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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: 1.6838
- Accuracy: 0.525
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 10 | 1.3274 | 0.5125 |
| No log | 2.0 | 20 | 1.3119 | 0.5188 |
| No log | 3.0 | 30 | 1.3825 | 0.4625 |
| No log | 4.0 | 40 | 1.2916 | 0.5312 |
| No log | 5.0 | 50 | 1.2821 | 0.525 |
| No log | 6.0 | 60 | 1.2407 | 0.525 |
| No log | 7.0 | 70 | 1.3288 | 0.5125 |
| No log | 8.0 | 80 | 1.2818 | 0.525 |
| No log | 9.0 | 90 | 1.3710 | 0.4875 |
| No log | 10.0 | 100 | 1.3298 | 0.5312 |
| No log | 11.0 | 110 | 1.3539 | 0.475 |
| No log | 12.0 | 120 | 1.4498 | 0.4688 |
| No log | 13.0 | 130 | 1.5422 | 0.4437 |
| No log | 14.0 | 140 | 1.4870 | 0.4625 |
| No log | 15.0 | 150 | 1.4354 | 0.525 |
| No log | 16.0 | 160 | 1.4286 | 0.4938 |
| No log | 17.0 | 170 | 1.5332 | 0.4437 |
| No log | 18.0 | 180 | 1.4164 | 0.5188 |
| No log | 19.0 | 190 | 1.5024 | 0.4625 |
| No log | 20.0 | 200 | 1.4730 | 0.5125 |
| No log | 21.0 | 210 | 1.3083 | 0.55 |
| No log | 22.0 | 220 | 1.4468 | 0.525 |
| No log | 23.0 | 230 | 1.3198 | 0.525 |
| No log | 24.0 | 240 | 1.3530 | 0.5563 |
| No log | 25.0 | 250 | 1.4821 | 0.4938 |
| No log | 26.0 | 260 | 1.3475 | 0.5437 |
| No log | 27.0 | 270 | 1.5152 | 0.4875 |
| No log | 28.0 | 280 | 1.4290 | 0.55 |
| No log | 29.0 | 290 | 1.5505 | 0.5 |
| No log | 30.0 | 300 | 1.5796 | 0.5062 |
| No log | 31.0 | 310 | 1.5988 | 0.5125 |
| No log | 32.0 | 320 | 1.6272 | 0.4875 |
| No log | 33.0 | 330 | 1.4324 | 0.5437 |
| No log | 34.0 | 340 | 1.5245 | 0.5062 |
| No log | 35.0 | 350 | 1.7228 | 0.45 |
| No log | 36.0 | 360 | 1.4861 | 0.525 |
| No log | 37.0 | 370 | 1.5317 | 0.5312 |
| No log | 38.0 | 380 | 1.7776 | 0.475 |
| No log | 39.0 | 390 | 1.5386 | 0.5563 |
| No log | 40.0 | 400 | 1.7608 | 0.475 |
| No log | 41.0 | 410 | 1.5469 | 0.55 |
| No log | 42.0 | 420 | 1.6919 | 0.4625 |
| No log | 43.0 | 430 | 1.5814 | 0.525 |
| No log | 44.0 | 440 | 1.5877 | 0.5125 |
| No log | 45.0 | 450 | 1.6370 | 0.5188 |
| No log | 46.0 | 460 | 1.7375 | 0.5188 |
| No log | 47.0 | 470 | 1.7004 | 0.5 |
| No log | 48.0 | 480 | 1.6309 | 0.4938 |
| No log | 49.0 | 490 | 1.5931 | 0.5437 |
