jaffe_crop_200_iter
This model is a fine-tuned version of WinKawaks/vit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.6110
- Accuracy: 0.8333
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 200
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 1.0 | 1 | 2.7256 | 0.1667 |
No log | 2.0 | 2 | 2.5775 | 0.1667 |
No log | 3.0 | 3 | 2.5633 | 0.1333 |
No log | 4.0 | 4 | 2.3386 | 0.1333 |
No log | 5.0 | 5 | 2.3028 | 0.1 |
No log | 6.0 | 6 | 2.1672 | 0.2333 |
No log | 7.0 | 7 | 2.1696 | 0.0333 |
No log | 8.0 | 8 | 2.0133 | 0.0667 |
No log | 9.0 | 9 | 2.0010 | 0.1 |
2.3074 | 10.0 | 10 | 1.9153 | 0.3333 |
2.3074 | 11.0 | 11 | 2.0177 | 0.3 |
2.3074 | 12.0 | 12 | 1.8241 | 0.3333 |
2.3074 | 13.0 | 13 | 1.7967 | 0.3 |
2.3074 | 14.0 | 14 | 1.7210 | 0.3333 |
2.3074 | 15.0 | 15 | 1.6551 | 0.3333 |
2.3074 | 16.0 | 16 | 1.5307 | 0.5333 |
2.3074 | 17.0 | 17 | 1.4263 | 0.4333 |
2.3074 | 18.0 | 18 | 1.4219 | 0.4333 |
2.3074 | 19.0 | 19 | 1.4402 | 0.4333 |
1.5864 | 20.0 | 20 | 1.3050 | 0.5 |
1.5864 | 21.0 | 21 | 1.4508 | 0.4 |
1.5864 | 22.0 | 22 | 1.3384 | 0.4667 |
1.5864 | 23.0 | 23 | 1.1632 | 0.6 |
1.5864 | 24.0 | 24 | 1.0711 | 0.6333 |
1.5864 | 25.0 | 25 | 1.1361 | 0.4667 |
1.5864 | 26.0 | 26 | 1.2230 | 0.5667 |
1.5864 | 27.0 | 27 | 1.0167 | 0.7 |
1.5864 | 28.0 | 28 | 0.8815 | 0.6667 |
1.5864 | 29.0 | 29 | 1.0191 | 0.6667 |
0.7909 | 30.0 | 30 | 0.9474 | 0.6667 |
0.7909 | 31.0 | 31 | 0.9328 | 0.5333 |
0.7909 | 32.0 | 32 | 0.9476 | 0.6 |
0.7909 | 33.0 | 33 | 0.7911 | 0.7 |
0.7909 | 34.0 | 34 | 0.7609 | 0.7333 |
0.7909 | 35.0 | 35 | 0.9489 | 0.6333 |
0.7909 | 36.0 | 36 | 0.7199 | 0.6667 |
0.7909 | 37.0 | 37 | 0.6195 | 0.7667 |
0.7909 | 38.0 | 38 | 0.7611 | 0.7333 |
0.7909 | 39.0 | 39 | 0.8063 | 0.7333 |
0.3677 | 40.0 | 40 | 0.8360 | 0.6667 |
0.3677 | 41.0 | 41 | 0.7250 | 0.7333 |
0.3677 | 42.0 | 42 | 0.6228 | 0.8333 |
0.3677 | 43.0 | 43 | 0.7757 | 0.7667 |
0.3677 | 44.0 | 44 | 0.6767 | 0.7667 |
0.3677 | 45.0 | 45 | 0.8128 | 0.6667 |
0.3677 | 46.0 | 46 | 0.8584 | 0.7333 |
0.3677 | 47.0 | 47 | 0.8866 | 0.6333 |
