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This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1152
- Accuracy: 0.9645
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6588 | 1.0 | 357 | 0.6460 | 0.7707 |
| 0.3752 | 2.0 | 714 | 0.2969 | 0.8933 |
| 0.3275 | 3.0 | 1071 | 0.1912 | 0.9319 |
| 0.2183 | 4.0 | 1429 | 0.1794 | 0.9305 |
| 0.2133 | 5.0 | 1786 | 0.1638 | 0.9414 |
| 0.1984 | 6.0 | 2143 | 0.1322 | 0.9484 |
| 0.1409 | 7.0 | 2500 | 0.1304 | 0.9529 |
| 0.1864 | 8.0 | 2858 | 0.1212 | 0.9572 |
| 0.1778 | 9.0 | 3215 | 0.1216 | 0.9540 |
| 0.1734 | 10.0 | 3572 | 0.1129 | 0.9593 |
| 0.1349 | 11.0 | 3929 | 0.1127 | 0.9614 |
| 0.1057 | 12.0 | 4287 | 0.1177 | 0.9582 |
| 0.1434 | 13.0 | 4644 | 0.1153 | 0.9603 |
| 0.0832 | 14.0 | 5001 | 0.1264 | 0.9593 |
| 0.0963 | 15.0 | 5358 | 0.1146 | 0.9607 |
| 0.0642 | 16.0 | 5716 | 0.1135 | 0.9635 |
| 0.0763 | 17.0 | 6073 | 0.1210 | 0.9614 |
| 0.0432 | 18.0 | 6430 | 0.1162 | 0.9645 |
| 0.0618 | 19.0 | 6787 | 0.1269 | 0.9600 |
| 0.049 | 19.99 | 7140 | 0.1152 | 0.9645 |
Framework versions
- Transformers 4.30.0.dev0
- Pytorch 1.13.0
- Datasets 2.10.1
- Tokenizers 0.11.0
