Dataset Viewer
Full Screen Viewer
Full Screen
The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider
removing the
loading script
and relying on
automated data support
(you can use
convert_to_parquet
from the datasets
library). If this is not possible, please
open a discussion
for direct help.
Usage
For using the COCO dataset (2017), you need to download it manually first:
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:
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:
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)
- Downloads last month
- 38