README.md
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1 ---
2 library_name: transformers
3 license: apache-2.0
4 language:
5 - en
6 pipeline_tag: object-detection
7 tags:
8 - object-detection
9 - vision
10 datasets:
11 - coco
12 widget:
13 - src: >-
14 https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg
15 example_title: Savanna
16 - src: >-
17 https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
18 example_title: Football Match
19 - src: >-
20 https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg
21 example_title: Airport
22 ---
23
24
25 # Model Card for RT-DETR
26
27
28 ## Table of Contents
29
30 1. [Model Details](#model-details)
31 2. [Model Sources](#model-sources)
32 3. [How to Get Started with the Model](#how-to-get-started-with-the-model)
33 4. [Training Details](#training-details)
34 5. [Evaluation](#evaluation)
35 6. [Model Architecture and Objective](#model-architecture-and-objective)
36 7. [Citation](#citation)
37
38
39 ## Model Details
40
41 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6579e0eaa9e58aec614e9d97/WULSDLsCVs7RNEs9KB0Lr.png)
42
43 > The YOLO series has become the most popular framework for real-time object detection due to its reasonable trade-off between speed and accuracy.
44 However, we observe that the speed and accuracy of YOLOs are negatively affected by the NMS.
45 Recently, end-to-end Transformer-based detectors (DETRs) have provided an alternative to eliminating NMS.
46 Nevertheless, the high computational cost limits their practicality and hinders them from fully exploiting the advantage of excluding NMS.
47 In this paper, we propose the Real-Time DEtection TRansformer (RT-DETR), the first real-time end-to-end object detector to our best knowledge that addresses the above dilemma.
48 We build RT-DETR in two steps, drawing on the advanced DETR:
49 first we focus on maintaining accuracy while improving speed, followed by maintaining speed while improving accuracy.
50 Specifically, we design an efficient hybrid encoder to expeditiously process multi-scale features by decoupling intra-scale interaction and cross-scale fusion to improve speed.
51 Then, we propose the uncertainty-minimal query selection to provide high-quality initial queries to the decoder, thereby improving accuracy.
52 In addition, RT-DETR supports flexible speed tuning by adjusting the number of decoder layers to adapt to various scenarios without retraining.
53 Our RT-DETR-R50 / R101 achieves 53.1% / 54.3% AP on COCO and 108 / 74 FPS on T4 GPU, outperforming previously advanced YOLOs in both speed and accuracy.
54 We also develop scaled RT-DETRs that outperform the lighter YOLO detectors (S and M models).
55 Furthermore, RT-DETR-R50 outperforms DINO-R50 by 2.2% AP in accuracy and about 21 times in FPS.
56 After pre-training with Objects365, RT-DETR-R50 / R101 achieves 55.3% / 56.2% AP. The project page: this [https URL](https://zhao-yian.github.io/RTDETR/).
57
58
59
60 This is the model card of a 🤗 [transformers](https://huggingface.co/docs/transformers/index) model that has been pushed on the Hub.
61
62 - **Developed by:** Yian Zhao and Sangbum Choi
63 - **Funded by:** National Key R&D Program of China (No.2022ZD0118201), Natural Science Foundation of China (No.61972217, 32071459, 62176249, 62006133, 62271465),
64 and the Shenzhen Medical Research Funds in China (No.
65 B2302037).
66 - **Shared by:** Sangbum Choi
67 - **Model type:** [RT-DETR](https://huggingface.co/docs/transformers/main/en/model_doc/rt_detr)
68 - **License:** Apache-2.0
69
70 ### Model Sources
71
72 <!-- Provide the basic links for the model. -->
73
74 - **HF Docs:** [RT-DETR](https://huggingface.co/docs/transformers/main/en/model_doc/rt_detr)
75 - **Repository:** https://github.com/lyuwenyu/RT-DETR
76 - **Paper:** https://arxiv.org/abs/2304.08069
77 - **Demo:** [RT-DETR Tracking](https://huggingface.co/spaces/merve/RT-DETR-tracking-coco)
