Update README.md

README.md

@@ -1,186 +1,5 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: objects
-    struct:
-    - name: bbox
-      sequence:
-        sequence: float64
-    - name: segmentation
-      sequence:
-        sequence:
-          sequence: float64
-    - name: categories
-      sequence: int64
-  splits:
-  - name: train
-    num_bytes: 17598458856.47
-    num_examples: 117266
-  - name: validation
-    num_bytes: 795110726.04
-    num_examples: 4952
-  download_size: 20170024873
-  dataset_size: 18393569582.510002
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-task_categories:
-- object-detection
+viewer: false
 ---
 
-# MS-COCO2017
-
-## Use the dataset
-
-```py
-from datasets import load_dataset
-ds = load_dataset("ariG23498/coco2017", streaming=True, split="validation")
-
-sample = next(iter(ds))
-
-from PIL import Image, ImageDraw
-
-def draw_bboxes_on_image(
-        image: Image.Image,
-        objects: dict,
-        category_names: dict = None,
-        box_color: str = "red",
-        text_color: str = "white"
-    ):
-    draw = ImageDraw.Draw(image)
-    bboxes = objects.get("bbox", [])
-    categories = objects.get("categories", [])
-
-    for i, bbox in enumerate(bboxes):
-        x, y, width, height = bbox
-        # PIL expects (x_min, y_min, x_max, y_max) for rectangle
-        x_min, y_min, x_max, y_max = x, y, x + width, y + height
-
-        # Draw the rectangle
-        draw.rectangle([x_min, y_min, x_max, y_max], outline=box_color, width=2)
-
-        # Get category label
-        category_id = categories[i]
-        label = str(category_id)
-        if category_names and category_id in category_names:
-            label = category_names[category_id]
-
-        # Draw the category label
-        text_bbox = draw.textbbox((x_min, y_min), label) # Use textbbox to get text size
-        text_width = text_bbox[2] - text_bbox[0]
-        text_height = text_bbox[3] - text_bbox[1]
-
-        # Draw a filled rectangle behind the text for better readability
-        draw.rectangle([x_min, y_min - text_height - 5, x_min + text_width + 5, y_min], fill=box_color)
-        draw.text((x_min + 2, y_min - text_height - 2), label, fill=text_color)
-
-    return image
-
-draw_bboxes_on_image(
-    image=sample["image"],
-    objects=sample["objects"],
-)
-```
-
-## Get the categories
-
-```py
-import json
-
-with open("/content/annotations/instances_train2017.json") as f:
-    instances = json.load(f)
-
-instances["categories"]
-```
-
-## Build the dataset and upload to Hub
-
-```py
-!wget http://images.cocodataset.org/zips/train2017.zip
-!wget http://images.cocodataset.org/zips/val2017.zip
-!wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
-
-!unzip train2017.zip
-!unzip val2017.zip
-!unzip annotations_trainval2017.zip
-
-import json
-from pathlib import Path
-from tqdm import tqdm
-from huggingface_hub import upload_folder
-from datasets import Dataset, DatasetDict, Features, Value, Sequence, Array2D
-import shutil
-
-# === Paths ===
-base_dir = Path("/content")
-splits = {
-    "train": {
-        "image_dir": base_dir / "train2017",
-        "annotation_file": base_dir / "annotations" / "instances_train2017.json",
-    },
-    "val": {
-        "image_dir": base_dir / "val2017",
-        "annotation_file": base_dir / "annotations" / "instances_val2017.json",
-    }
-}
-output_dir = base_dir / "coco_imagefolder"
-
-# Clean existing directory
-if output_dir.exists():
-    shutil.rmtree(output_dir)
-output_dir.mkdir(parents=True)
-
-def convert_coco_to_jsonl(image_dir, annotation_path, output_metadata_path):
-    with open(annotation_path) as f:
-        data = json.load(f)
-
-    id_to_filename = {img['id']: img['file_name'] for img in data['images']}
-    annotations_by_image = {}
-
-    for ann in data['annotations']:
-        img_id = ann['image_id']
-        bbox = ann['bbox']  # [x, y, width, height]
-        category = ann['category_id']
-
-        if img_id not in annotations_by_image:
-            annotations_by_image[img_id] = {
-                "file_name": id_to_filename[img_id],
-                "objects": {
-                    "bbox": [],
-                    "categories": []
-                }
-            }
-
-        annotations_by_image[img_id]["objects"]["bbox"].append(bbox)
-        annotations_by_image[img_id]["objects"]["categories"].append(category)
-
-    with open(output_metadata_path, "w") as f:
-        for img_id, metadata in annotations_by_image.items():
-            json.dump(metadata, f)
-            f.write("\n")
-
-# Convert and copy files to imagefolder-style structure
-for split, info in splits.items():
-    split_dir = output_dir / split
-    split_dir.mkdir(parents=True)
-
-    # Copy images
-    for img_path in tqdm(info["image_dir"].glob("*.jpg"), desc=f"Copying {split} images"):
-        shutil.copy(img_path, split_dir / img_path.name)
-
-    # Convert annotations
-    metadata_path = split_dir / "metadata.jsonl"
-    convert_coco_to_jsonl(split_dir, info["annotation_file"], metadata_path)
-
-# HF Dataset
-from datasets import load_dataset
-
-dataset = load_dataset("imagefolder", data_dir="/content/coco_imagefolder")
-dataset.push_to_hub("ariG23498/coco2017")
-```
+Cloned from ariG23498.