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vit-base-patch16-224-in21k
This model was trained from scratch on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.6306
- Accuracy: 0.5375
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: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 40 | 1.2472 | 0.5312 |
| No log | 2.0 | 80 | 1.2878 | 0.5188 |
| No log | 3.0 | 120 | 1.3116 | 0.525 |
| No log | 4.0 | 160 | 1.2578 | 0.55 |
| No log | 5.0 | 200 | 1.2186 | 0.5563 |
| No log | 6.0 | 240 | 1.2680 | 0.5563 |
| No log | 7.0 | 280 | 1.3674 | 0.5 |
| No log | 8.0 | 320 | 1.3814 | 0.525 |
| No log | 9.0 | 360 | 1.4394 | 0.5 |
| No log | 10.0 | 400 | 1.3710 | 0.5437 |
| No log | 11.0 | 440 | 1.3721 | 0.5437 |
| No log | 12.0 | 480 | 1.4309 | 0.5563 |
| 0.4861 | 13.0 | 520 | 1.3424 | 0.575 |
| 0.4861 | 14.0 | 560 | 1.4617 | 0.525 |
| 0.4861 | 15.0 | 600 | 1.3964 | 0.5813 |
| 0.4861 | 16.0 | 640 | 1.4751 | 0.5687 |
| 0.4861 | 17.0 | 680 | 1.5296 | 0.55 |
| 0.4861 | 18.0 | 720 | 1.5887 | 0.5188 |
| 0.4861 | 19.0 | 760 | 1.5784 | 0.5312 |
| 0.4861 | 20.0 | 800 | 1.7036 | 0.5375 |
| 0.4861 | 21.0 | 840 | 1.6988 | 0.5188 |
| 0.4861 | 22.0 | 880 | 1.6070 | 0.5687 |
| 0.4861 | 23.0 | 920 | 1.7111 | 0.55 |
| 0.4861 | 24.0 | 960 | 1.6730 | 0.55 |
| 0.2042 | 25.0 | 1000 | 1.6559 | 0.55 |
| 0.2042 | 26.0 | 1040 | 1.7221 | 0.5563 |
| 0.2042 | 27.0 | 1080 | 1.6637 | 0.5813 |
| 0.2042 | 28.0 | 1120 | 1.6806 | 0.5625 |
| 0.2042 | 29.0 | 1160 | 1.5743 | 0.5938 |
| 0.2042 | 30.0 | 1200 | 1.7899 | 0.4938 |
| 0.2042 | 31.0 | 1240 | 1.7422 | 0.5312 |
| 0.2042 | 32.0 | 1280 | 1.7712 | 0.55 |
| 0.2042 | 33.0 | 1320 | 1.7480 | 0.5188 |
| 0.2042 | 34.0 | 1360 | 1.7964 | 0.5375 |
| 0.2042 | 35.0 | 1400 | 1.9687 | 0.5188 |
| 0.2042 | 36.0 | 1440 | 1.7412 | 0.5813 |
| 0.2042 | 37.0 | 1480 | 1.9312 | 0.4875 |
| 0.1342 | 38.0 | 1520 | 1.7944 | 0.525 |
| 0.1342 | 39.0 | 1560 | 1.8180 | 0.55 |
| 0.1342 | 40.0 | 1600 | 1.7720 | 0.5563 |
| 0.1342 | 41.0 | 1640 | 1.9014 | 0.5312 |
| 0.1342 | 42.0 | 1680 | 1.7519 | 0.55 |
| 0.1342 | 43.0 | 1720 | 1.9793 | 0.5 |
| 0.1342 | 44.0 | 1760 | 1.8642 | 0.55 |
| 0.1342 | 45.0 | 1800 | 1.7573 | 0.5875 |
| 0.1342 | 46.0 | 1840 | 1.8508 | 0.5125 |
| 0.1342 | 47.0 | 1880 | 1.9741 | 0.5625 |
| 0.1342 | 48.0 | 1920 | 1.9012 | 0.525 |
| 0.1342 | 49.0 | 1960 | 1.8771 | 0.5625 |
| 0.0926 | 50.0 | 2000 | 1.8728 | 0.5125 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
