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vca
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: 0.3295
- Recall: 0.6533
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: 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: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Recall |
|---|---|---|---|---|
| No log | 0.95 | 5 | 0.6389 | 0.0133 |
| No log | 1.9 | 10 | 0.5645 | 0.0 |
| No log | 2.86 | 15 | 0.4792 | 0.0 |
| No log | 4.0 | 21 | 0.4224 | 0.0 |
| No log | 4.95 | 26 | 0.3652 | 0.0 |
| No log | 5.9 | 31 | 0.3357 | 0.0 |
| No log | 6.86 | 36 | 0.2953 | 0.0 |
| No log | 8.0 | 42 | 0.2909 | 0.0133 |
| No log | 8.95 | 47 | 0.2937 | 0.7333 |
| No log | 9.9 | 52 | 0.2718 | 0.68 |
| No log | 10.86 | 57 | 0.2673 | 0.64 |
| No log | 12.0 | 63 | 0.3019 | 0.8667 |
| No log | 12.95 | 68 | 0.2945 | 0.4667 |
| No log | 13.9 | 73 | 0.2669 | 0.6667 |
| No log | 14.86 | 78 | 0.2504 | 0.7467 |
| No log | 16.0 | 84 | 0.2380 | 0.64 |
| No log | 16.95 | 89 | 0.2525 | 0.64 |
| No log | 17.9 | 94 | 0.2648 | 0.7467 |
| No log | 18.86 | 99 | 0.2711 | 0.7067 |
| No log | 20.0 | 105 | 0.2747 | 0.64 |
| No log | 20.95 | 110 | 0.2772 | 0.64 |
| No log | 21.9 | 115 | 0.3000 | 0.6267 |
| No log | 22.86 | 120 | 0.2871 | 0.5733 |
| No log | 24.0 | 126 | 0.3025 | 0.6667 |
| No log | 24.95 | 131 | 0.3317 | 0.5867 |
| No log | 25.9 | 136 | 0.3171 | 0.5467 |
| No log | 26.86 | 141 | 0.3322 | 0.64 |
| No log | 28.0 | 147 | 0.3207 | 0.6533 |
| No log | 28.95 | 152 | 0.3492 | 0.5733 |
| No log | 29.9 | 157 | 0.2965 | 0.68 |
| No log | 30.86 | 162 | 0.3256 | 0.72 |
| No log | 32.0 | 168 | 0.3460 | 0.6267 |
| No log | 32.95 | 173 | 0.3118 | 0.7067 |
| No log | 33.9 | 178 | 0.3656 | 0.6933 |
| No log | 34.86 | 183 | 0.3111 | 0.5867 |
| No log | 36.0 | 189 | 0.3119 | 0.6667 |
| No log | 36.95 | 194 | 0.3524 | 0.72 |
| No log | 37.9 | 199 | 0.3457 | 0.5333 |
| No log | 38.86 | 204 | 0.3460 | 0.56 |
| No log | 40.0 | 210 | 0.3518 | 0.6933 |
| No log | 40.95 | 215 | 0.2948 | 0.5867 |
| No log | 41.9 | 220 | 0.3640 | 0.5867 |
| No log | 42.86 | 225 | 0.3408 | 0.6133 |
| No log | 44.0 | 231 | 0.3350 | 0.6 |
| No log | 44.95 | 236 | 0.3832 | 0.72 |
| No log | 45.9 | 241 | 0.3298 | 0.6667 |
| No log | 46.86 | 246 | 0.3557 | 0.64 |
| No log | 48.0 | 252 | 0.3832 | 0.64 |
| No log | 48.95 | 257 | 0.3317 | 0.6533 |
| No log | 49.9 | 262 | 0.3980 | 0.6933 |
| No log | 50.86 | 267 | 0.3295 | 0.6533 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
