Delete README.Md

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 |