vit-msn-small-lateral_flow_ivalidation_train_test_4
This model is a fine-tuned version of facebook/vit-msn-small on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3980
- Accuracy: 0.8938
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-07
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8038 | 1.0 | 13 | 0.8368 | 0.4029 |
| 0.6874 | 2.0 | 26 | 0.8356 | 0.4029 |
| 0.6487 | 3.0 | 39 | 0.8336 | 0.3810 |
| 0.773 | 4.0 | 52 | 0.8307 | 0.3700 |
| 0.7002 | 5.0 | 65 | 0.8270 | 0.3480 |
| 0.6991 | 6.0 | 78 | 0.8223 | 0.3407 |
| 0.6809 | 7.0 | 91 | 0.8164 | 0.3480 |
| 0.7359 | 8.0 | 104 | 0.8093 | 0.3516 |
| 0.771 | 9.0 | 117 | 0.8017 | 0.3443 |
| 0.6855 | 10.0 | 130 | 0.7934 | 0.3443 |
| 0.6674 | 11.0 | 143 | 0.7851 | 0.3480 |
| 0.6296 | 12.0 | 156 | 0.7746 | 0.3810 |
| 0.5597 | 13.0 | 169 | 0.7643 | 0.3956 |
| 0.5636 | 14.0 | 182 | 0.7519 | 0.4066 |
| 0.5718 | 15.0 | 195 | 0.7382 | 0.4432 |
| 0.5527 | 16.0 | 208 | 0.7256 | 0.4579 |
| 0.5646 | 17.0 | 221 | 0.7115 | 0.5055 |
| 0.4843 | 18.0 | 234 | 0.6966 | 0.5275 |
| 0.492 | 19.0 | 247 | 0.6805 | 0.5788 |
| 0.4865 | 20.0 | 260 | 0.6630 | 0.6117 |
| 0.4198 | 21.0 | 273 | 0.6448 | 0.6410 |
| 0.4203 | 22.0 | 286 | 0.6280 | 0.6740 |
| 0.4547 | 23.0 | 299 | 0.6083 | 0.6923 |
| 0.3916 | 24.0 | 312 | 0.5909 | 0.7143 |
| 0.4329 | 25.0 | 325 | 0.5768 | 0.7289 |
| 0.4645 | 26.0 | 338 | 0.5629 | 0.7399 |
| 0.3376 | 27.0 | 351 | 0.5536 | 0.7436 |
| 0.4417 | 28.0 | 364 | 0.5417 | 0.7729 |
| 0.3908 | 29.0 | 377 | 0.5262 | 0.7619 |
| 0.3715 | 30.0 | 390 | 0.5130 | 0.7729 |
| 0.438 | 31.0 | 403 | 0.5059 | 0.7912 |
| 0.2937 | 32.0 | 416 | 0.4937 | 0.8022 |
| 0.2944 | 33.0 | 429 | 0.4871 | 0.8022 |
| 0.3474 | 34.0 | 442 | 0.4820 | 0.8059 |
| 0.2302 | 35.0 | 455 | 0.4776 | 0.7949 |
| 0.3543 | 36.0 | 468 | 0.4690 | 0.8022 |
| 0.3325 | 37.0 | 481 | 0.4640 | 0.8059 |
| 0.4004 | 38.0 | 494 | 0.4584 | 0.8095 |
| 0.3031 | 39.0 | 507 | 0.4548 | 0.8132 |
| 0.4862 | 40.0 | 520 | 0.4520 | 0.8095 |
| 0.2609 | 41.0 | 533 | 0.4498 | 0.8278 |
| 0.1859 | 42.0 | 546 | 0.4450 | 0.8462 |
| 0.2712 | 43.0 | 559 | 0.4408 | 0.8462 |
| 0.221 | 44.0 | 572 | 0.4387 | 0.8425 |
| 0.2328 | 45.0 | 585 | 0.4371 | 0.8498 |
| 0.3004 | 46.0 | 598 | 0.4339 | 0.8425 |
| 0.2036 | 47.0 | 611 | 0.4318 | 0.8462 |
| 0.1925 | 48.0 | 624 | 0.4299 | 0.8498 |
