Edit model card

        language: ru
        tags:
        - object-detection
        - pytorch-lightning
        - russian-license-plates
        - rt-detr
        datasets:
        - testcarplate/russian-license-plates-classification-by-this-type
        metrics:
        - map
        pipeline_tag: object-detection
        ---
    
        # RT-DETR Russian car plate detection with classification by type
    
        This model was fine-tuned on Russian license plates dataset using PyTorch Lightning.
    
        ## Training metrics:
        - Final training loss: 1.7576
        - Final validation mAP: 0.8979
    
        ## Model description
        - Base model: PekingU/rtdetr_r50vd_coco_o365
        - Training epochs: 19
        - Dataset: Russian License Plates with type classification
    
        ## Usage
    
        ```python
        
        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))
    
        ```
    
        ## Training details
        The model was trained using PyTorch Lightning with the following configuration:
        - Batch size: 16
        - Learning rate: 5e-05
        - Optimizer: AdamW
        - Training device: <pytorch_lightning.accelerators.cuda.CUDAAccelerator object at 0x7edd6233bf50>
        - Number of GPUs: 1
    
        ## Limitations and bias
        This model is specifically trained on Russian license plates and may not perform well on license plates from other countries.
    
        ## Author
        [Garon16](https://huggingface.co/Garon16)
    
Downloads last month
5
Safetensors
Model size
42.9M params
Tensor type
F32
·
Inference API
Unable to determine this model's library. Check the docs .