--- library_name: transformers language: - ru license: apache-2.0 base_model: PekingU/rtdetr_r50vd_coco_o365 tags: - generated_from_trainer model-index: - name: RT-DETR Russian car plate detection with classification by type results: [] --- # RT-DETR Russian car plate detection with classification by type This model is a fine-tuned version of [PekingU/rtdetr_r50vd_coco_o365](https://huggingface.co/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 ## Model description Модель детекции номерных знаков автомобилей РФ, в данный момент 2 класса n_p и p_p, обычные номера и полицейские ## Intended uses & limitations Пример использования: 
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 and evaluation data Обучал на своём датасете - 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 | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Car-plates-and-these-types | Map Large | Map Medium | Map N P | Map P P | Map Small | Mar 1 | Mar 10 | Mar 100 | Mar 100 Car-plates-and-these-types | Mar 100 N P | Mar 100 P P | Mar Large | Mar Medium | Mar Small | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:------------------------------:|:---------:|:----------:|:-------:|:-------:|:---------:|:------:|:------:|:-------:|:----------------------------------:|:-----------:|:-----------:|:---------:|:----------:|:---------:| | No log | 1.0 | 109 | 64.6127 | 0.035 | 0.0558 | 0.0379 | -1.0 | 0.0039 | 0.0663 | 0.0191 | 0.0508 | 0.0071 | 0.1523 | 0.3009 | 0.3361 | -1.0 | 0.3179 | 0.3543 | 0.7625 | 0.3788 | 0.1157 | | No log | 2.0 | 218 | 15.4008 | 0.8237 | 0.9418 | 0.9327 | -1.0 | 0.893 | 0.879 | 0.7945 | 0.8529 | 0.4319 | 0.8203 | 0.8924 | 0.9018 | -1.0 | 0.8766 | 0.9269 | 0.9656 | 0.9324 | 0.7653 | | No log | 3.0 | 327 | 9.4050 | 0.8439 | 0.9566 | 0.9479 | -1.0 | 0.9439 | 0.8908 | 0.8158 | 0.872 | 0.5171 | 0.8416 | 0.908 | 0.9144 | -1.0 | 0.9002 | 0.9286 | 0.9781 | 0.9368 | 0.8051 | | No log | 4.0 | 436 | 7.9164 | 0.8493 | 0.9665 | 0.9543 | -1.0 | 0.9567 | 0.8903 | 0.8338 | 0.8648 | 0.5581 | 0.8481 | 0.9159 | 0.9267 | -1.0 | 0.9173 | 0.936 | 0.975 | 0.949 | 0.8185 | | 70.2867 | 5.0 | 545 | 6.8177 | 0.8525 | 0.9723 | 0.9602 | -1.0 | 0.9521 | 0.8918 | 0.8234 | 0.8816 | 0.6025 | 0.8438 | 0.9214 | 0.9279 | -1.0 | 0.9181 | 0.9378 | 0.975 | 0.9492 | 0.8211 | | 70.2867 | 6.0 | 654 | 6.0182 | 0.854 | 0.9744 | 0.9619 | -1.0 | 0.9574 | 0.8912 | 0.8251 | 0.8829 | 0.6123 | 0.8438 | 0.9176 | 0.927 | -1.0 | 0.9137 | 0.9403 | 0.9781 | 0.9503 | 0.8163 | | 70.2867 | 7.0 | 763 | 5.4024 | 0.8731 | 0.9772 | 0.9667 | -1.0 | 0.9635 | 0.9113 | 0.8462 | 0.9001 | 0.6376 | 0.8608 | 0.9275 | 0.9336 | -1.0 | 0.9202 | 0.9471 | 0.9781 | 0.956 | 0.8266 | | 70.2867 | 8.0 | 872 | 5.2224 | 0.8726 | 0.9809 | 0.9767 | -1.0 | 0.9582 | 0.9069 | 0.8487 | 0.8966 | 0.6472 | 0.8625 | 0.9265 | 0.9301 | -1.0 | 0.9137 | 0.9464 | 0.9875 | 0.9528 | 0.8232 | | 70.2867 | 9.0 | 981 | 4.7844 | 0.8679 | 0.9821 | 0.9687 | -1.0 | 0.9574 | 0.9023 | 0.8451 | 0.8907 | 0.6382 | 0.8606 | 0.9213 | 0.9283 | -1.0 | 0.9119 | 0.9448 | 0.9844 | 0.952 | 0.8165 | | 4.2466 | 10.0 | 1090 | 5.1437 | 0.8729 | 0.9816 | 0.9762 | -1.0 | 0.9577 | 0.9028 | 0.8448 | 0.901 | 0.6686 | 0.8605 | 0.9296 | 0.9359 | -1.0 | 0.9203 | 0.9514 | 0.9781 | 0.9567 | 0.8413 | | 4.2466 | 11.0 | 1199 | 4.5169 | 0.8858 | 0.9828 | 0.9768 | -1.0 | 0.9707 | 0.9162 | 0.8628 | 0.9087 | 0.6734 | 0.8695 | 0.9264 | 0.931 | -1.0 | 0.9121 | 0.95 | 0.9781 | 0.9538 | 0.823 | | 4.2466 | 12.0 | 1308 | 4.5858 | 0.8813 | 0.9865 | 0.9744 | -1.0 | 0.9623 | 0.9126 | 0.8585 | 0.9041 | 0.6815 | 0.8671 | 0.9308 | 0.9355 | -1.0 | 0.9185 | 0.9526 | 0.9812 | 0.9583 | 0.8308 | | 4.2466 | 13.0 | 1417 | 4.5345 | 0.8778 | 0.9843 | 0.9726 | -1.0 | 0.957 | 0.9101 | 0.8526 | 0.903 | 0.6754 | 0.8628 | 0.9281 | 0.9335 | -1.0 | 0.9158 | 0.9512 | 0.9812 | 0.9557 | 0.8314 | | 3.589 | 14.0 | 1526 | 4.3003 | 0.8885 | 0.9857 | 0.9759 | -1.0 | 0.9656 | 0.9189 | 0.8642 | 0.9128 | 0.6957 | 0.8724 | 0.9334 | 0.9375 | -1.0 | 0.9194 | 0.9555 | 0.9875 | 0.959 | 0.8375 | | 3.589 | 15.0 | 1635 | 4.3999 | 0.8819 | 0.986 | 0.9741 | -1.0 | 0.9606 | 0.9118 | 0.8575 | 0.9064 | 0.6892 | 0.8659 | 0.9283 | 0.9336 | -1.0 | 0.9137 | 0.9534 | 0.9844 | 0.9566 | 0.8245 | | 3.589 | 16.0 | 1744 | 4.2719 | 0.8796 | 0.986 | 0.9726 | -1.0 | 0.9661 | 0.9093 | 0.8543 | 0.905 | 0.6914 | 0.8649 | 0.927 | 0.9313 | -1.0 | 0.9121 | 0.9505 | 0.9875 | 0.9543 | 0.8266 | | 3.589 | 17.0 | 1853 | 4.2497 | 0.8838 | 0.9845 | 0.9733 | -1.0 | 0.9656 | 0.9141 | 0.8599 | 0.9077 | 0.6997 | 0.8678 | 0.9295 | 0.9352 | -1.0 | 0.9141 | 0.9562 | 0.9812 | 0.958 | 0.832 | | 3.589 | 18.0 | 1962 | 4.2807 | 0.8829 | 0.9855 | 0.9754 | -1.0 | 0.9673 | 0.9121 | 0.8558 | 0.9099 | 0.6964 | 0.8683 | 0.9286 | 0.9337 | -1.0 | 0.9126 | 0.9548 | 0.9844 | 0.9555 | 0.8357 | | 3.2442 | 19.0 | 2071 | 4.1978 | 0.8835 | 0.9861 | 0.9748 | -1.0 | 0.9675 | 0.9121 | 0.8559 | 0.911 | 0.6932 | 0.8691 | 0.9272 | 0.9336 | -1.0 | 0.9134 | 0.9538 | 0.9844 | 0.9557 | 0.8337 | | 3.2442 | 20.0 | 2180 | 4.1673 | 0.8829 | 0.9858 | 0.9736 | -1.0 | 0.9689 | 0.9125 | 0.857 | 0.9087 | 0.696 | 0.8686 | 0.9299 | 0.9357 | -1.0 | 0.9169 | 0.9545 | 0.9844 | 0.958 | 0.8354 | ### Framework versions - Transformers 4.46.0.dev0 - Pytorch 2.5.0+cu124 - Tokenizers 0.20.1