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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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- license-plate-detection |
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- vehicle-detection |
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datasets: |
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- coco |
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- license-plate-detection |
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widget: |
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- src: https://drive.google.com/uc?id=1VwYLbGak5c-2P5qdvfWVOeg7DTDYPbro |
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example_title: "City" |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg |
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example_title: "Parking" |
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metrics: |
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- average precision |
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- recall |
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- IOU |
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model-index: |
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- name: yolos-small-rego-plates-detection |
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results: [] |
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--- |
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# YOLOS (small-sized) model |
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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). |
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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 |
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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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## 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 = 'https://drive.google.com/uc?id=1VwYLbGak5c-2P5qdvfWVOeg7DTDYPbro' |
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image = Image.open(requests.get(url, stream=True).raw) |
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feature_extractor = YolosFeatureExtractor.from_pretrained('nickmuchi/yolos-small-rego-plates-detection') |
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model = YolosForObjectDetection.from_pretrained('nickmuchi/yolos-small-finetuned-masks') |
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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 face mask detection 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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This model was fine-tuned for 200 epochs on the [license plate dataset]("https://www.kaggle.com/datasets/andrewmvd/car-plate-detection"). |
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## Evaluation results |
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This model achieves an AP (average precision) of **47.9**. |
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Accumulating evaluation results... |
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IoU metric: bbox |
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Metrics | Metric Parameter | Location | Dets | Value | |
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---------------- | --------------------- | ------------| ------------- | ----- | |
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Average Precision | (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] | 0.479 | |
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Average Precision | (AP) @[ IoU=0.50 | area= all | maxDets=100 ] | 0.752 | |
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Average Precision | (AP) @[ IoU=0.75 | area= all | maxDets=100 ] | 0.555 | |
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Average Precision | (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.147 | |
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Average Precision | (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.420 | |
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Average Precision | (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.804 | |
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Average Recall | (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] | 0.437 | |
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Average Recall | (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] | 0.641 | |
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Average Recall | (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] | 0.676 | |
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Average Recall | (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.268 | |
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Average Recall | (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.641 | |
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Average Recall | (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.870 | |
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