Datasets:

yonigozlan
/

coco_detection_dataset_script

	---
	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)
	```