Yolos Small Rego Plates Detection

🚀 YOLOS (small-sized) model

The YOLOS (small-sized) model is fine - tuned for object detection, especially license plate detection, based on the original YOLOS architecture.

🚀 Quick Start

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 by Fang et al. and first released in this repository. This model was further fine - tuned on the license plate dataset 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.

✨ Features

• Fine - tuned on COCO 2017 object detection dataset.
• Further fine - tuned on a license plate detection dataset.
• Capable of object detection, especially for vehicles and license plates.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

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.

📚 Documentation

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 to look for all available YOLOS models.

🔧 Technical Details

Training data

The YOLOS model was pre - trained on ImageNet - 1k and fine - tuned on COCO 2017 object detection, 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.

Evaluation results

This model achieves an AP (average precision) of 47.9.

Accumulating evaluation results...

📄 License

The model is licensed under the Apache 2.0 license.