights\n", + "Freezing layer 'model.23.dfl.conv.weight'\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 2052.3±792.8 MB/s, size: 50.1 KB)\n", + "\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/road-damage-1/train/labels.cache... 4930 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 4930/4930 1.6Git/s 0.0s\n", + "WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 82, len(boxes) = 15024. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\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 ✅ (ping: 0.3±0.7 ms, read: 822.3±627.1 MB/s, size: 88.7 KB)\n", + "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /content/road-damage-1/valid/labels.cache... 1146 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 1146/1146 49.0Mit/s 0.0s\n", + "WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 33, len(boxes) = 3588. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\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=0.000909, momentum=0.9) with parameter groups 95 weight(decay=0.0), 108 weight(decay=0.0005), 107 bias(decay=0.0)\n", + "Plotting labels to /content/runs/detect/yolov10_road/labels.jpg... \n", + "Image sizes 640 train, 640 val\n", + "Using 2 dataloader workers\n", + "Logging results to \u001b[1m/content/runs/detect/yolov10_road\u001b[0m\n", + "Starting training for 50 epochs...\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 1/50 2.91G 1.749 5.032 1.666 9 640: 100% ━━━━━━━━━━━━ 309/309 2.6it/s 1:58\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 2.3it/s 15.9s\n", + " all 1146 3588 0.458 0.106 0.0702 0.034\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 2/50 3.38G 1.783 3.886 1.648 16 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:35\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.2s\n", + " all 1146 3588 0.236 0.0997 0.0664 0.0273\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 3/50 3.38G 1.827 3.316 1.66 4 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:34\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.3it/s 8.4s\n", + " all 1146 3588 0.254 0.113 0.0729 0.0331\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 4/50 3.38G 1.808 3.157 1.647 13 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:34\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.4it/s 8.2s\n", + " all 1146 3588 0.228 0.147 0.109 0.0489\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 5/50 3.38G 1.785 3.006 1.609 6 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.3s\n", + " all 1146 3588 0.243 0.159 0.128 0.0654\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 6/50 3.38G 1.771 2.926 1.604 7 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.2s\n", + " all 1146 3588 0.287 0.187 0.142 0.0741\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 7/50 3.38G 1.752 2.852 1.591 8 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.0it/s 9.1s\n", + " all 1146 3588 0.379 0.206 0.176 0.0862\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 8/50 3.38G 1.76 2.783 1.577 19 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.4it/s 8.2s\n", + " all 1146 3588 0.269 0.23 0.174 0.0836\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 9/50 3.38G 1.734 2.758 1.56 11 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.1s\n", + " all 1146 3588 0.248 0.181 0.171 0.0952\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 10/50 3.38G 1.724 2.674 1.555 15 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.3s\n", + " all 1146 3588 0.414 0.234 0.199 0.11\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 11/50 3.38G 1.718 2.652 1.554 8 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:34\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.3s\n", + " all 1146 3588 0.37 0.203 0.211 0.115\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 12/50 3.38G 1.69 2.598 1.545 4 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.2s\n", + " all 1146 3588 0.291 0.25 0.226 0.124\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 13/50 3.38G 1.704 2.6 1.543 9 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:34\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.2it/s 8.5s\n", + " all 1146 3588 0.311 0.246 0.229 0.124\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 14/50 3.38G 1.697 2.554 1.539 8 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:35\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.4it/s 