Instructions to use merve/rfdetr-road-signs-agree1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use merve/rfdetr-road-signs-agree1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="merve/rfdetr-road-signs-agree1")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("merve/rfdetr-road-signs-agree1") model = AutoModelForObjectDetection.from_pretrained("merve/rfdetr-road-signs-agree1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
rfdetr-road-signs-agree1
This model is a fine-tuned version of Roboflow/rf-detr-medium on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 16.2265
- Map: 0.2689
- Map 50: 0.3209
- Map 75: 0.2914
- Map Small: 0.1286
- Map Medium: 0.2837
- Map Large: 0.2816
- Mar 1: 0.8019
- Mar 10: 0.8631
- Mar 100: 0.8721
- Mar Small: 0.3889
- Mar Medium: 0.65
- Mar Large: 0.8778
- Map Bus Stop: 0.4038
- Mar 100 Bus Stop: 0.925
- Map Do Not Enter: 0.4978
- Mar 100 Do Not Enter: 0.8467
- Map Do Not Stop: 0.0699
- Mar 100 Do Not Stop: 0.9556
- Map Do Not Turn L: 0.3458
- Mar 100 Do Not Turn L: 0.875
- Map Do Not Turn R: 0.1562
- Mar 100 Do Not Turn R: 0.95
- Map Do Not U Turn: 0.3238
- Mar 100 Do Not U Turn: 0.95
- Map Enter Left Lane: 0.0153
- Mar 100 Enter Left Lane: 0.9
- Map Green Light: 0.3311
- Mar 100 Green Light: 0.6143
- Map Left Right Lane: 0.6104
- Mar 100 Left Right Lane: 0.9833
- Map No Parking: 0.2497
- Mar 100 No Parking: 0.8842
- Map Parking: 0.9
- Mar 100 Parking: 0.9
- Map Ped Crossing: 0.1528
- Mar 100 Ped Crossing: 0.9545
- Map Ped Zebra Cross: 0.0137
- Mar 100 Ped Zebra Cross: 1.0
- Map Railway Crossing: 0.0997
- Mar 100 Railway Crossing: 0.7667
- Map Red Light: 0.1385
- Mar 100 Red Light: 0.6875
- Map Stop: 0.0998
- Mar 100 Stop: 0.9571
- Map T Intersection L: 0.0476
- Mar 100 T Intersection L: 1.0
- Map Traffic Light: 0.4625
- Mar 100 Traffic Light: 0.7049
- Map U Turn: 0.0343
- Mar 100 U Turn: 0.8
- Map Warning: 0.6763
- Mar 100 Warning: 0.86
- Map Yellow Light: 0.0174
- Mar 100 Yellow Light: 0.8
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.05
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Bus Stop | Mar 100 Bus Stop | Map Do Not Enter | Mar 100 Do Not Enter | Map Do Not Stop | Mar 100 Do Not Stop | Map Do Not Turn L | Mar 100 Do Not Turn L | Map Do Not Turn R | Mar 100 Do Not Turn R | Map Do Not U Turn | Mar 100 Do Not U Turn | Map Enter Left Lane | Mar 100 Enter Left Lane | Map Green Light | Mar 100 Green Light | Map Left Right Lane | Mar 100 Left Right Lane | Map No Parking | Mar 100 No Parking | Map Parking | Mar 100 Parking | Map Ped Crossing | Mar 100 Ped Crossing | Map Ped Zebra Cross | Mar 100 Ped Zebra Cross | Map Railway Crossing | Mar 100 Railway Crossing | Map Red Light | Mar 100 Red Light | Map Stop | Mar 100 Stop | Map T Intersection L | Mar 100 T Intersection L | Map Traffic Light | Mar 100 Traffic Light | Map U Turn | Mar 100 U Turn | Map Warning | Mar 100 Warning | Map Yellow Light | Mar 100 Yellow Light |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 28.0822 | 1.0 | 143 | 23.4630 | 0.0001 | 0.0002 | 0.0001 | 0.0 | 0.0005 | 0.0001 | 0.0027 | 0.0113 | 0.0166 | 0.0 | 0.002 | 0.018 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0005 | 0.1167 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0007 | 0.0389 | 0.0 | 0.0158 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0714 | 0.0 | 0.0 | 0.0 | 0.0459 | 0.0 | 0.0 | 0.0001 | 0.06 | 0.0 | 0.0 |
| 13.7666 | 2.0 | 286 | 17.0324 | 0.002 | 0.0057 | 0.0012 | 0.0 | 0.0005 | 0.002 | 0.0392 | 0.076 | 0.0988 | 0.0 | 0.005 | 0.1058 | 0.0 | 0.0 | 0.0041 | 0.2667 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0129 | 0.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0051 | 0.2889 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0035 | 0.4273 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0007 | 0.1934 | 0.0 | 0.0 | 0.0151 | 0.698 | 0.0 | 0.0 |
| 10.8868 | 3.0 | 429 | 14.3461 | 0.0287 | 0.0469 | 0.0288 | 0.0814 | 0.1076 | 0.0197 | 0.2561 | 0.3408 | 0.3612 | 0.2333 | 0.325 | 0.3427 | 0.0 | 0.0 | 0.0179 | 0.7067 | 0.0379 | 0.6222 | 0.0011 | 0.15 | 0.0067 | 0.6083 | 0.0382 | 0.9417 | 0.0 | 0.0 | 0.1938 | 0.3714 | 0.0622 | 0.9111 | 0.0235 | 0.4737 | 0.0 | 0.0 | 0.0502 | 0.8727 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0128 | 0.1625 | 0.0103 | 0.4571 | 0.0 | 0.0 | 0.0238 | 0.5082 | 0.0 | 0.0 | 0.1235 | 0.8 | 0.0 | 0.0 |
| 10.0276 | 4.0 | 572 | 13.8426 | 0.0886 | 0.1213 | 0.097 | 0.1707 | 0.2433 | 0.0924 | 0.5664 | 0.6646 | 0.684 | 0.3778 | 0.596 | 0.6887 | 0.258 | 0.9 | 0.0559 | 0.8 | 0.0306 | 0.9444 | 0.0417 | 0.9 | 0.028 | 0.9417 | 0.0775 | 0.9417 | 0.0 | 0.0 | 0.3225 | 0.5357 | 0.0645 | 0.9611 | 0.0615 | 0.7895 | 0.0409 | 0.9 | 0.0667 | 0.9545 | 0.0 | 0.0 | 0.0081 | 0.55 | 0.062 | 0.625 | 0.1993 | 0.8714 | 0.0071 | 0.9 | 0.223 | 0.6557 | 0.0082 | 0.3 | 0.3052 | 0.894 | 0.0 | 0.0 |
| 9.5180 | 5.0 | 715 | 16.2613 | 0.1193 | 0.1587 | 0.1365 | 0.1329 | 0.2408 | 0.1224 | 0.6571 | 0.7468 | 0.7649 | 0.3444 | 0.639 | 0.772 | 0.1024 | 0.9 | 0.06 | 0.8467 | 0.0388 | 0.9444 | 0.0952 | 0.85 | 0.0599 | 0.9417 | 0.216 | 0.925 | 0.0093 | 0.9 | 0.313 | 0.6357 | 0.1456 | 0.9222 | 0.181 | 0.8737 | 0.0667 | 0.8 | 0.1754 | 0.9091 | 0.0 | 0.0 | 0.0096 | 0.5833 | 0.0794 | 0.7125 | 0.0819 | 0.9143 | 0.006 | 1.0 | 0.2937 | 0.6639 | 0.0334 | 0.8667 | 0.5384 | 0.874 | 0.0 | 0.0 |
