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resnet-101-finetuned_resnet101-sgd-optimizer20-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: 0.3318
- Accuracy: 0.8848
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: 0.1
- 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 |
|---|---|---|---|---|
| 1.1302 | 0.99 | 65 | 1.0040 | 0.6724 |
| 1.1708 | 1.99 | 130 | 1.4856 | 0.5495 |
| 1.141 | 2.99 | 195 | 1.1486 | 0.6352 |
| 1.0119 | 3.99 | 260 | 0.8829 | 0.7314 |
| 0.8091 | 4.99 | 325 | 0.8301 | 0.7419 |
| 0.7878 | 5.99 | 390 | 0.8121 | 0.7333 |
| 0.6827 | 6.99 | 455 | 0.6047 | 0.7990 |
| 0.5525 | 7.99 | 520 | 0.6028 | 0.8048 |
| 0.5787 | 8.99 | 585 | 0.5183 | 0.8352 |
| 0.4797 | 9.99 | 650 | 0.4737 | 0.8543 |
| 0.4224 | 10.99 | 715 | 0.4943 | 0.8305 |
| 0.4389 | 11.99 | 780 | 0.4162 | 0.8629 |
| 0.4142 | 12.99 | 845 | 0.4000 | 0.8629 |
| 0.3144 | 13.99 | 910 | 0.3833 | 0.8695 |
| 0.2915 | 14.99 | 975 | 0.3688 | 0.8733 |
| 0.3302 | 15.99 | 1040 | 0.3643 | 0.8810 |
| 0.2954 | 16.99 | 1105 | 0.3446 | 0.8867 |
| 0.2186 | 17.99 | 1170 | 0.3571 | 0.8905 |
| 0.1812 | 18.99 | 1235 | 0.3334 | 0.8886 |
| 0.1911 | 19.99 | 1300 | 0.3318 | 0.8848 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2