8.2s\n", + " all 1146 3588 0.357 0.252 0.248 0.135\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 15/50 3.38G 1.69 2.509 1.535 11 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.2s\n", + " all 1146 3588 0.333 0.262 0.245 0.136\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 16/50 3.38G 1.669 2.491 1.518 33 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.2s\n", + " all 1146 3588 0.31 0.254 0.233 0.125\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 17/50 3.38G 1.68 2.481 1.529 34 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.0it/s 9.1s\n", + " all 1146 3588 0.387 0.267 0.263 0.147\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 18/50 3.38G 1.668 2.419 1.519 7 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.3it/s 8.3s\n", + " all 1146 3588 0.355 0.276 0.261 0.144\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 19/50 3.38G 1.645 2.405 1.508 12 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.2it/s 8.6s\n", + " all 1146 3588 0.32 0.302 0.271 0.148\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 20/50 3.38G 1.643 2.38 1.508 10 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.3s\n", + " all 1146 3588 0.334 0.296 0.284 0.161\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 21/50 3.38G 1.645 2.377 1.498 21 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.3s\n", + " all 1146 3588 0.349 0.298 0.288 0.164\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 22/50 3.38G 1.642 2.358 1.507 19 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.2it/s 8.5s\n", + " all 1146 3588 0.348 0.277 0.27 0.152\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 23/50 3.38G 1.624 2.333 1.495 16 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.3it/s 8.4s\n", + " all 1146 3588 0.501 0.291 0.285 0.161\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 24/50 3.38G 1.613 2.305 1.489 8 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.2s\n", + " all 1146 3588 0.398 0.293 0.3 0.167\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 25/50 3.38G 1.606 2.303 1.483 15 640: 100% ━━━━━━━━━━━━ 309/309 3.4it/s 1:32\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.0it/s 9.0s\n", + " all 1146 3588 0.498 0.32 0.303 0.172\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 26/50 3.38G 1.59 2.275 1.484 5 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:32\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.3it/s 8.3s\n", + " all 1146 3588 0.508 0.314 0.3 0.17\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 27/50 3.38G 1.604 2.239 1.474 8 640: 100% ━━━━━━━━━━━━ 309/309 3.4it/s 1:32\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.0it/s 9.0s\n", + " all 1146 3588 0.46 0.32 0.292 0.161\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 28/50 3.38G 1.596 2.226 1.47 7 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.2s\n", + " all 1146 3588 0.512 0.321 0.307 0.173\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 29/50 3.38G 1.588 2.212 1.466 9 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.3s\n", + " all 1146 3588 0.499 0.328 0.301 0.168\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 30/50 3.38G 1.572 2.201 1.467 5 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.4it/s 8.2s\n", + " all 1146 3588 0.526 0.321 0.316 0.18\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 31/50 3.38G 1.575 2.147 1.457 6 640: 100% ━━━━━━━━━━━━ 309/309 3.4it/s 1:32\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.3s\n", + " all 1146 3588 0.547 0.327 0.331 0.183\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 32/50 3.38G 1.562 2.143 1.457 13 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.2s\n", + " all 1146 3588 0.493 0.334 0.324 0.182\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 33/50 3.38G 1.571 2.139 1.457 8 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.2s\n", + " all 1146 3588 0.514 0.34 0.324 0.184\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 34/50 3.38G 1.548 2.137 1.448 9 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:33\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.5it/s 8.0s\n", + " all 1146 3588 0.559 0.316 0.331 0.188\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 35/50 3.38G 1.553 2.103 1.447 35 640: 100% ━━━━━━━━━━━━ 309/309 3.4it/s 1:32\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.3s\n", + " all 