Files changed (1) hide show
  1. README.md +125 -132
README.md CHANGED
@@ -1,77 +1,119 @@
1
  ---
2
  library_name: transformers
3
- tags: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4
  ---
5
 
6
- # Model Card for Model ID
7
 
8
- <!-- Provide a quick summary of what the model is/does. -->
9
 
10
 
 
11
 
12
- ## Model Details
13
-
14
- ### Model Description
15
-
16
- <!-- Provide a longer summary of what this model is. -->
17
-
18
- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
19
-
20
- - **Developed by:** [More Information Needed]
21
- - **Funded by [optional]:** [More Information Needed]
22
- - **Shared by [optional]:** [More Information Needed]
23
- - **Model type:** [More Information Needed]
24
- - **Language(s) (NLP):** [More Information Needed]
25
- - **License:** [More Information Needed]
26
- - **Finetuned from model [optional]:** [More Information Needed]
27
-
28
- ### Model Sources [optional]
29
 
30
- <!-- Provide the basic links for the model. -->
31
-
32
- - **Repository:** [More Information Needed]
33
- - **Paper [optional]:** [More Information Needed]
34
- - **Demo [optional]:** [More Information Needed]
35
 
36
- ## Uses
37
 
38
- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
39
 
40
- ### Direct Use
 
 
 
 
 
 
 
 
 
 
 
 
 
41
 
42
- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
43
 
44
- [More Information Needed]
45
 
46
- ### Downstream Use [optional]
47
 
48
- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
 
 
 
 
 
49
 
50
- [More Information Needed]
51
 
52
- ### Out-of-Scope Use
53
 
54
- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
 
 
55
 
56
- [More Information Needed]
57
 
58
- ## Bias, Risks, and Limitations
59
 
60
- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
 
61
 
62
- [More Information Needed]
 
63
 
64
- ### Recommendations
 
65
 
66
- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
67
 
68
- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
69
 
70
- ## How to Get Started with the Model
 
71
 
72
- Use the code below to get started with the model.
73
 
74
- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
75
 
76
  ## Training Details
77
 
@@ -79,121 +121,72 @@ Use the code below to get started with the model.
79
 
80
  <!-- 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. -->
81
 
82
- [More Information Needed]
83
 
84
  ### Training Procedure
85
 
86
  <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
87
 
88
- #### Preprocessing [optional]
89
-
90
- [More Information Needed]
91
 
 
92
 
93
- #### Training Hyperparameters
94
 
95
- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
96
 
97
- #### Speeds, Sizes, Times [optional]
98
 
99
- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
100
 
101
- [More Information Needed]
102
 
103
  ## Evaluation
104
 
105
- <!-- This section describes the evaluation protocols and provides the results. -->
106
-
107
- ### Testing Data, Factors & Metrics
108
-
109
- #### Testing Data
110
-
111
- <!-- This should link to a Dataset Card if possible. -->
112
-
113
- [More Information Needed]
114
-
115
- #### Factors
116
-
117
- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
118
-
119
- [More Information Needed]
120
-
121
- #### Metrics
122
-
123
- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
124
 
125
- [More Information Needed]
 
 
 
 
 
 
 
 
126
 
127
- ### Results
128
 
129
- [More Information Needed]
130
-
131
- #### Summary
132
-
133
-
134
-
135
- ## Model Examination [optional]
136
-
137
- <!-- Relevant interpretability work for the model goes here -->
138
-
139
- [More Information Needed]
140
-
141
- ## Environmental Impact
142
-
143
- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
144
-
145
- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
146
-
147
- - **Hardware Type:** [More Information Needed]
148
- - **Hours used:** [More Information Needed]
149
- - **Cloud Provider:** [More Information Needed]
150
- - **Compute Region:** [More Information Needed]
151
- - **Carbon Emitted:** [More Information Needed]
152
-
153
- ## Technical Specifications [optional]
154
 
155
  ### Model Architecture and Objective
156
 
157
- [More Information Needed]
158
-
159
- ### Compute Infrastructure
160
 
161
- [More Information Needed]
 
 
 
 
162
 
163
- #### Hardware
164
 
165
- [More Information Needed]
166
-
167
- #### Software
168
-
169
- [More Information Needed]
170
-
171
- ## Citation [optional]
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
- [More Information Needed]
178
-
179
- **APA:**
180
-
181
- [More Information Needed]
182
-
183
- ## Glossary [optional]
184
-
185
- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
186
-
187
- [More Information Needed]
188
-
189
- ## More Information [optional]
190
-
191
- [More Information Needed]
192
-
193
- ## Model Card Authors [optional]
194
 
195
- [More Information Needed]
196
 
197
- ## Model Card Contact
 
198
 
199
- [More Information Needed]
 
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_r101vd")
94
+ model = RTDetrForObjectDetection.from_pretrained("PekingU/rtdetr_r101vd")
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
 
 
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