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resnet-50-finetuned-omars1
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: 1.2536
- Accuracy: 0.6667
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: 0.0005
- 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: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3877 | 1.0 | 11 | 1.3919 | 0.2564 |
| 1.383 | 2.0 | 22 | 1.3813 | 0.3077 |
| 1.366 | 3.0 | 33 | 1.3663 | 0.3077 |
| 1.348 | 4.0 | 44 | 1.3393 | 0.4103 |
| 1.3034 | 5.0 | 55 | 1.2699 | 0.5641 |
| 1.2227 | 6.0 | 66 | 1.1615 | 0.6154 |
| 1.0912 | 7.0 | 77 | 1.1262 | 0.6154 |
| 0.9553 | 8.0 | 88 | 1.1313 | 0.5897 |
| 0.8801 | 9.0 | 99 | 1.1711 | 0.6667 |
| 0.8017 | 10.0 | 110 | 1.0136 | 0.6667 |
| 0.7451 | 11.0 | 121 | 0.9310 | 0.6923 |
| 0.6817 | 12.0 | 132 | 0.8635 | 0.6667 |
| 0.6579 | 13.0 | 143 | 1.1545 | 0.6667 |
| 0.6357 | 14.0 | 154 | 0.9239 | 0.6154 |
| 0.6006 | 15.0 | 165 | 1.0271 | 0.6667 |
| 0.5551 | 16.0 | 176 | 1.1781 | 0.5897 |
| 0.5619 | 17.0 | 187 | 1.1831 | 0.6923 |
| 0.5359 | 18.0 | 198 | 0.9667 | 0.6667 |
| 0.5247 | 19.0 | 209 | 1.1237 | 0.6667 |
| 0.5134 | 20.0 | 220 | 1.1176 | 0.6410 |
| 0.4469 | 21.0 | 231 | 0.9955 | 0.7179 |
| 0.4908 | 22.0 | 242 | 1.1411 | 0.7179 |
| 0.4112 | 23.0 | 253 | 1.2766 | 0.6410 |
| 0.4225 | 24.0 | 264 | 1.1135 | 0.6923 |
| 0.4786 | 25.0 | 275 | 1.2243 | 0.7179 |
| 0.3908 | 26.0 | 286 | 1.1587 | 0.7179 |
| 0.4706 | 27.0 | 297 | 1.2236 | 0.6923 |
| 0.502 | 28.0 | 308 | 1.1733 | 0.7179 |
| 0.4514 | 29.0 | 319 | 1.0931 | 0.7436 |
| 0.4386 | 30.0 | 330 | 1.2536 | 0.6667 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3