1146 3588 0.494 0.349 0.332 0.192\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 36/50 3.38G 1.545 2.104 1.436 9 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:32\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.2s\n", + " all 1146 3588 0.563 0.33 0.338 0.191\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 37/50 3.38G 1.547 2.072 1.434 8 640: 100% ━━━━━━━━━━━━ 309/309 3.4it/s 1:32\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.3it/s 8.3s\n", + " all 1146 3588 0.539 0.349 0.341 0.196\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 38/50 3.38G 1.54 2.054 1.429 12 640: 100% ━━━━━━━━━━━━ 309/309 3.4it/s 1:31\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.0it/s 9.1s\n", + " all 1146 3588 0.535 0.347 0.345 0.199\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 39/50 3.38G 1.536 2.054 1.434 14 640: 100% ━━━━━━━━━━━━ 309/309 3.3it/s 1:32\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.1s\n", + " all 1146 3588 0.559 0.333 0.346 0.2\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 40/50 3.38G 1.531 2.042 1.423 13 640: 100% ━━━━━━━━━━━━ 309/309 3.4it/s 1:32\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.4it/s 8.1s\n", + " all 1146 3588 0.544 0.352 0.344 0.199\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 41/50 3.38G 1.593 2.003 1.499 7 640: 100% ━━━━━━━━━━━━ 309/309 3.4it/s 1:31\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.2s\n", + " all 1146 3588 0.546 0.353 0.35 0.202\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 42/50 3.38G 1.592 1.961 1.499 3 640: 100% ━━━━━━━━━━━━ 309/309 3.5it/s 1:29\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.0it/s 8.9s\n", + " all 1146 3588 0.558 0.354 0.351 0.204\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 43/50 3.38G 1.569 1.924 1.487 3 640: 100% ━━━━━━━━━━━━ 309/309 3.5it/s 1:28\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.2s\n", + " all 1146 3588 0.602 0.334 0.364 0.21\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 44/50 3.38G 1.568 1.9 1.479 9 640: 100% ━━━━━━━━━━━━ 309/309 3.5it/s 1:29\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.5it/s 8.1s\n", + " all 1146 3588 0.555 0.359 0.364 0.211\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 45/50 3.38G 1.556 1.892 1.48 8 640: 100% ━━━━━━━━━━━━ 309/309 3.5it/s 1:28\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.0it/s 9.1s\n", + " all 1146 3588 0.586 0.359 0.369 0.214\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 46/50 3.38G 1.546 1.869 1.474 9 640: 100% ━━━━━━━━━━━━ 309/309 3.5it/s 1:28\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.3it/s 8.3s\n", + " all 1146 3588 0.578 0.358 0.368 0.216\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 47/50 3.38G 1.54 1.863 1.462 5 640: 100% ━━━━━━━━━━━━ 309/309 3.5it/s 1:28\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.2s\n", + " all 1146 3588 0.578 0.36 0.369 0.217\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 48/50 3.38G 1.541 1.863 1.468 4 640: 100% ━━━━━━━━━━━━ 309/309 3.5it/s 1:28\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.0it/s 8.9s\n", + " all 1146 3588 0.577 0.353 0.368 0.216\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 49/50 3.38G 1.522 1.836 1.449 4 640: 100% ━━━━━━━━━━━━ 309/309 3.5it/s 1:28\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.5it/s 8.0s\n", + " all 1146 3588 0.593 0.35 0.37 0.215\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 50/50 3.38G 1.531 1.842 1.457 3 640: 100% ━━━━━━━━━━━━ 309/309 3.5it/s 1:29\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.2s\n", + " all 1146 3588 0.588 0.353 0.369 0.217\n", + "\n", + "50 epochs completed in 1.419 hours.\n", + "Optimizer stripped from /content/runs/detect/yolov10_road/weights/last.pt, 5.8MB\n", + "Optimizer stripped from /content/runs/detect/yolov10_road/weights/best.pt, 5.8MB\n", + "\n", + "Validating /content/runs/detect/yolov10_road/weights/best.pt...