0.3677 | 48.0 | 48 | 0.5143 | 0.8 |
0.3677 | 49.0 | 49 | 0.7074 | 0.7 |
0.192 | 50.0 | 50 | 0.7536 | 0.7333 |
0.192 | 51.0 | 51 | 0.7113 | 0.7667 |
0.192 | 52.0 | 52 | 0.8321 | 0.7 |
0.192 | 53.0 | 53 | 0.7229 | 0.7333 |
0.192 | 54.0 | 54 | 0.8839 | 0.7 |
0.192 | 55.0 | 55 | 0.5496 | 0.8 |
0.192 | 56.0 | 56 | 0.6769 | 0.8 |
0.192 | 57.0 | 57 | 0.5740 | 0.8333 |
0.192 | 58.0 | 58 | 0.6321 | 0.7667 |
0.192 | 59.0 | 59 | 0.7635 | 0.7667 |
0.1225 | 60.0 | 60 | 0.6869 | 0.8 |
0.1225 | 61.0 | 61 | 0.6595 | 0.8333 |
0.1225 | 62.0 | 62 | 0.5097 | 0.8 |
0.1225 | 63.0 | 63 | 0.5642 | 0.8333 |
0.1225 | 64.0 | 64 | 0.6503 | 0.8 |
0.1225 | 65.0 | 65 | 0.6877 | 0.7667 |
0.1225 | 66.0 | 66 | 0.6811 | 0.7333 |
0.1225 | 67.0 | 67 | 0.7854 | 0.7 |
0.1225 | 68.0 | 68 | 0.6378 | 0.8 |
0.1225 | 69.0 | 69 | 0.5148 | 0.8667 |
0.088 | 70.0 | 70 | 0.6571 | 0.8 |
0.088 | 71.0 | 71 | 0.7112 | 0.7333 |
0.088 | 72.0 | 72 | 0.6602 | 0.7333 |
0.088 | 73.0 | 73 | 0.4259 | 0.9 |
0.088 | 74.0 | 74 | 0.3600 | 0.8667 |
0.088 | 75.0 | 75 | 0.5726 | 0.8667 |
0.088 | 76.0 | 76 | 0.6638 | 0.7667 |
0.088 | 77.0 | 77 | 0.4641 | 0.8667 |
0.088 | 78.0 | 78 | 0.5186 | 0.8667 |
0.088 | 79.0 | 79 | 0.6024 | 0.8333 |
0.0581 | 80.0 | 80 | 0.4450 | 0.8667 |
0.0581 | 81.0 | 81 | 0.3267 | 0.9 |
0.0581 | 82.0 | 82 | 0.6442 | 0.8 |
0.0581 | 83.0 | 83 | 0.8130 | 0.8 |
0.0581 | 84.0 | 84 | 0.4391 | 0.8667 |
0.0581 | 85.0 | 85 | 0.5013 | 0.8333 |
0.0581 | 86.0 | 86 | 0.6059 | 0.8 |
0.0581 | 87.0 | 87 | 0.4916 | 0.8333 |
0.0581 | 88.0 | 88 | 0.4384 | 0.8667 |
0.0581 | 89.0 | 89 | 0.3366 | 0.9 |
0.0359 | 90.0 | 90 | 0.6082 | 0.7667 |
0.0359 | 91.0 | 91 | 0.2967 | 0.8333 |
0.0359 | 92.0 | 92 | 0.3954 | 0.9 |
0.0359 | 93.0 | 93 | 0.5354 | 0.8333 |
0.0359 | 94.0 | 94 | 0.4755 | 0.8667 |
0.0359 | 95.0 | 95 | 0.5060 | 0.8 |
0.0359 | 96.0 | 96 | 0.3716 | 0.8333 |
0.0359 | 97.0 | 97 | 0.3547 | 0.8333 |
0.0359 | 98.0 | 98 | 0.6991 | 0.8333 |
0.0359 | 99.0 | 99 | 0.4895 | 0.8333 |
0.0251 | 100.0 | 100 | 0.5676 | 0.8 |
0.0251 | 101.0 | 101 | 0.3866 | 0.8667 |
0.0251 | 102.0 | 102 | 0.7054 | 0.7667 |