78
79 ## How to Get Started with the Model
80
81 Use the code below to get started with the model.
82
83 ```python
84 import torch
85 import requests
86
87 from PIL import Image
88 from transformers import RTDetrForObjectDetection, RTDetrImageProcessor
89
90 url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
91 image = Image.open(requests.get(url, stream=True).raw)
92
93 image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_r18vd_coco_o365")
94 model = RTDetrForObjectDetection.from_pretrained("PekingU/rtdetr_r18vd_coco_o365")
95
96 inputs = image_processor(images=image, return_tensors="pt")
97
98 with torch.no_grad():
99 outputs = model(**inputs)
100
101 results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)
102
103 for result in results:
104 for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
105 score, label = score.item(), label_id.item()
106 box = [round(i, 2) for i in box.tolist()]
107 print(f"{model.config.id2label[label]}: {score:.2f} {box}")
108 ```
109 This should output
110 ```
111 sofa: 0.97 [0.14, 0.38, 640.13, 476.21]
112 cat: 0.96 [343.38, 24.28, 640.14, 371.5]
113 cat: 0.96 [13.23, 54.18, 318.98, 472.22]
114 remote: 0.95 [40.11, 73.44, 175.96, 118.48]
115 remote: 0.92 [333.73, 76.58, 369.97, 186.99]
116 ```
117
118 ## Training Details
119
120 ### Training Data
121
122 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
123
124 The RTDETR model was trained on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively.
125
126 ### Training Procedure
127
128 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
129
130 We conduct experiments on COCO and Objects365 datasets, where RT-DETR is trained on COCO train2017 and validated on COCO val2017 dataset.
131 We report the standard COCO metrics, including AP (averaged over uniformly sampled IoU thresholds ranging from 0.50-0.95 with a step size of 0.05),
132 AP50, AP75, as well as AP at different scales: APS, APM, APL.
133
134 ### Preprocessing
135
136 Images are resized to 640x640 pixels and rescaled with `image_mean=[0.485, 0.456, 0.406]` and `image_std=[0.229, 0.224, 0.225]`.
137
138 ### Training Hyperparameters
139
140 - **Training regime:** <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
141
142 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6579e0eaa9e58aec614e9d97/E15I9MwZCtwNIms-W8Ra9.png)
143
144
145 ## Evaluation
146
147
148 | Model | #Epochs | #Params (M) | GFLOPs | FPS_bs=1 | AP (val) | AP50 (val) | AP75 (val) | AP-s (val) | AP-m (val) | AP-l (val) |
149 |----------------------------|---------|-------------|--------|----------|--------|-----------|-----------|----------|----------|----------|
150 | RT-DETR-R18 | 72 | 20 | 60.7 | 217 | 46.5 | 63.8 | 50.4 | 28.4 | 49.8 | 63.0 |
151 | RT-DETR-R34 | 72 | 31 | 91.0 | 172 | 48.5 | 66.2 | 52.3 | 30.2 | 51.9 | 66.2 |
152 | RT-DETR R50 | 72 | 42 | 136 | 108 | 53.1 | 71.3 | 57.7 | 34.8 | 58.0 | 70.0 |
153 | RT-DETR R101| 72 | 76 | 259 | 74 | 54.3 | 72.7 | 58.6 | 36.0 | 58.8 | 72.1 |
154 | RT-DETR-R18 (Objects 365 pretrained) | 60 | 20 | 61 | 217 | 49.2 | 66.6 | 53.5 | 33.2 | 52.3 | 64.8 |
155 | RT-DETR-R50 (Objects 365 pretrained) | 24 | 42 | 136 | 108 | 55.3 | 73.4 | 60.1 | 37.9 | 59.9 | 71.8 |
156 | RT-DETR-R101 (Objects 365 pretrained) | 24 | 76 | 259 | 74 | 56.2 | 74.6 | 61.3 | 38.3 | 60.5 | 73.5 |
157
158
159
160 ### Model Architecture and Objective
161
162 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6579e0eaa9e58aec614e9d97/sdIwTRlHNwPzyBNwHja60.png)
163
164 Overview of RT-DETR. We feed the features from the last three stages of the backbone into the encoder. The efficient hybrid
165 encoder transforms multi-scale features into a sequence of image features through the Attention-based Intra-scale Feature Interaction (AIFI)
166 and the CNN-based Cross-scale Feature Fusion (CCFF). Then, the uncertainty-minimal query selection selects a fixed number of encoder
167 features to serve as initial object queries for the decoder. Finally, the decoder with auxiliary prediction heads iteratively optimizes object
168 queries to generate categories and boxes.
169
170
171 ## Citation
172
173 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
174
175 **BibTeX:**
176
177 ```bibtex
178 @misc{lv2023detrs,
179 title={DETRs Beat YOLOs on Real-time Object Detection},
180 author={Yian Zhao and Wenyu Lv and Shangliang Xu and Jinman Wei and Guanzhong Wang and Qingqing Dang and Yi Liu and Jie Chen},
181 year={2023},
182 eprint={2304.08069},
183 archivePrefix={arXiv},
184 primaryClass={cs.CV}
185 }
186 ```
187
188 ## Model Card Authors
189
190 [Sangbum Choi](https://huggingface.co/danelcsb)
191 [Pavel Iakubovskii](https://huggingface.co/qubvel-hf)
192
193