\n", + "Ultralytics 8.4.92 🚀 Python-3.12.13 torch-2.11.0+cu128 CUDA:0 (Tesla T4, 14913MiB)\n", + "YOLOv10n summary (fused): 102 layers, 2,266,533 parameters, 0 gradients, 6.5 GFLOPs\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.2it/s 11.3s\n", + " all 1146 3588 0.582 0.352 0.369 0.217\n", + " alligator 502 1001 0.586 0.239 0.345 0.18\n", + " block 81 86 0.659 0.721 0.788 0.612\n", + " crack 113 226 0.409 0.226 0.2 0.09\n", + " edge 1 1 1 0 0 0\n", + " longitudinal 247 548 0.331 0.184 0.185 0.0756\n", + " pothole 576 1600 0.598 0.554 0.566 0.296\n", + " transverse 91 126 0.486 0.54 0.497 0.264\n", + "Speed: 0.2ms preprocess, 2.9ms inference, 0.0ms loss, 0.5ms postprocess per image\n", + "Results saved to \u001b[1m/content/runs/detect/yolov10_road\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "ultralytics.utils.metrics.DetMetrics object with attributes:\n", + "\n", + "ap_class_index: array([0, 1, 2, 3, 4, 5, 6])\n", + "box: ultralytics.utils.metrics.Metric object\n", + "confusion_matrix: \n", + 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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([[ 0.88911, 0.88911, 0.87313, ..., 0, 0, 0],\n", + " [ 0.96512, 0.96512, 0.95349, ..., 0, 0, 0],\n", + " [ 0.74336, 0.74336, 0.72566, ..., 0, 0, 0],\n", + " ...,\n", + " [ 0.85401, 0.85401, 0.81934, ..., 0, 0, 0],\n", + " [ 0.8825, 0.8825, 0.86562, ..., 0, 0, 0],\n", + " [ 0.84127, 0.84127, 0.84127, ..., 0, 0, 0]]), 'Confidence', 'Recall']]\n", + "fitness: 0.21680536921887986\n", + "keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']\n", + "maps: array([ 0.17971, 0.61184, 0.089955, 0, 0.07559, 0.29641, 0.26414])\n", + "names: {0: 'alligator', 1: 'block', 2: 'crack', 3: 'edge', 4: 'longitudinal', 5: 'pothole', 6: 'transverse'}\n", + "nt_per_class: array([1001, 86, 226, 1, 548, 1600, 126])\n", + "nt_per_image: array([502, 81, 113, 1, 247, 576, 91])\n", + "results_dict: {'metrics/precision(B)': 0.5815596636776207, 'metrics/recall(B)': 0.35187061387987756, 'metrics/mAP50(B)': 0.3687048648377552, 'metrics/mAP50-95(B)': 0.21680536921887986, 'fitness': 0.21680536921887986}\n", + "save_dir: PosixPath('/content/runs/detect/yolov10_road')\n", + "speed: {'preprocess': 0.24004823211072088, 'inference': 2.90608333246368, 'loss': 0.00037679231879939317, 'postprocess': 0.5166126710184351}\n", + "stats: {'tp': [], 'conf': [], 'pred_cls': [], 'target_cls': [], 'target_img': []}" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**YOLO11**" + ], + "metadata": { + "id": "pR70-cAHhZAx" + } + }, + { + "cell_type": "code", + "source": [ + "model_v11 = YOLO(\"yolo11n.pt\")\n", + "model_v11.train(\n", + " data=data_path,\n", + " epochs=50,\n", + " imgsz=640,\n", + " batch=16,\n", + " patience=3,\n", + " name=\"yolov11_road\"\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9ME2_8FLpUUE", + "outputId": "52704b2e-f4d2-4e5b-d2c1-52ba6fa3b3bf" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Ultralytics 8.4.92 🚀 Python-3.12.13 torch-2.11.0+cu128 CUDA:0 (Tesla T4, 14913MiB)\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, cls_pw=0.0, cls_remap=True, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/content/road-damage-1/data.yaml, degrees=0.0, deterministic=True, device=, dfl=1.5, dis=6.0, distill_model=None, dnn=False, dropout=0.0, dynamic=False, embed=None, end2end=None, epochs=50, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, 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=yolo11n.pt, momentum=0.937, mosaic=1.0, multi_scale=0.0, name=yolov11_road-2, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=3, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, quantize=None, rect=False, resume=False, retina_masks=False, rle=1.0, save=True, save_conf=False, save_crop=False, save_dir=/content/runs/detect/yolov11_road-2, 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=tracktrack.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=7\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 6640 ultralytics.nn.modules.block.C3k2 [32, 64, 1, False, 0.25] \n", + " 3 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n", + " 4 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25] \n", + " 5 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n", + " 6 -1 1 87040 ultralytics.nn.modules.block.C3k2 [128, 128, 1, True] \n", + " 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n", + " 8 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True] \n", + " 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] \n", + " 10 -1 1 249728 ultralytics.nn.modules.block.C2PSA [256, 256, 1] \n", + " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 13 -1 1 111296 ultralytics.nn.modules.block.C3k2 [384, 128, 1, False] \n", + " 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 