--- 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 --- # MS-COCO2017 ## Use the dataset ```py from random import randint from datasets import load_dataset from PIL import Image, ImageDraw, ImageFont ds = load_dataset("ariG23498/coco2017", streaming=True, split="validation") sample = next(iter(ds)) def draw_bboxes_on_image( image: Image.Image, objects: dict, category_names: dict = None, box_color: str = "red", text_color: str = "white" ) -> Image.Image: image_copy = image.copy() draw = ImageDraw.Draw(image_copy) font = ImageFont.load_default() bboxes = objects.get("bbox", []) categories = objects.get("categories", []) for bbox, category_id in zip(bboxes, categories): x, y, width, height = bbox x_min, y_min = x, y x_max, y_max = x + width, y + height # Draw bounding box draw.rectangle([x_min, y_min, x_max, y_max], outline=box_color, width=2) # Prepare label label = category_names.get(category_id, str(category_id)) if category_names else str(category_id) text_bbox = draw.textbbox((0, 0), label, font=font) text_width = text_bbox[2] - text_bbox[0] text_height = text_bbox[3] - text_bbox[1] label_top = max(y_min - text_height - 4, 0) # Draw label background and text draw.rectangle( [x_min, label_top, x_min + text_width + 4, label_top + text_height + 2], fill=box_color ) draw.text((x_min + 2, label_top + 1), label, fill=text_color, font=font) return image_copy def draw_segmaps_on_image( image: Image.Image, objects: dict, category_names: dict = None, alpha: float = 0.4, text_color: str = "white" ) -> Image.Image: base_image = image.convert("RGBA").copy() overlay = Image.new("RGBA", base_image.size, (255, 255, 255, 0)) draw = ImageDraw.Draw(overlay) font = ImageFont.load_default() segmentations = objects.get("segmentation", []) categories = objects.get("categories", []) for segmentation, category_id in zip(segmentations, categories): polygons = segmentation if isinstance(segmentation[0], list) else [segmentation] label = category_names.get(category_id, str(category_id)) if category_names else str(category_id) for polygon in polygons: if len(polygon) >= 6: points = [(polygon[i], polygon[i + 1]) for i in range(0, len(polygon), 2)] # Draw filled polygon segmap_color = (randint(125, 255), randint(0, 125), randint(0, 255)) rgba_fill = (*segmap_color, int(255 * alpha)) draw.polygon(points, fill=rgba_fill) # Draw label at first vertex x0, y0 = points[0] draw.text((x0 + 2, y0 + 2), label, fill=text_color, font=font) return Image.alpha_composite(base_image, overlay).convert("RGB") # For Bounding Boxes od_image = draw_bboxes_on_image( image=sample["image"], objects=sample["objects"], ) # For Segmentation Maps segmap_image = draw_segmaps_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 !pip install -U -q datasets # Download and unzip COCO 2017 !wget -q http://images.cocodataset.org/zips/train2017.zip !wget -q http://images.cocodataset.org/zips/val2017.zip !wget -q http://images.cocodataset.org/annotations/annotations_trainval2017.zip !unzip -q train2017.zip !unzip -q val2017.zip !unzip -q annotations_trainval2017.zip import json import shutil from pathlib import Path from tqdm import tqdm from datasets import load_dataset 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" output_dir.mkdir(parents=True, exist_ok=True) def normalize_segmentation(segmentation): if isinstance(segmentation, list): if all(isinstance(poly, list) for poly in segmentation): return segmentation # already a list of polygons elif all(isinstance(pt, (int, float)) for pt in segmentation): return [segmentation] # wrap single polygon return [] # skip RLE or malformed segmentations 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'] category = ann['category_id'] segmentation = normalize_segmentation(ann['segmentation']) if not segmentation: continue # skip if malformed or RLE if img_id not in annotations_by_image: annotations_by_image[img_id] = { "file_name": id_to_filename[img_id], "objects": { "bbox": [], "segmentation": [], "categories": [], } } annotations_by_image[img_id]["objects"]["bbox"].append(bbox) annotations_by_image[img_id]["objects"]["segmentation"].append(segmentation) annotations_by_image[img_id]["objects"]["categories"].append(category) with open(output_metadata_path, "w") as f: for metadata in annotations_by_image.values(): json.dump(metadata, f) f.write("\n") # Build imagefolder structure for split, info in splits.items(): split_dir = output_dir / split split_dir.mkdir(parents=True, exist_ok=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) # Write JSONL metadata metadata_path = split_dir / "metadata.jsonl" convert_coco_to_jsonl(split_dir, info["annotation_file"], metadata_path) # Load and push dataset = load_dataset("imagefolder", data_dir=str(output_dir)) dataset.push_to_hub("ariG23498/coco2017") ```