| --- |
| license: cc-by-4.0 |
| task_categories: |
| - object-detection |
| tags: |
| - COCO |
| - Detection |
| - '2017' |
| pretty_name: COCO detection dataset script |
| size_categories: |
| - 100K<n<1M |
| dataset_info: |
| config_name: '2017' |
| features: |
| - name: id |
| dtype: int64 |
| - name: objects |
| struct: |
| - name: bbox_id |
| sequence: int64 |
| - name: category_id |
| sequence: |
| class_label: |
| names: |
| '0': N/A |
| '1': person |
| '2': bicycle |
| '3': car |
| '4': motorcycle |
| '5': airplane |
| '6': bus |
| '7': train |
| '8': truck |
| '9': boat |
| '10': traffic light |
| '11': fire hydrant |
| '12': street sign |
| '13': stop sign |
| '14': parking meter |
| '15': bench |
| '16': bird |
| '17': cat |
| '18': dog |
| '19': horse |
| '20': sheep |
| '21': cow |
| '22': elephant |
| '23': bear |
| '24': zebra |
| '25': giraffe |
| '26': hat |
| '27': backpack |
| '28': umbrella |
| '29': shoe |
| '30': eye glasses |
| '31': handbag |
| '32': tie |
| '33': suitcase |
| '34': frisbee |
| '35': skis |
| '36': snowboard |
| '37': sports ball |
| '38': kite |
| '39': baseball bat |
| '40': baseball glove |
| '41': skateboard |
| '42': surfboard |
| '43': tennis racket |
| '44': bottle |
| '45': plate |
| '46': wine glass |
| '47': cup |
| '48': fork |
| '49': knife |
| '50': spoon |
| '51': bowl |
| '52': banana |
| '53': apple |
| '54': sandwich |
| '55': orange |
| '56': broccoli |
| '57': carrot |
| '58': hot dog |
| '59': pizza |
| '60': donut |
| '61': cake |
| '62': chair |
| '63': couch |
| '64': potted plant |
| '65': bed |
| '66': mirror |
| '67': dining table |
| '68': window |
| '69': desk |
| '70': toilet |
| '71': door |
| '72': tv |
| '73': laptop |
| '74': mouse |
| '75': remote |
| '76': keyboard |
| '77': cell phone |
| '78': microwave |
| '79': oven |
| '80': toaster |
| '81': sink |
| '82': refrigerator |
| '83': blender |
| '84': book |
| '85': clock |
| '86': vase |
| '87': scissors |
| '88': teddy bear |
| '89': hair drier |
| '90': toothbrush |
| - name: bbox |
| sequence: |
| sequence: float64 |
| length: 4 |
| - name: iscrowd |
| sequence: int64 |
| - name: area |
| sequence: float64 |
| - name: height |
| dtype: int64 |
| - name: width |
| dtype: int64 |
| - name: file_name |
| dtype: string |
| - name: coco_url |
| dtype: string |
| - name: image_path |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 87231216 |
| num_examples: 117266 |
| - name: validation |
| num_bytes: 3692192 |
| num_examples: 4952 |
| download_size: 20405354669 |
| dataset_size: 90923408 |
| --- |
| ## Usage |
| For using the COCO dataset (2017), you need to download it manually first: |
| ```bash |
| 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 |
| ``` |
|
|
| Then to load the dataset: |
| ```python |
| import datasets |
| |
| COCO_DIR = ...(path to the downloaded dataset directory)... |
| ds = datasets.load_dataset( |
| "yonigozlan/coco_detection_dataset_script", |
| "2017", |
| data_dir=COCO_DIR, |
| trust_remote_code=True, |
| ) |
| ``` |
|
|
| ## Benchmarking |
| Here is an example of how to benchmark a 🤗 Transformers object detection model on the validation data of the COCO dataset: |
|
|
| ```python |
| import datasets |
| import torch |
| from PIL import Image |
| from torch.utils.data import DataLoader |
| from torchmetrics.detection.mean_ap import MeanAveragePrecision |
| from tqdm import tqdm |
| |
| from transformers import AutoImageProcessor, AutoModelForObjectDetection |
| |
| # prepare data |
| COCO_DIR = ...(path to the downloaded dataset directory)... |
| ds = datasets.load_dataset( |
| "yonigozlan/coco_detection_dataset_script", |
| "2017", |
| data_dir=COCO_DIR, |
| trust_remote_code=True, |
| ) |
| val_data = ds["validation"] |
| categories = val_data.features["objects"]["category_id"].feature.names |
| id2label = {index: x for index, x in enumerate(categories, start=0)} |
| label2id = {v: k for k, v in id2label.items()} |
| checkpoint = "facebook/detr-resnet-50" |
| |
| # load model and processor |
| model = AutoModelForObjectDetection.from_pretrained( |
| checkpoint, torch_dtype=torch.float16 |
| ).to("cuda") |
| id2label_model = model.config.id2label |
| processor = AutoImageProcessor.from_pretrained(checkpoint) |
| |
| |
| def collate_fn(batch): |
| data = {} |
| images = [Image.open(x["image_path"]).convert("RGB") for x in batch] |
| data["images"] = images |
| annotations = [] |
| for x in batch: |
| boxes = x["objects"]["bbox"] |
| # convert to xyxy format |
| boxes = [[box[0], box[1], box[0] + box[2], box[1] + box[3]] for box in boxes] |
| labels = x["objects"]["category_id"] |
| boxes = torch.tensor(boxes) |
| labels = torch.tensor(labels) |
| annotations.append({"boxes": boxes, "labels": labels}) |
| data["original_size"] = [(x["height"], x["width"]) for x in batch] |
| data["annotations"] = annotations |
| return data |
| |
| |
| # prepare dataloader |
| dataloader = DataLoader(val_data, batch_size=8, collate_fn=collate_fn) |
| |
| # prepare metric |
| metric = MeanAveragePrecision(box_format="xyxy", class_metrics=True) |
| |
| # evaluation loop |
| for i, batch in tqdm(enumerate(dataloader), total=len(dataloader)): |
| inputs = ( |
| processor(batch["images"], return_tensors="pt").to("cuda").to(torch.float16) |
| ) |
| with torch.no_grad(): |
| outputs = model(**inputs) |
| target_sizes = torch.tensor([x for x in batch["original_size"]]).to("cuda") |
| results = processor.post_process_object_detection( |
| outputs, threshold=0.0, target_sizes=target_sizes |
| ) |
| |
| # convert predicted label id to dataset label id |
| if len(id2label_model) != len(id2label): |
| for result in results: |
| result["labels"] = torch.tensor( |
| [label2id.get(id2label_model[x.item()], 0) for x in result["labels"]] |
| ) |
| # put results back to cpu |
| for result in results: |
| for k, v in result.items(): |
| if isinstance(v, torch.Tensor): |
| result[k] = v.to("cpu") |
| metric.update(results, batch["annotations"]) |
| |
| metrics = metric.compute() |
| print(metrics) |
| ``` |
