README.md

---
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)
```