readme: ---
  license: apache-2.0
  tags:
  - object-detection
  - vision
  datasets:
  - coco
  widget:
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg
    example_title: Savanna
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
    example_title: Football Match
  - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg
    example_title: Airport
  ---
  
  # YOLOS (small-sized) model (300 pre-train epochs)
  
  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). 
  
  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.
  
  ## Model description
  
  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).
  
  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.
  
  ## Intended uses & limitations
  
  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.
  
  ### How to use
  
  Here is how to use this model:
  
  ```python
  from transformers import YolosFeatureExtractor, YolosForObjectDetection
  from PIL import Image
  import requests
  
  url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
  image = Image.open(requests.get(url, stream=True).raw)
  
  feature_extractor = YolosFeatureExtractor.from_pretrained('hustvl/yolos-small-300')
  model = YolosForObjectDetection.from_pretrained('hustvl/yolos-small-300')
  
  inputs = feature_extractor(images=image, return_tensors="pt")
  outputs = model(**inputs)
  
  # model predicts bounding boxes and corresponding COCO classes
  logits = outputs.logits
  bboxes = outputs.pred_boxes
  ```
  
  Currently, both the feature extractor and model support PyTorch. 
  
  ## Training data
  
  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. 
  
  ### Training
  
  The model was pre-trained for 300 epochs on ImageNet-1k and fine-tuned for 150 epochs on COCO.
  
  ## Evaluation results
  
  This model achieves an AP (average precision) of **36.1** on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper.
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @article{DBLP:journals/corr/abs-2106-00666,
    author    = {Yuxin Fang and
                 Bencheng Liao and
                 Xinggang Wang and
                 Jiemin Fang and
                 Jiyang Qi and
                 Rui Wu and
                 Jianwei Niu and
                 Wenyu Liu},
    title     = {You Only Look at One Sequence: Rethinking Transformer in Vision through
                 Object Detection},
    journal   = {CoRR},
    volume    = {abs/2106.00666},
    year      = {2021},
    url       = {https://arxiv.org/abs/2106.00666},
    eprinttype = {arXiv},
    eprint    = {2106.00666},
    timestamp = {Fri, 29 Apr 2022 19:49:16 +0200},
    biburl    = {https://dblp.org/rec/journals/corr/abs-2106-00666.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  ```