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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: 1.2586
- Accuracy: 0.55
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 | 1.8677 | 0.3688 |
| No log | 2.0 | 80 | 1.5622 | 0.3625 |
| No log | 3.0 | 120 | 1.4344 | 0.5375 |
| No log | 4.0 | 160 | 1.2909 | 0.5 |
| No log | 5.0 | 200 | 1.2146 | 0.6 |
| No log | 6.0 | 240 | 1.2457 | 0.55 |
| No log | 7.0 | 280 | 1.2429 | 0.5563 |
| No log | 8.0 | 320 | 1.2015 | 0.5375 |
| No log | 9.0 | 360 | 1.2393 | 0.5188 |
| No log | 10.0 | 400 | 1.1908 | 0.5687 |
| No log | 11.0 | 440 | 1.1580 | 0.6188 |
| No log | 12.0 | 480 | 1.1608 | 0.575 |
| 1.0532 | 13.0 | 520 | 1.2468 | 0.5687 |
| 1.0532 | 14.0 | 560 | 1.2747 | 0.5188 |
| 1.0532 | 15.0 | 600 | 1.3293 | 0.525 |
| 1.0532 | 16.0 | 640 | 1.3720 | 0.525 |
| 1.0532 | 17.0 | 680 | 1.4374 | 0.5125 |
| 1.0532 | 18.0 | 720 | 1.3092 | 0.5687 |
| 1.0532 | 19.0 | 760 | 1.4143 | 0.5437 |
| 1.0532 | 20.0 | 800 | 1.5023 | 0.4938 |
| 1.0532 | 21.0 | 840 | 1.4033 | 0.575 |
| 1.0532 | 22.0 | 880 | 1.4476 | 0.5437 |
| 1.0532 | 23.0 | 920 | 1.3089 | 0.5813 |
| 1.0532 | 24.0 | 960 | 1.3866 | 0.5813 |
| 0.3016 | 25.0 | 1000 | 1.3748 | 0.5875 |
| 0.3016 | 26.0 | 1040 | 1.5846 | 0.5312 |
| 0.3016 | 27.0 | 1080 | 1.3451 | 0.5875 |
| 0.3016 | 28.0 | 1120 | 1.5289 | 0.5062 |
| 0.3016 | 29.0 | 1160 | 1.6067 | 0.5125 |
| 0.3016 | 30.0 | 1200 | 1.5002 | 0.5375 |
| 0.3016 | 31.0 | 1240 | 1.5404 | 0.55 |
| 0.3016 | 32.0 | 1280 | 1.5542 | 0.5563 |
| 0.3016 | 33.0 | 1320 | 1.4320 | 0.6062 |
| 0.3016 | 34.0 | 1360 | 1.6465 | 0.5312 |
| 0.3016 | 35.0 | 1400 | 1.7259 | 0.5062 |
| 0.3016 | 36.0 | 1440 | 1.5655 | 0.5687 |
| 0.3016 | 37.0 | 1480 | 1.4517 | 0.6188 |
| 0.1764 | 38.0 | 1520 | 1.5884 | 0.575 |
| 0.1764 | 39.0 | 1560 | 1.4692 | 0.5813 |
| 0.1764 | 40.0 | 1600 | 1.5062 | 0.6125 |
| 0.1764 | 41.0 | 1640 | 1.5122 | 0.6 |
| 0.1764 | 42.0 | 1680 | 1.5859 | 0.6 |
| 0.1764 | 43.0 | 1720 | 1.6816 | 0.525 |
| 0.1764 | 44.0 | 1760 | 1.5594 | 0.6062 |
| 0.1764 | 45.0 | 1800 | 1.7011 | 0.5375 |
| 0.1764 | 46.0 | 1840 | 1.5676 | 0.575 |
| 0.1764 | 47.0 | 1880 | 1.5260 | 0.6 |
| 0.1764 | 48.0 | 1920 | 1.5711 | 0.575 |
| 0.1764 | 49.0 | 1960 | 1.7095 | 0.5563 |
| 0.1256 | 50.0 | 2000 | 1.7625 | 0.5188 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