0.0251 | 103.0 | 103 | 0.5586 | 0.8667 |
0.0251 | 104.0 | 104 | 0.5839 | 0.8333 |
0.0251 | 105.0 | 105 | 0.5091 | 0.8333 |
0.0251 | 106.0 | 106 | 0.5459 | 0.9 |
0.0251 | 107.0 | 107 | 0.4228 | 0.9 |
0.0251 | 108.0 | 108 | 0.4447 | 0.9 |
0.0251 | 109.0 | 109 | 0.5052 | 0.8667 |
0.023 | 110.0 | 110 | 0.5494 | 0.8 |
0.023 | 111.0 | 111 | 0.6297 | 0.8333 |
0.023 | 112.0 | 112 | 0.6272 | 0.8667 |
0.023 | 113.0 | 113 | 0.6439 | 0.8333 |
0.023 | 114.0 | 114 | 0.3996 | 0.8667 |
0.023 | 115.0 | 115 | 0.4805 | 0.8333 |
0.023 | 116.0 | 116 | 0.4350 | 0.9 |
0.023 | 117.0 | 117 | 0.7719 | 0.8 |
0.023 | 118.0 | 118 | 0.5710 | 0.8333 |
0.023 | 119.0 | 119 | 0.3579 | 0.9 |
0.0292 | 120.0 | 120 | 0.3309 | 0.9333 |
0.0292 | 121.0 | 121 | 0.2649 | 0.9333 |
0.0292 | 122.0 | 122 | 0.3858 | 0.8333 |
0.0292 | 123.0 | 123 | 0.6109 | 0.8 |
0.0292 | 124.0 | 124 | 0.4734 | 0.8 |
0.0292 | 125.0 | 125 | 0.5604 | 0.8333 |
0.0292 | 126.0 | 126 | 0.3509 | 0.8667 |
0.0292 | 127.0 | 127 | 0.4752 | 0.9 |
0.0292 | 128.0 | 128 | 0.2966 | 0.8667 |
0.0292 | 129.0 | 129 | 0.5186 | 0.8667 |
0.0155 | 130.0 | 130 | 0.4547 | 0.8667 |
0.0155 | 131.0 | 131 | 0.3391 | 0.9 |
0.0155 | 132.0 | 132 | 0.4527 | 0.8667 |
0.0155 | 133.0 | 133 | 0.4476 | 0.8667 |
0.0155 | 134.0 | 134 | 0.5800 | 0.8667 |
0.0155 | 135.0 | 135 | 0.4653 | 0.8667 |
0.0155 | 136.0 | 136 | 0.3927 | 0.9 |
0.0155 | 137.0 | 137 | 0.4538 | 0.8333 |
0.0155 | 138.0 | 138 | 0.3952 | 0.9 |
0.0155 | 139.0 | 139 | 0.3949 | 0.9333 |
0.0112 | 140.0 | 140 | 0.5354 | 0.9 |
0.0112 | 141.0 | 141 | 0.5307 | 0.8333 |
0.0112 | 142.0 | 142 | 0.4757 | 0.9 |
0.0112 | 143.0 | 143 | 0.4342 | 0.9 |
0.0112 | 144.0 | 144 | 0.3978 | 0.9 |
0.0112 | 145.0 | 145 | 0.5695 | 0.8333 |
0.0112 | 146.0 | 146 | 0.5495 | 0.8 |
0.0112 | 147.0 | 147 | 0.4235 | 0.8667 |
0.0112 | 148.0 | 148 | 0.3878 | 0.9 |
0.0112 | 149.0 | 149 | 0.5536 | 0.9 |
0.0066 | 150.0 | 150 | 0.4343 | 0.8333 |
0.0066 | 151.0 | 151 | 0.5915 | 0.8667 |
0.0066 | 152.0 | 152 | 0.6523 | 0.8 |
0.0066 | 153.0 | 153 | 0.6478 | 0.8333 |
0.0066 | 154.0 | 154 | 0.6078 | 0.8333 |
