Yolos Small Finetuned License Plate Detection

🚀 YOLOS (small-sized) model

This is a fine - tuned object detection model for license plate and vehicle detection, offering high - performance results on specific datasets.

🚀 Quick Start

This model is a fine - tuned version of hustvl/yolos-small on the licesne-plate-recognition dataset from Roboflow. The training set contains 5200 images and the validation set has 380 images. The original YOLOS model was fine - tuned on COCO 2017 object detection (118k annotated images).

✨ Features

• Object Detection: Can be used for general object detection tasks.
• License Plate Detection: Specifically fine - tuned for license plate recognition.
• Vehicle Detection: Capable of detecting vehicles.
• Metrics: Evaluated using average precision, recall, and IOU.

📦 Installation

No specific installation steps are provided in the original document.

💻 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-finetuned-license-plate-detection')
model = YolosForObjectDetection.from_pretrained('nickmuchi/yolos-small-finetuned-license-plate-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.

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 [licesne - plate - recognition](https://app.roboflow.com/objectdetection - jhgr1/license - plates - recognition/2).

Evaluation results

This model achieves an AP (average precision) of 49.0.

Accumulating evaluation results...

IoU metric: bbox