đ 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.
đ 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.
đģ 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))
đ 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
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
đ License
This project is licensed under the apache - 2.0 license.