| 0.4543 | 49.0 | 637 | 0.4266 | 0.8498 |
| 0.4056 | 50.0 | 650 | 0.4251 | 0.8462 |
| 0.2326 | 51.0 | 663 | 0.4247 | 0.8498 |
| 0.327 | 52.0 | 676 | 0.4224 | 0.8571 |
| 0.2385 | 53.0 | 689 | 0.4193 | 0.8571 |
| 0.2876 | 54.0 | 702 | 0.4183 | 0.8571 |
| 0.2257 | 55.0 | 715 | 0.4162 | 0.8718 |
| 0.252 | 56.0 | 728 | 0.4150 | 0.8755 |
| 0.4299 | 57.0 | 741 | 0.4129 | 0.8645 |
| 0.3146 | 58.0 | 754 | 0.4124 | 0.8755 |
| 0.1993 | 59.0 | 767 | 0.4124 | 0.8755 |
| 0.2507 | 60.0 | 780 | 0.4118 | 0.8791 |
| 0.324 | 61.0 | 793 | 0.4101 | 0.8535 |
| 0.2303 | 62.0 | 806 | 0.4090 | 0.8718 |
| 0.2767 | 63.0 | 819 | 0.4072 | 0.8608 |
| 0.3318 | 64.0 | 832 | 0.4071 | 0.8681 |
| 0.1946 | 65.0 | 845 | 0.4064 | 0.8681 |
| 0.4204 | 66.0 | 858 | 0.4055 | 0.8608 |
| 0.3351 | 67.0 | 871 | 0.4031 | 0.8608 |
| 0.2772 | 68.0 | 884 | 0.4013 | 0.8645 |
| 0.2969 | 69.0 | 897 | 0.4000 | 0.8681 |
| 0.2755 | 70.0 | 910 | 0.4021 | 0.8901 |
| 0.2835 | 71.0 | 923 | 0.4005 | 0.8608 |
| 0.2487 | 72.0 | 936 | 0.3998 | 0.8608 |
| 0.2447 | 73.0 | 949 | 0.3987 | 0.8571 |
| 0.3512 | 74.0 | 962 | 0.3970 | 0.8718 |
| 0.2303 | 75.0 | 975 | 0.3975 | 0.8681 |
| 0.2271 | 76.0 | 988 | 0.3976 | 0.8791 |
| 0.2325 | 77.0 | 1001 | 0.3980 | 0.8938 |
| 0.2517 | 78.0 | 1014 | 0.3965 | 0.8901 |
| 0.2839 | 79.0 | 1027 | 0.3956 | 0.8938 |
| 0.1994 | 80.0 | 1040 | 0.3940 | 0.8828 |
| 0.4525 | 81.0 | 1053 | 0.3934 | 0.8864 |
| 0.2178 | 82.0 | 1066 | 0.3930 | 0.8828 |
| 0.2784 | 83.0 | 1079 | 0.3929 | 0.8901 |
| 0.1956 | 84.0 | 1092 | 0.3930 | 0.8901 |
| 0.2713 | 85.0 | 1105 | 0.3922 | 0.8828 |
| 0.2331 | 86.0 | 1118 | 0.3920 | 0.8828 |
| 0.3294 | 87.0 | 1131 | 0.3917 | 0.8864 |
| 0.2998 | 88.0 | 1144 | 0.3911 | 0.8864 |
| 0.3767 | 89.0 | 1157 | 0.3909 | 0.8864 |
| 0.3126 | 90.0 | 1170 | 0.3908 | 0.8828 |
| 0.2427 | 91.0 | 1183 | 0.3903 | 0.8791 |
| 0.2696 | 92.0 | 1196 | 0.3898 | 0.8828 |
| 0.2664 | 93.0 | 1209 | 0.3897 | 0.8828 |
| 0.3718 | 94.0 | 1222 | 0.3898 | 0.8828 |
| 0.2813 | 95.0 | 1235 | 0.3899 | 0.8828 |
| 0.3105 | 96.0 | 1248 | 0.3898 | 0.8828 |
| 0.2452 | 97.0 | 1261 | 0.3901 | 0.8828 |
| 0.2775 | 98.0 | 1274 | 0.3900 | 0.8828 |
| 0.3814 | 99.0 | 1287 | 0.3901 | 0.8828 |
| 0.2861 | 100.0 | 1300 | 0.3901 | 0.8828 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1