image_classification
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.1599
- Accuracy: 0.5813
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 13
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 40 | 1.8887 | 0.35 |
| No log | 2.0 | 80 | 1.5494 | 0.425 |
| No log | 3.0 | 120 | 1.4015 | 0.5188 |
| No log | 4.0 | 160 | 1.2919 | 0.55 |
| No log | 5.0 | 200 | 1.2205 | 0.5813 |
| No log | 6.0 | 240 | 1.2246 | 0.575 |
| No log | 7.0 | 280 | 1.2053 | 0.5312 |
| No log | 8.0 | 320 | 1.1487 | 0.5687 |
| No log | 9.0 | 360 | 1.1727 | 0.5437 |
| No log | 10.0 | 400 | 1.1459 | 0.55 |
| No log | 11.0 | 440 | 1.1313 | 0.5813 |
| No log | 12.0 | 480 | 1.0990 | 0.6062 |
| 1.1138 | 13.0 | 520 | 1.1020 | 0.6188 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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