Deformable DEtection TRansformer (DETR), single scale model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper
Deformable DETR: Deformable Transformers for End-to-End Object Detection by Zhu et al. and first released in
this repository.
Disclaimer: The team releasing Deformable DETR did not write a model card for this model so this model card has been written by the Hugging Face team.
The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100.
The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.
You can use the raw model for object detection. See the
model hub to look for all available Deformable DETR models.
1from transformers import AutoImageProcessor, DeformableDetrForObjectDetection
2import torch
3from PIL import Image
4import requests
5
6url = "http://images.cocodataset.org/val2017/000000039769.jpg"
7image = Image.open(requests.get(url, stream=True).raw)
8
9processor = AutoImageProcessor.from_pretrained("SenseTime/deformable-detr-single-scale")
10model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr-single-scale")
11
12inputs = processor(images=image, return_tensors="pt")
13outputs = model(**inputs)
14
15# convert outputs (bounding boxes and class logits) to COCO API
16# let's only keep detections with score > 0.7
17target_sizes = torch.tensor([image.size[::-1]])
18results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0]
19
20for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
21 box = [round(i, 2) for i in box.tolist()]
22 print(
23 f"Detected {model.config.id2label[label.item()]} with confidence "
24 f"{round(score.item(), 3)} at location {box}"
25 )
Currently, both the feature extractor and model support PyTorch.
The Deformable DETR model was trained on
COCO 2017 object detection, a dataset consisting of 118k/5k annotated images for training/validation respectively.
1@misc{https://doi.org/10.48550/arxiv.2010.04159,
2 doi = {10.48550/ARXIV.2010.04159},
3 url = {https://arxiv.org/abs/2010.04159},
4 author = {Zhu, Xizhou and Su, Weijie and Lu, Lewei and Li, Bin and Wang, Xiaogang and Dai, Jifeng},
5 keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
6 title = {Deformable DETR: Deformable Transformers for End-to-End Object Detection},
7 publisher = {arXiv},
8 year = {2020},
9 copyright = {arXiv.org perpetual, non-exclusive license}
10}
