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--- |
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library_name: transformers |
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language: |
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- ru |
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license: apache-2.0 |
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base_model: PekingU/rtdetr_r50vd_coco_o365 |
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tags: |
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- object-detection |
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- pytorch-lightning |
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- russian-license-plates |
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- rt-detr |
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model-index: |
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- name: RT-DETR Russian car plate detection with classification by type fine tuned with pytorch lighting |
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results: [] |
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--- |
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## Model description |
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Модель детекции номерных знаков автомобилей РФ, в данный момент 2 класса n_p и p_p, обычные номера и полицейские |
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## Intended uses & limitations |
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Пример использования: |
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<pre> |
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from transformers import AutoModelForObjectDetection, AutoImageProcessor |
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import torch |
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import supervision as sv |
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DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model = AutoModelForObjectDetection.from_pretrained('Garon16/rtdetr_r50vd_russia_plate_detector_lightning').to(DEVICE) |
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processor = AutoImageProcessor.from_pretrained('Garon16/rtdetr_r50vd_russia_plate_detector_lightning') |
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path = 'path/to/image' |
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image = Image.open(path) |
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inputs = processor(image, return_tensors="pt").to(DEVICE) |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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w, h = image.size |
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results = processor.post_process_object_detection( |
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outputs, target_sizes=[(h, w)], threshold=0.3) |
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detections = sv.Detections.from_transformers(results[0]).with_nms(0.3) |
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labels = [ |
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model.config.id2label[class_id] |
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for class_id |
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in detections.class_id |
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] |
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annotated_image = image.copy() |
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annotated_image = sv.BoundingBoxAnnotator().annotate(annotated_image, detections) |
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annotated_image = sv.LabelAnnotator().annotate(annotated_image, detections, labels=labels) |
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grid = sv.create_tiles( |
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[annotated_image], |
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grid_size=(1, 1), |
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single_tile_size=(512, 512), |
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tile_padding_color=sv.Color.WHITE, |
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tile_margin_color=sv.Color.WHITE |
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) |
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sv.plot_image(grid, size=(10, 10)) |
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</pre> |
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## Training and evaluation data |
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Обучал на своём датасете - https://universe.roboflow.com/testcarplate/russian-license-plates-classification-by-this-type |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- seed: 42 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 300 |
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- num_epochs: 20 |
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### Training results |
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Пока не разобрался, как при дообучении лайтингом автоматом всё отправить сюда |
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### Framework versions |
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- Transformers 4.46.0.dev0 |
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- Pytorch 2.5.0+cu124 |
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- Tokenizers 0.20.1 |
