File size: 9,702 Bytes
c1cb6c2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
---
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:
<pre>
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))
</pre>
## 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
|