| 0.2996 | 50.0 | 500 | 1.7687 | 0.5062 |
| 0.2996 | 51.0 | 510 | 1.5321 | 0.5188 |
| 0.2996 | 52.0 | 520 | 1.8099 | 0.4688 |
| 0.2996 | 53.0 | 530 | 1.5138 | 0.575 |
| 0.2996 | 54.0 | 540 | 1.7569 | 0.4688 |
| 0.2996 | 55.0 | 550 | 1.7451 | 0.4813 |
| 0.2996 | 56.0 | 560 | 1.6871 | 0.5125 |
| 0.2996 | 57.0 | 570 | 1.6471 | 0.525 |
| 0.2996 | 58.0 | 580 | 1.6966 | 0.525 |
| 0.2996 | 59.0 | 590 | 1.7714 | 0.5 |
| 0.2996 | 60.0 | 600 | 1.4985 | 0.5938 |
| 0.2996 | 61.0 | 610 | 1.9804 | 0.4313 |
| 0.2996 | 62.0 | 620 | 1.6116 | 0.5375 |
| 0.2996 | 63.0 | 630 | 1.6056 | 0.525 |
| 0.2996 | 64.0 | 640 | 1.6115 | 0.5062 |
| 0.2996 | 65.0 | 650 | 1.9694 | 0.4625 |
| 0.2996 | 66.0 | 660 | 1.6338 | 0.5563 |
| 0.2996 | 67.0 | 670 | 1.4823 | 0.5938 |
| 0.2996 | 68.0 | 680 | 1.9253 | 0.5 |
| 0.2996 | 69.0 | 690 | 1.9015 | 0.4813 |
| 0.2996 | 70.0 | 700 | 1.5446 | 0.5687 |
| 0.2996 | 71.0 | 710 | 1.9302 | 0.4938 |
| 0.2996 | 72.0 | 720 | 1.6973 | 0.5375 |
| 0.2996 | 73.0 | 730 | 1.8271 | 0.5 |
| 0.2996 | 74.0 | 740 | 1.7559 | 0.5188 |
| 0.2996 | 75.0 | 750 | 1.8127 | 0.5312 |
| 0.2996 | 76.0 | 760 | 1.8096 | 0.4938 |
| 0.2996 | 77.0 | 770 | 1.8460 | 0.5062 |
| 0.2996 | 78.0 | 780 | 1.8853 | 0.4813 |
| 0.2996 | 79.0 | 790 | 1.7706 | 0.5125 |
| 0.2996 | 80.0 | 800 | 1.8129 | 0.5312 |
| 0.2996 | 81.0 | 810 | 1.9488 | 0.4688 |
| 0.2996 | 82.0 | 820 | 1.8817 | 0.4813 |
| 0.2996 | 83.0 | 830 | 1.6759 | 0.5563 |
| 0.2996 | 84.0 | 840 | 1.6884 | 0.5 |
| 0.2996 | 85.0 | 850 | 1.8146 | 0.4875 |
| 0.2996 | 86.0 | 860 | 1.6610 | 0.55 |
| 0.2996 | 87.0 | 870 | 1.8811 | 0.475 |
| 0.2996 | 88.0 | 880 | 1.8964 | 0.5062 |
| 0.2996 | 89.0 | 890 | 1.6848 | 0.5437 |
| 0.2996 | 90.0 | 900 | 1.8642 | 0.4938 |
| 0.2996 | 91.0 | 910 | 1.8819 | 0.5125 |
| 0.2996 | 92.0 | 920 | 1.9193 | 0.4875 |
| 0.2996 | 93.0 | 930 | 1.8110 | 0.5 |
| 0.2996 | 94.0 | 940 | 1.9086 | 0.4813 |
| 0.2996 | 95.0 | 950 | 1.8895 | 0.4625 |
| 0.2996 | 96.0 | 960 | 1.7554 | 0.5312 |
| 0.2996 | 97.0 | 970 | 1.8978 | 0.5188 |
| 0.2996 | 98.0 | 980 | 1.9791 | 0.4875 |
| 0.2996 | 99.0 | 990 | 1.7030 | 0.5687 |
| 0.0883 | 100.0 | 1000 | 1.8398 | 0.4813 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
