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resnet-101-finetuned_resnet101-adam-optimizer5e-4-autotags
This model is a fine-tuned version of microsoft/resnet-101 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.2477
- Accuracy: 0.9267
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|---|---|---|---|---|
| 2.6033 | 0.99 | 65 | 2.5693 | 0.1381 |
| 1.5517 | 1.99 | 130 | 1.1376 | 0.6733 |
| 0.9423 | 2.99 | 195 | 0.6290 | 0.7895 |
| 0.6334 | 3.99 | 260 | 0.4372 | 0.86 |
| 0.4735 | 4.99 | 325 | 0.4719 | 0.8429 |
| 0.4573 | 5.99 | 390 | 0.3909 | 0.8590 |
| 0.3236 | 6.99 | 455 | 0.3507 | 0.8752 |
| 0.2511 | 7.99 | 520 | 0.2931 | 0.9019 |
| 0.2073 | 8.99 | 585 | 0.2757 | 0.9133 |
| 0.2174 | 9.99 | 650 | 0.2706 | 0.9114 |
| 0.1558 | 10.99 | 715 | 0.2654 | 0.9114 |
| 0.2017 | 11.99 | 780 | 0.2820 | 0.9114 |
| 0.134 | 12.99 | 845 | 0.2431 | 0.9238 |
| 0.0943 | 13.99 | 910 | 0.2606 | 0.9105 |
| 0.1396 | 14.99 | 975 | 0.2514 | 0.9229 |
| 0.1374 | 15.99 | 1040 | 0.2349 | 0.9305 |
| 0.0953 | 16.99 | 1105 | 0.2502 | 0.9210 |
| 0.0742 | 17.99 | 1170 | 0.2515 | 0.9210 |
| 0.0708 | 18.99 | 1235 | 0.2437 | 0.9257 |
| 0.0619 | 19.99 | 1300 | 0.2477 | 0.9267 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2
