readme: ---
  license: apache-2.0
  tags:
  - object-detection
  - license-plate-detection
  - vehicle-detection
  datasets:
  - coco
  - license-plate-detection
  widget:
  - src: https://drive.google.com/uc?id=1j9VZQ4NDS4gsubFf3m2qQoTMWLk552bQ
    example_title: "Skoda 1"
  - src: https://drive.google.com/uc?id=1p9wJIqRz3W50e2f_A0D8ftla8hoXz4T5
    example_title: "Skoda 2"
  metrics:
  - average precision
  - recall
  - IOU
  model-index:
  - name: yolos-small-rego-plates-detection
    results: []
  ---
  # YOLOS (small-sized) model
  
  The original YOLOS model was 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). 
  This model was further fine-tuned on the [license plate dataset]("https://www.kaggle.com/datasets/andrewmvd/car-plate-detection") from Kaggle. The dataset consists of 735 images of annotations categorised as "vehicle" and "license-plate". The model was trained for 200 epochs on a single GPU using Google Colab
  
  ## 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).
  ## 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 = 'https://drive.google.com/uc?id=1p9wJIqRz3W50e2f_A0D8ftla8hoXz4T5'
  image = Image.open(requests.get(url, stream=True).raw)
  feature_extractor = YolosFeatureExtractor.from_pretrained('nickmuchi/yolos-small-rego-plates-detection')
  model = YolosForObjectDetection.from_pretrained('nickmuchi/yolos-small-rego-plates-detection')
  inputs = feature_extractor(images=image, return_tensors="pt")
  outputs = model(**inputs)
  
  # model predicts bounding boxes and corresponding face mask detection 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
  
  This model was fine-tuned for 200 epochs on the [license plate dataset]("https://www.kaggle.com/datasets/andrewmvd/car-plate-detection").
  
  ## Evaluation results
  
  This model achieves an AP (average precision) of **47.9**.
  
  Accumulating evaluation results...
  
  IoU metric: bbox
  
  Metrics           | Metric Parameter      | Location    | Dets          | Value |
  ----------------  | --------------------- | ------------| ------------- | ----- |
  Average Precision | (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] | 0.479 |
  Average Precision | (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] | 0.752 |
  Average Precision | (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] | 0.555 |
  Average Precision | (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.147 |
  Average Precision | (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.420 |
  Average Precision | (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.804 |
  Average Recall    | (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] | 0.437 |
  Average Recall    | (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] | 0.641 |
  Average Recall    | (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] | 0.676 |
  Average Recall    | (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.268 |
  Average Recall    | (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.641 |
  Average Recall    | (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.870 |