Rtdetr R50vd Russia Plate Detector

🚀 RT-DETR Russian car plate detection with classification by type

This model is a fine - tuned version of PekingU/rtdetr_r50vd_coco_o365 on an unknown dataset. It can effectively detect Russian car plates and classify them by type, providing accurate results for related applications.

🚀 Quick Start

This model is a fine-tuned version of PekingU/rtdetr_r50vd_coco_o365 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 4.1673
• Map: 0.8829
• Map 50: 0.9858
• Map 75: 0.9736
• Map Car-plates-and-these-types: -1.0
• Map Large: 0.9689
• Map Medium: 0.9125
• Map N P: 0.857
• Map P P: 0.9087
• Map Small: 0.696
• Mar 1: 0.8686
• Mar 10: 0.9299
• Mar 100: 0.9357
• Mar 100 Car-plates-and-these-types: -1.0
• Mar 100 N P: 0.9169
• Mar 100 P P: 0.9545
• Mar Large: 0.9844
• Mar Medium: 0.958
• Mar Small: 0.8354

✨ Features

• Fine - tuned Model: Based on the pre - trained model [PekingU/rtdetr_r50vd_coco_o365], fine - tuned for Russian car plate detection and type classification.
• Accurate Results: Achieves high accuracy on multiple evaluation metrics, such as Map, Map 50, etc.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForObjectDetection, AutoImageProcessor
import torch
import supervision as sv


DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModelForObjectDetection.from_pretrained('Garon16/rtdetr_r50vd_russia_plate_detector').to(DEVICE)
processor = AutoImageProcessor.from_pretrained('Garon16/rtdetr_r50vd_russia_plate_detector')

path = 'path/to/image'
image = Image.open(path)
inputs = processor(image, return_tensors="pt").to(DEVICE)
with torch.no_grad():
    outputs = model(**inputs)
w, h = image.size
results = processor.post_process_object_detection(
    outputs, target_sizes=[(h, w)], threshold=0.3)
detections = sv.Detections.from_transformers(results[0]).with_nms(0.3)
labels = [
    model.config.id2label[class_id]
    for class_id
    in detections.class_id
]

annotated_image = image.copy()
annotated_image = sv.BoundingBoxAnnotator().annotate(annotated_image, detections)
annotated_image = sv.LabelAnnotator().annotate(annotated_image, detections, labels=labels)
  
grid = sv.create_tiles(
  [annotated_image],
  grid_size=(1, 1),
  single_tile_size=(512, 512),
  tile_padding_color=sv.Color.WHITE,
  tile_margin_color=sv.Color.WHITE
)
sv.plot_image(grid, size=(10, 10))

📚 Documentation

Model description

This model is designed for detecting Russian car plates. Currently, it has two classes: n_p (ordinary license plates) and p_p (police license plates).

Intended uses & limitations

The provided code example demonstrates how to use the model for object detection on an image. You can adjust the parameters according to your specific needs.

Training and evaluation data

The model was trained on a custom dataset - https://universe.roboflow.com/testcarplate/russian-license-plates-classification-by-this-type

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 300
• num_epochs: 20

Training results

Framework versions

• Transformers 4.46.0.dev0
• Pytorch 2.5.0+cu124
• Tokenizers 0.20.1

📄 License

This project is licensed under the apache - 2.0 license.