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---
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)
``` |