| 8.8634 | 6.0 | 858 | 15.7990 | 0.1696 | 0.2226 | 0.1805 | 0.1544 | 0.2718 | 0.1847 | 0.7274 | 0.8358 | 0.8466 | 0.4444 | 0.582 | 0.8562 | 0.0794 | 0.925 | 0.1404 | 0.8267 | 0.0463 | 0.9333 | 0.2734 | 0.7875 | 0.0873 | 0.95 | 0.121 | 0.9083 | 0.0098 | 0.9 | 0.2805 | 0.4929 | 0.337 | 0.9278 | 0.3447 | 0.8632 | 0.45 | 0.9 | 0.0874 | 0.9 | 0.0117 | 0.9 | 0.0263 | 0.7167 | 0.1487 | 0.7063 | 0.058 | 0.9286 | 0.0096 | 1.0 | 0.3852 | 0.7016 | 0.0261 | 0.7667 | 0.6143 | 0.844 | 0.0237 | 0.9 |
| 8.4210 | 7.0 | 1001 | 15.3068 | 0.2227 | 0.2736 | 0.2492 | 0.1669 | 0.3058 | 0.2396 | 0.8044 | 0.8706 | 0.8812 | 0.3833 | 0.656 | 0.8884 | 0.1591 | 0.925 | 0.2264 | 0.8733 | 0.0643 | 0.9667 | 0.3429 | 0.8625 | 0.1144 | 0.9583 | 0.2645 | 0.9583 | 0.0104 | 1.0 | 0.3766 | 0.6786 | 0.4295 | 0.9444 | 0.1889 | 0.8842 | 0.9 | 0.9 | 0.0913 | 0.9182 | 0.0092 | 1.0 | 0.0929 | 0.7333 | 0.1627 | 0.6875 | 0.0802 | 0.9286 | 0.0189 | 1.0 | 0.4232 | 0.6984 | 0.0353 | 0.8 | 0.6566 | 0.888 | 0.03 | 0.9 |
| 8.2428 | 8.0 | 1144 | 16.1830 | 0.2493 | 0.2984 | 0.2729 | 0.1366 | 0.3077 | 0.2634 | 0.8024 | 0.8674 | 0.8788 | 0.3889 | 0.6635 | 0.8922 | 0.3068 | 0.925 | 0.4441 | 0.8533 | 0.0652 | 0.9444 | 0.3309 | 0.8875 | 0.1337 | 0.9667 | 0.2375 | 0.9417 | 0.0125 | 0.9 | 0.3596 | 0.6714 | 0.6035 | 0.9722 | 0.2338 | 0.9 | 0.9 | 0.9 | 0.1209 | 0.9182 | 0.0078 | 1.0 | 0.0481 | 0.75 | 0.1333 | 0.6875 | 0.0963 | 0.9571 | 0.0476 | 1.0 | 0.4454 | 0.7049 | 0.0307 | 0.8 | 0.6553 | 0.874 | 0.022 | 0.9 |
| 8.0494 | 9.0 | 1287 | 16.2679 | 0.2778 | 0.3306 | 0.3049 | 0.125 | 0.2839 | 0.2917 | 0.7989 | 0.8605 | 0.8725 | 0.3667 | 0.648 | 0.8844 | 0.4067 | 0.925 | 0.5109 | 0.8533 | 0.0681 | 0.9778 | 0.4016 | 0.9125 | 0.1547 | 0.9417 | 0.3238 | 0.9417 | 0.014 | 0.8 | 0.3377 | 0.6357 | 0.6367 | 0.9833 | 0.2883 | 0.8842 | 0.9 | 0.9 | 0.1278 | 0.9455 | 0.0133 | 1.0 | 0.0984 | 0.7667 | 0.1521 | 0.6875 | 0.1251 | 0.9714 | 0.0909 | 1.0 | 0.4551 | 0.6902 | 0.0291 | 0.8333 | 0.684 | 0.872 | 0.0154 | 0.8 |
| 7.8667 | 10.0 | 1430 | 16.2265 | 0.2689 | 0.3209 | 0.2914 | 0.1286 | 0.2837 | 0.2816 | 0.8019 | 0.8631 | 0.8721 | 0.3889 | 0.65 | 0.8778 | 0.4038 | 0.925 | 0.4978 | 0.8467 | 0.0699 | 0.9556 | 0.3458 | 0.875 | 0.1562 | 0.95 | 0.3238 | 0.95 | 0.0153 | 0.9 | 0.3311 | 0.6143 | 0.6104 | 0.9833 | 0.2497 | 0.8842 | 0.9 | 0.9 | 0.1528 | 0.9545 | 0.0137 | 1.0 | 0.0997 | 0.7667 | 0.1385 | 0.6875 | 0.0998 | 0.9571 | 0.0476 | 1.0 | 0.4625 | 0.7049 | 0.0343 | 0.8 | 0.6763 | 0.86 | 0.0174 | 0.8 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.12.1+cu130
- Datasets 5.0.0
- Tokenizers 0.22.2
- Downloads last month
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Model tree for merve/rfdetr-road-signs-agree1
Base model
Roboflow/rf-detr-medium