16 -1 1 32096 ultralytics.nn.modules.block.C3k2 [256, 64, 1, False] \n", + " 17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n", + " 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 19 -1 1 86720 ultralytics.nn.modules.block.C3k2 [192, 128, 1, False] \n", + " 20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n", + " 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", + " 22 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True] \n", + " 23 [16, 19, 22] 1 432037 ultralytics.nn.modules.head.Detect [7, 16, None, [64, 128, 256]] \n", + "YOLO11n summary: 182 layers, 2,591,205 parameters, 2,591,189 gradients, 6.4 GFLOPs\n", + "\n", + "Transferred 448/499 items from pretrained weights\n", + "Freezing layer 'model.23.dfl.conv.weight'\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 2112.9±805.7 MB/s, size: 50.1 KB)\n", + "\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/road-damage-1/train/labels.cache... 4930 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 4930/4930 1.9Git/s 0.0s\n", + "WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 82, len(boxes) = 15024. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\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 ✅ (ping: 0.0±0.0 ms, read: 525.1±323.9 MB/s, size: 88.7 KB)\n", + "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /content/road-damage-1/valid/labels.cache... 1146 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 1146/1146 43.7Mit/s 0.0s\n", + "WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 33, len(boxes) = 3588. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\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=0.000909, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0)\n", + "Plotting labels to /content/runs/detect/yolov11_road-2/labels.jpg... \n", + "Image sizes 640 train, 640 val\n", + "Using 2 dataloader workers\n", + "Logging results to \u001b[1m/content/runs/detect/yolov11_road-2\u001b[0m\n", + "Starting training for 50 epochs...\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 1/50 2.66G 1.953 3.421 1.884 9 640: 100% ━━━━━━━━━━━━ 309/309 3.4it/s 1:30\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.5it/s 10.3s\n", + " all 1146 3588 0.288 0.148 0.0942 0.0453\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 2/50 3.26G 1.922 2.774 1.879 16 640: 100% ━━━━━━━━━━━━ 309/309 3.7it/s 1:24\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.7s\n", + " all 1146 3588 0.342 0.159 0.125 0.0599\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 3/50 3.26G 1.902 2.606 1.864 4 640: 100% ━━━━━━━━━━━━ 309/309 3.7it/s 1:25\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.5it/s 10.2s\n", + " all 1146 3588 0.477 0.178 0.151 0.0715\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 4/50 3.26G 1.881 2.534 1.868 13 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:27\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.8it/s 9.6s\n", + " all 1146 3588 0.252 0.169 0.138 0.0574\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 5/50 3.26G 1.826 2.432 1.812 6 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:25\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.6s\n", + " all 1146 3588 0.175 0.187 0.12 0.0522\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 6/50 3.26G 1.82 2.376 1.808 7 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:25\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.6s\n", + " all 1146 3588 0.217 0.223 0.171 0.0902\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 7/50 3.26G 1.796 2.322 1.793 8 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:26\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.8it/s 9.5s\n", + " all 1146 3588 0.264 0.271 0.198 0.102\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 8/50 3.26G 1.766 2.255 1.757 19 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:26\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.2it/s 8.6s\n", + " all 1146 3588 0.257 0.264 0.219 0.114\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 9/50 3.26G 1.757 2.237 1.757 11 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:26\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.6it/s 10.1s\n", + " all 1146 3588 0.312 0.21 0.206 0.104\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 10/50 3.26G 1.732 2.168 1.735 15 