0.0066 | 155.0 | 155 | 0.5253 | 0.9 |
0.0066 | 156.0 | 156 | 0.5023 | 0.9 |
0.0066 | 157.0 | 157 | 0.6782 | 0.8667 |
0.0066 | 158.0 | 158 | 0.4155 | 0.9 |
0.0066 | 159.0 | 159 | 0.6239 | 0.8667 |
0.0063 | 160.0 | 160 | 0.4657 | 0.8667 |
0.0063 | 161.0 | 161 | 0.3858 | 0.9 |
0.0063 | 162.0 | 162 | 0.4525 | 0.8667 |
0.0063 | 163.0 | 163 | 0.2853 | 0.9 |
0.0063 | 164.0 | 164 | 0.3835 | 0.8667 |
0.0063 | 165.0 | 165 | 0.3866 | 0.8333 |
0.0063 | 166.0 | 166 | 0.5272 | 0.8 |
0.0063 | 167.0 | 167 | 0.5175 | 0.8667 |
0.0063 | 168.0 | 168 | 0.5153 | 0.9 |
0.0063 | 169.0 | 169 | 0.4730 | 0.8667 |
0.0048 | 170.0 | 170 | 0.6657 | 0.8333 |
0.0048 | 171.0 | 171 | 0.5745 | 0.9 |
0.0048 | 172.0 | 172 | 0.7296 | 0.8 |
0.0048 | 173.0 | 173 | 0.4948 | 0.8333 |
0.0048 | 174.0 | 174 | 0.4577 | 0.8667 |
0.0048 | 175.0 | 175 | 0.5703 | 0.8667 |
0.0048 | 176.0 | 176 | 0.7392 | 0.8333 |
0.0048 | 177.0 | 177 | 0.7239 | 0.9 |
0.0048 | 178.0 | 178 | 0.3202 | 0.9 |
0.0048 | 179.0 | 179 | 0.4292 | 0.9 |
0.003 | 180.0 | 180 | 0.6611 | 0.8333 |
0.003 | 181.0 | 181 | 0.4829 | 0.9 |
0.003 | 182.0 | 182 | 0.4959 | 0.8667 |
0.003 | 183.0 | 183 | 0.2886 | 0.9 |
0.003 | 184.0 | 184 | 0.6587 | 0.8667 |
0.003 | 185.0 | 185 | 0.7223 | 0.8 |
0.003 | 186.0 | 186 | 0.6374 | 0.8667 |
0.003 | 187.0 | 187 | 0.5756 | 0.8333 |
0.003 | 188.0 | 188 | 0.6505 | 0.8667 |
0.003 | 189.0 | 189 | 0.5517 | 0.8667 |
0.0025 | 190.0 | 190 | 0.5176 | 0.8667 |
0.0025 | 191.0 | 191 | 0.4003 | 0.9 |
0.0025 | 192.0 | 192 | 0.5594 | 0.8667 |
0.0025 | 193.0 | 193 | 0.4229 | 0.9 |
0.0025 | 194.0 | 194 | 0.5938 | 0.8667 |
0.0025 | 195.0 | 195 | 0.6415 | 0.8667 |
0.0025 | 196.0 | 196 | 0.5056 | 0.8667 |
0.0025 | 197.0 | 197 | 0.5146 | 0.9 |
0.0025 | 198.0 | 198 | 0.5822 | 0.8667 |
0.0025 | 199.0 | 199 | 0.6066 | 0.8667 |
0.0021 | 200.0 | 200 | 0.6110 | 0.8333 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
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Base model
WinKawaks/vit-tiny-patch16-224Evaluation results
- Accuracy on imagefoldervalidation set self-reported0.833