| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: microsoft/conditional-detr-resnet-50 |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: detr-cppe5-ppe-detector |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # detr-cppe5-ppe-detector |
|
|
| This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.1687 |
|
|
| ## 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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: cosine |
| - num_epochs: 30 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:-----:|:----:|:---------------:| |
| | No log | 1.0 | 106 | 1.9109 | |
| | No log | 2.0 | 212 | 1.7408 | |
| | No log | 3.0 | 318 | 1.7527 | |
| | No log | 4.0 | 424 | 1.5295 | |
| | 3.5859 | 5.0 | 530 | 1.5355 | |
| | 3.5859 | 6.0 | 636 | 1.5048 | |
| | 3.5859 | 7.0 | 742 | 1.4679 | |
| | 3.5859 | 8.0 | 848 | 1.4037 | |
| | 3.5859 | 9.0 | 954 | 1.4161 | |
| | 1.3248 | 10.0 | 1060 | 1.3470 | |
| | 1.3248 | 11.0 | 1166 | 1.3197 | |
| | 1.3248 | 12.0 | 1272 | 1.3047 | |
| | 1.3248 | 13.0 | 1378 | 1.3000 | |
| | 1.3248 | 14.0 | 1484 | 1.2670 | |
| | 1.1606 | 15.0 | 1590 | 1.2603 | |
| | 1.1606 | 16.0 | 1696 | 1.2669 | |
| | 1.1606 | 17.0 | 1802 | 1.2255 | |
| | 1.1606 | 18.0 | 1908 | 1.2329 | |
| | 1.0363 | 19.0 | 2014 | 1.1964 | |
| | 1.0363 | 20.0 | 2120 | 1.1991 | |
| | 1.0363 | 21.0 | 2226 | 1.1966 | |
| | 1.0363 | 22.0 | 2332 | 1.1834 | |
| | 1.0363 | 23.0 | 2438 | 1.1747 | |
| | 0.938 | 24.0 | 2544 | 1.1785 | |
| | 0.938 | 25.0 | 2650 | 1.1752 | |
| | 0.938 | 26.0 | 2756 | 1.1718 | |
| | 0.938 | 27.0 | 2862 | 1.1747 | |
| | 0.938 | 28.0 | 2968 | 1.1736 | |
| | 0.8943 | 29.0 | 3074 | 1.1734 | |
| | 0.8943 | 30.0 | 3180 | 1.1687 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.49.0 |
| - Pytorch 2.11.0+cu128 |
| - Datasets 2.21.0 |
| - Tokenizers 0.21.4 |
|
|