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:26\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.6it/s 10.1s\n", + " all 1146 3588 0.244 0.265 0.211 0.108\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 11/50 3.26G 1.721 2.155 1.723 8 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:25\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.0it/s 9.1s\n", + " all 1146 3588 0.304 0.277 0.253 0.138\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 12/50 3.26G 1.694 2.11 1.71 4 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:26\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.6it/s 9.9s\n", + " all 1146 3588 0.335 0.248 0.243 0.133\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 13/50 3.26G 1.698 2.118 1.713 9 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:26\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.5it/s 10.3s\n", + " all 1146 3588 0.277 0.287 0.255 0.141\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 14/50 3.26G 1.692 2.073 1.708 8 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:26\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.6it/s 10.1s\n", + " all 1146 3588 0.33 0.288 0.262 0.144\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 15/50 3.26G 1.672 2.05 1.693 11 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:25\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.6it/s 10.0s\n", + " all 1146 3588 0.347 0.28 0.269 0.145\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 16/50 3.26G 1.648 2.025 1.68 33 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:27\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.2s\n", + " all 1146 3588 0.361 0.3 0.28 0.152\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 17/50 3.26G 1.663 2.003 1.676 34 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:26\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.6it/s 10.0s\n", + " all 1146 3588 0.373 0.315 0.306 0.168\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 18/50 3.26G 1.636 1.967 1.653 7 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:26\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 4.0it/s 8.9s\n", + " all 1146 3588 0.394 0.294 0.282 0.158\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 19/50 3.26G 1.617 1.963 1.646 12 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:26\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.6it/s 10.0s\n", + " all 1146 3588 0.513 0.323 0.309 0.169\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 20/50 3.26G 1.625 1.935 1.646 10 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:27\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.1s\n", + " all 1146 3588 0.377 0.315 0.29 0.161\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 21/50 3.26G 1.618 1.945 1.641 21 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:26\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.8s\n", + " all 1146 3588 0.372 0.342 0.313 0.176\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 22/50 3.26G 1.614 1.91 1.634 19 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:26\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.8s\n", + " all 1146 3588 0.38 0.338 0.329 0.182\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 23/50 3.26G 1.601 1.896 1.628 16 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:26\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.7s\n", + " all 1146 3588 0.386 0.342 0.323 0.177\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 24/50 3.26G 1.588 1.881 1.619 8 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:25\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.7it/s 9.7s\n", + " all 1146 3588 0.509 0.35 0.334 0.19\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 25/50 3.26G 1.585 1.877 1.608 15 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:25\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.9it/s 9.3s\n", + " all 1146 3588 0.552 0.336 0.335 0.19\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 26/50 3.26G 1.574 1.853 1.611 5 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:25\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.6it/s 10.1s\n", + " all 1146 3588 0.508 0.348 0.336 0.189\n", + "\n", + " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", + "\u001b[K 27/50 3.26G 1.576 1.822 1.6 8 640: 100% ━━━━━━━━━━━━ 309/309 3.6it/s 1:25\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 3.8it/s 9.5s\n", + " all 1146 3588 0.527 0.345 0.326 0.183\n", + "\u001b[34m\u001b[1mEarlyStopping: \u001b[0mTraining stopped early as no improvement observed in last 3 epochs. Best results observed at epoch 24, best model saved as best.pt.