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README.md

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+---
+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=1VwYLbGak5c-2P5qdvfWVOeg7DTDYPbro
+  example_title: "City"
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
+  example_title: "Parking"
+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=1VwYLbGak5c-2P5qdvfWVOeg7DTDYPbro'
+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-finetuned-masks')
+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 |