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--- |
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license: apache-2.0 |
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tags: |
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- object-detection |
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datasets: |
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- coco |
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widget: |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg |
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example_title: Savanna |
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- src: 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: 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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# YOLOS (tiny-sized) model |
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YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](https://github.com/hustvl/YOLOS). |
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Disclaimer: The team releasing YOLOS did not write a model card for this model so this model card has been written by the Hugging Face team. |
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## Model description |
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YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN). |
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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. |
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## Intended uses & limitations |
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You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=hustvl/yolos) to look for all available YOLOS models. |
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### How to use |
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Here is how to use this model: |
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```python |
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from transformers import YolosFeatureExtractor, YolosForObjectDetection |
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from PIL import Image |
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import requests |
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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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feature_extractor = YolosFeatureExtractor.from_pretrained('hustvl/yolos-tiny') |
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model = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny') |
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inputs = feature_extractor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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# model predicts bounding boxes and corresponding COCO classes |
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logits = outputs.logits |
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bboxes = outputs.pred_boxes |
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``` |
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Currently, both the feature extractor and model support PyTorch. |
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## Training data |
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The YOLOS model was pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet2012) and fine-tuned 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 |
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The model was pre-trained for 300 epochs on ImageNet-1k and fine-tuned for 300 epochs on COCO. |
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## Evaluation results |
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This model achieves an AP (average precision) of **28.7** on COCO 2017 validation. For more details regarding evaluation results, we refer to the original paper. |
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### BibTeX entry and citation info |
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```bibtex |
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@article{DBLP:journals/corr/abs-2106-00666, |
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author = {Yuxin Fang and |
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Bencheng Liao and |
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Xinggang Wang and |
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Jiemin Fang and |
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Jiyang Qi and |
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Rui Wu and |
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Jianwei Niu and |
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Wenyu Liu}, |
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title = {You Only Look at One Sequence: Rethinking Transformer in Vision through |
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Object Detection}, |
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journal = {CoRR}, |
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volume = {abs/2106.00666}, |
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year = {2021}, |
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url = {https://arxiv.org/abs/2106.00666}, |
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eprinttype = {arXiv}, |
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eprint = {2106.00666}, |
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timestamp = {Fri, 29 Apr 2022 19:49:16 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2106-00666.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