\n", + "To update EarlyStopping(patience=3) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.\n", + "\n", + "27 epochs completed in 0.721 hours.\n", + "Optimizer stripped from /content/runs/detect/yolov11_road-2/weights/last.pt, 5.5MB\n", + "Optimizer stripped from /content/runs/detect/yolov11_road-2/weights/best.pt, 5.5MB\n", + "\n", + "Validating /content/runs/detect/yolov11_road-2/weights/best.pt...\n", + "Ultralytics 8.4.92 🚀 Python-3.12.13 torch-2.11.0+cu128 CUDA:0 (Tesla T4, 14913MiB)\n", + "YOLO11n summary (fused): 101 layers, 2,583,517 parameters, 0 gradients, 6.3 GFLOPs\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 36/36 2.9it/s 12.6s\n", + " all 1146 3588 0.51 0.351 0.334 0.19\n", + " alligator 502 1001 0.527 0.21 0.298 0.162\n", + " block 81 86 0.522 0.779 0.727 0.541\n", + " crack 113 226 0.286 0.242 0.172 0.0747\n", + " edge 1 1 1 0 0 0\n", + " longitudinal 247 548 0.261 0.124 0.135 0.049\n", + " pothole 576 1600 0.459 0.612 0.563 0.273\n", + " transverse 91 126 0.514 0.487 0.443 0.229\n", + "Speed: 0.2ms preprocess, 2.3ms inference, 0.0ms loss, 2.4ms postprocess per image\n", + "Results saved to \u001b[1m/content/runs/detect/yolov11_road-2\u001b[0m\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "ultralytics.utils.metrics.DetMetrics object with attributes:\n", + "\n", + "ap_class_index: array([0, 1, 2, 3, 4, 5, 6])\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, 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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([[ 0.8971, 0.8971, 0.88312, ..., 0, 0, 0],\n", + " [ 0.97674, 0.97674, 0.97674, ..., 0, 0, 0],\n", + " [ 0.81858, 0.81858, 0.80088, ..., 0, 0, 0],\n", + " ...,\n", + " [ 0.84124, 0.84124, 0.82847, ..., 0, 0, 0],\n", + " [ 0.90187, 0.90187, 0.89625, ..., 0, 0, 0],\n", + " [ 0.86508, 0.86508, 0.83333, ..., 0, 0, 0]]), 'Confidence', 'Recall']]\n", + "fitness: 0.1898745901181122\n", + "keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']\n", + "maps: array([ 0.16177, 0.54144, 0.074686, 0, 0.049049, 0.27313, 0.22905])\n", + "names: {0: 'alligator', 1: 'block', 2: 'crack', 3: 'edge', 4: 'longitudinal', 5: 'pothole', 6: 'transverse'}\n", + "nt_per_class: array([1001, 86, 226, 1, 548, 1600, 126])\n", + "nt_per_image: array([502, 81, 113, 1, 247, 576, 91])\n", + "results_dict: {'metrics/precision(B)': 0.5098548297577096, 'metrics/recall(B)': 0.35065984603076317, 'metrics/mAP50(B)': 0.3338002233634235, 'metrics/mAP50-95(B)': 0.1898745901181122, 'fitness': 0.1898745901181122}\n", + "save_dir: PosixPath('/content/runs/detect/yolov11_road-2')\n", + "speed: {'preprocess': 0.2125806125659628, 'inference': 2.3254544424100403, 'loss': 0.0005038490413254766, 'postprocess': 2.379640601220973}\n", + "stats: {'tp': [], 'conf': [], 'pred_cls': [], 'target_cls': [], 'target_img': []}" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Evaluation Metrics" + ], + "metadata": { + "id": "PRogSZ2DheOm" + } + }, + { + "cell_type": "code", + "source": [ + "metrics_v8 = model_v8.val()\n", + "metrics_v10 = model_v10.val()\n", + "metrics_v11 = model_v11.val()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zK9BJgwApUWf", + "outputId": "4339d712-0322-4204-baca-f1e848459cd9" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Ultralytics 8.4.92 🚀 Python-3.12.13 torch-2.11.0+cu128 CUDA:0 (Tesla T4, 14913MiB)\n", + "Model summary (fused): 73 layers, 3,007,013 parameters, 0 gradients, 8.1 GFLOPs\n", + "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1418.6±297.4 MB/s, size: 68.9 KB)\n", + "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /content/road-damage-1/valid/labels.cache... 1146 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 1146/1146 480.7Mit/s 0.0s\n", + "WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 33, len(boxes) = 3588. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 72/72 5.7it/s 12.7s\n", + " all 1146 3588 0.583 0.395 0.394 0.23\n", + " alligator 502 1001 0.554 0.27 0.358 0.181\n", + " block 81 86 0.701 0.764 0.802 0.617\n", + " crack 113 226 0.341 0.274 0.215 0.108\n", + " edge 1 1 1 0 0 0\n", + " longitudinal 247 548 0.364 0.266 0.236 0.0953\n", + " pothole 576 1600 0.591 0.612 0.602 0.307\n", + " transverse 91 126 0.533 0.579 0.542 0.3\n", + "Speed: 1.4ms preprocess, 3.7ms inference, 0.0ms loss, 1.5ms postprocess per image\n", + "Results saved to \u001b[1m/content/runs/detect/val\u001b[0m\n", + "Ultralytics 8.4.92 🚀 Python-3.12.13 torch-2.11.0+cu128 CUDA:0 (Tesla T4, 14913MiB)\n", + "YOLOv10n summary (fused): 102 layers, 2,266,533 parameters, 0 gradients, 6.5 GFLOPs\n", + "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 2166.3±633.6 MB/s, size: 62.7 KB)\n", + "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /content/road-damage-1/valid/labels.cache... 1146 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 1146/1146 480.7Mit/s 0.0s\n", + "WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 33, len(boxes) = 3588. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 72/72 5.8it/s 12.3s\n", + " all 1146 3588 0.585 0.353 0.369 0.217\n", + " alligator 502 1001 0.588 0.238 0.346 0.18\n", + " block 81 86 0.667 0.721 0.789 0.613\n", + " crack 113 226 0.412 0.226 0.2 0.0901\n", + " edge 1 1 1 0 0 0\n", + " longitudinal 247 548 0.334 0.184 0.186 0.0759\n", + " pothole 576 1600 0.6 0.552 0.566 0.296\n", + " transverse 91 126 0.493 0.548 0.5 0.266\n", + "Speed: 1.4ms preprocess, 3.9ms inference, 0.0ms loss, 0.2ms postprocess per image\n", + "Results saved to \u001b[1m/content/runs/detect/val-2\u001b[0m\n", + "Ultralytics 8.4.92 🚀 Python-3.12.13 torch-2.11.0+cu128 CUDA:0 (Tesla T4, 14913MiB)\n", + "YOLO11n summary (fused): 101 layers, 2,583,517 parameters, 0 gradients, 6.3 GFLOPs\n", + "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 2238.7±1082.5 MB/s, size: 90.4 KB)\n", + "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /content/road-damage-1/valid/labels.cache... 1146 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 1146/1146 480.7Mit/s 0.0s\n", + "WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 33, len(boxes) = 3588. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\n", + "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 72/72 5.7it/s 12.7s\n", + " all 1146 3588 0.509 0.35 0.334 0.19\n", + " alligator 502 1001 0.526 0.211 0.298 0.162\n", + " block 81 86 0.522 0.779 0.727 0.542\n", + " crack 113 226 0.28 0.239 0.172 0.0748\n", + " edge 1 1 1 0 0 0\n", + " longitudinal 247 548 0.259 0.124 0.134 0.0486\n", + " pothole 576 1600 0.46 0.613 0.564 0.273\n", + " transverse 91 126 0.518 0.487 0.446 0.233\n", + "Speed: 1.5ms preprocess, 3.7ms inference, 0.0ms loss, 1.6ms postprocess per image\n", + "Results saved to \u001b[1m/content/runs/detect/val-3\u001b[0m\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "def get_size(path):\n", + " return round(os.path.getsize(path) / (1024 * 1024), 2)\n", + "size_v8 = get_size(\"/content/runs/detect/yolov8_road/weights/best.pt\")\n", + "size_v10 = get_size(\"/content/runs/detect/yolov10_road/weights/best.pt\")\n", + "size_v11 = get_size(\"/content/runs/detect/yolov11_road-2/weights/best.pt\")" + ], + "metadata": { + "id": "85cLBlDApUZA" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "comparison = pd.DataFrame({\n", + " \"Model\": [\"YOLOv8\", \"YOLOv10\", \"YOLO11\"],\n", + " \"mAP50\": [metrics_v8.box.map50, metrics_v10.box.map50, metrics_v11.box.map50],\n", + " \"mAP50-95\": [metrics_v8.box.map, metrics_v10.box.map, metrics_v11.box.map],\n", + " \"Precision\": [metrics_v8.box.mp, metrics_v10.box.mp, metrics_v11.box.mp],\n", + " \"Recall\": [metrics_v8.box.mr, metrics_v10.box.mr, metrics_v11.box.mr],\n", + " \"Model_Size_MB\": [size_v8, size_v10, size_v11],\n", + " \"FPS\": [1000 / metrics_v8.speed[\"inference\"], 1000 / metrics_v10.speed[\"inference\"], 1000 / metrics_v11.speed[\"inference\"]]\n", + "})\n", + "comparison" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + }, + "id": "uCf4NjW6pfWq", + "outputId": "c651365c-903d-4451-d3d4-1f9070a67a9d" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Model mAP50 mAP50-95 Precision Recall Model_Size_MB FPS\n", + "0 YOLOv8 0.393568 0.229851 0.583495 0.395155 23.36 269.971633\n", + "1 YOLOv10 0.369449 0.217349 0.584872 0.352650 5.49 253.435319\n", + "2 YOLO11 0.334375 0.190483 0.509290 0.350423 5.22 272.879149" + ], + "text/html": [ + "\n", + "
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