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--- |
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library_name: transformers |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: object-detection |
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tags: |
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- object-detection |
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- vision |
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datasets: |
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- coco |
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widget: |
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- src: >- |
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https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg |
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example_title: Savanna |
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- src: >- |
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https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg |
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example_title: Football Match |
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- src: >- |
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https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg |
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example_title: Airport |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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The YOLO series has become the most popular framework for real-time object detection due to its reasonable trade-off between speed and accuracy. |
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However, we observe that the speed and accuracy of YOLOs are negatively affected by the NMS. |
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Recently, end-to-end Transformer-based detectors (DETRs) have provided an alternative to eliminating NMS. |
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Nevertheless, the high computational cost limits their practicality and hinders them from fully exploiting the advantage of excluding NMS. |
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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. |
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We build RT-DETR in two steps, drawing on the advanced DETR: |
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first we focus on maintaining accuracy while improving speed, followed by maintaining speed while improving accuracy. |
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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. |
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Then, we propose the uncertainty-minimal query selection to provide high-quality initial queries to the decoder, thereby improving accuracy. |
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In addition, RT-DETR supports flexible speed tuning by adjusting the number of decoder layers to adapt to various scenarios without retraining. |
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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. |
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We also develop scaled RT-DETRs that outperform the lighter YOLO detectors (S and M models). |
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Furthermore, RT-DETR-R50 outperforms DINO-R50 by 2.2% AP in accuracy and about 21 times in FPS. |
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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/). |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** Yian Zhao and Sangbum Choi |
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- **Funded by [optional]:** National Key R&D Program of China (No.2022ZD0118201), Natural Science Foundation of China (No.61972217, 32071459, 62176249, 62006133, 62271465), |
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and the Shenzhen Medical Research Funds in China (No. |
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B2302037). |
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- **Shared by [optional]:** Sangbum Choi |
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- **Model type:** |
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- **Language(s) (NLP):** |
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- **License:** Apache-2.0 |
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- **Finetuned from model [optional]:** |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/lyuwenyu/RT-DETR |
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- **Paper [optional]:** https://arxiv.org/abs/2304.08069 |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=rtdetr) to look for all available RTDETR models. |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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``` |
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import torch |
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import requests |
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from PIL import Image |
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from transformers import RTDetrForObjectDetection, RTDetrImageProcessor |
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url = 'http://images.cocodataset.org/val2017/000000039769.jpg' |
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image = Image.open(requests.get(url, stream=True).raw) |
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image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_r101vd_coco_o365") |
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model = RTDetrForObjectDetection.from_pretrained("PekingU/rtdetr_r101vd_coco_o365") |
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inputs = image_processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3) |
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for result in results: |
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for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]): |
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score, label = score.item(), label_id.item() |
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box = [round(i, 2) for i in box.tolist()] |
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print(f"{model.config.id2label[label]}: {score:.2f} {box}") |
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``` |
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## Training Details |
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### Training Data |
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<!-- 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. --> |
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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. |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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We conduct experiments on |
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COCO [20] and Objects365 [35], where RT-DETR is trained |
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on COCO train2017 and validated on COCO val2017 |
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dataset. We report the standard COCO metrics, including |
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AP (averaged over uniformly sampled IoU thresholds ranging from 0.50-0.95 with a step size of 0.05), AP50, AP75, as |
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well as AP at different scales: APS, APM, APL. |
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#### Preprocessing [optional] |
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Images are resized/rescaled such that the shortest side is at 640 pixels. |
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#### Training Hyperparameters |
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- **Training regime:** <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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This model achieves an AP (average precision) of 53.1 on COCO 2017 validation. For more details regarding evaluation results, we refer to table 2 of the original paper. |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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### Results |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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). |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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### Compute Infrastructure |
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#### Hardware |
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#### Software |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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```bibtex |
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@misc{lv2023detrs, |
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title={DETRs Beat YOLOs on Real-time Object Detection}, |
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author={Yian Zhao and Wenyu Lv and Shangliang Xu and Jinman Wei and Guanzhong Wang and Qingqing Dang and Yi Liu and Jie Chen}, |
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year={2023}, |
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eprint={2304.08069}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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**APA:** |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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## More Information [optional] |
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## Model Card Authors [optional] |
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[Sangbum Choi](https://huggingface.co/danelcsb) |
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## Model Card Contact |
