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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: 1.2976
- Accuracy: 0.6
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: 7e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: reduce_lr_on_plateau
- num_epochs: 77
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.8 | 2 | 2.0706 | 0.15 |
| No log | 2.0 | 5 | 2.0309 | 0.2313 |
| No log | 2.8 | 7 | 1.9846 | 0.2562 |
| 1.9868 | 4.0 | 10 | 1.8915 | 0.4062 |
| 1.9868 | 4.8 | 12 | 1.8529 | 0.3125 |
| 1.9868 | 6.0 | 15 | 1.7422 | 0.4125 |
| 1.9868 | 6.8 | 17 | 1.6761 | 0.4313 |
| 1.6815 | 8.0 | 20 | 1.6310 | 0.4562 |
| 1.6815 | 8.8 | 22 | 1.5900 | 0.45 |
| 1.6815 | 10.0 | 25 | 1.5402 | 0.4313 |
| 1.6815 | 10.8 | 27 | 1.5018 | 0.5 |
| 1.4233 | 12.0 | 30 | 1.4620 | 0.4875 |
| 1.4233 | 12.8 | 32 | 1.4286 | 0.5062 |
| 1.4233 | 14.0 | 35 | 1.4045 | 0.5125 |
| 1.4233 | 14.8 | 37 | 1.3860 | 0.5312 |
| 1.2127 | 16.0 | 40 | 1.3571 | 0.5 |
| 1.2127 | 16.8 | 42 | 1.3293 | 0.5375 |
| 1.2127 | 18.0 | 45 | 1.3742 | 0.4813 |
| 1.2127 | 18.8 | 47 | 1.3151 | 0.5437 |
| 1.0075 | 20.0 | 50 | 1.3053 | 0.5312 |
| 1.0075 | 20.8 | 52 | 1.3266 | 0.5375 |
| 1.0075 | 22.0 | 55 | 1.2964 | 0.5312 |
| 1.0075 | 22.8 | 57 | 1.2278 | 0.5875 |
| 0.8232 | 24.0 | 60 | 1.2501 | 0.5563 |
| 0.8232 | 24.8 | 62 | 1.2330 | 0.575 |
| 0.8232 | 26.0 | 65 | 1.2198 | 0.5625 |
| 0.8232 | 26.8 | 67 | 1.2071 | 0.5875 |
| 0.6738 | 28.0 | 70 | 1.2643 | 0.5875 |
| 0.6738 | 28.8 | 72 | 1.2594 | 0.5563 |
| 0.6738 | 30.0 | 75 | 1.2263 | 0.5312 |
| 0.6738 | 30.8 | 77 | 1.3218 | 0.5188 |
| 0.5715 | 32.0 | 80 | 1.2593 | 0.5312 |
| 0.5715 | 32.8 | 82 | 1.2214 | 0.5625 |
| 0.5715 | 34.0 | 85 | 1.3060 | 0.55 |
| 0.5715 | 34.8 | 87 | 1.2727 | 0.5563 |
| 0.4523 | 36.0 | 90 | 1.2749 | 0.5375 |
| 0.4523 | 36.8 | 92 | 1.3570 | 0.5437 |
| 0.4523 | 38.0 | 95 | 1.2815 | 0.5687 |
| 0.4523 | 38.8 | 97 | 1.2233 | 0.6062 |
| 0.3971 | 40.0 | 100 | 1.2097 | 0.6 |
| 0.3971 | 40.8 | 102 | 1.2881 | 0.5813 |
| 0.3971 | 42.0 | 105 | 1.2400 | 0.575 |
| 0.3971 | 42.8 | 107 | 1.3140 | 0.5375 |
| 0.3616 | 44.0 | 110 | 1.1525 | 0.6125 |
| 0.3616 | 44.8 | 112 | 1.2725 | 0.5938 |
| 0.3616 | 46.0 | 115 | 1.2634 | 0.5813 |
| 0.3616 | 46.8 | 117 | 1.2299 | 0.6 |
| 0.338 | 48.0 | 120 | 1.3408 | 0.5375 |
| 0.338 | 48.8 | 122 | 1.1931 | 0.5938 |
| 0.338 | 50.0 | 125 | 1.2806 | 0.5938 |
| 0.338 | 50.8 | 127 | 1.2410 | 0.575 |
| 0.3445 | 52.0 | 130 | 1.2901 | 0.5813 |
| 0.3445 | 52.8 | 132 | 1.2504 | 0.6062 |
| 0.3445 | 54.0 | 135 | 1.1614 | 0.5875 |
| 0.3445 | 54.8 | 137 | 1.2247 | 0.6062 |
| 0.3299 | 56.0 | 140 | 1.2591 | 0.5625 |
| 0.3299 | 56.8 | 142 | 1.2629 | 0.5687 |
| 0.3299 | 58.0 | 145 | 1.2369 | 0.5938 |
| 0.3299 | 58.8 | 147 | 1.2771 | 0.575 |
| 0.3292 | 60.0 | 150 | 1.3284 | 0.5875 |
| 0.3292 | 60.8 | 152 | 1.2550 | 0.5625 |
| 0.3292 | 61.6 | 154 | 1.3047 | 0.55 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
