vit-msn-small-beta-fia-manually-enhanced-HSV_test_5
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: 0.3267
- Accuracy: 0.9167
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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 320
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.7143 | 1 | 1.1106 | 0.2292 |
| No log | 1.4286 | 2 | 1.0984 | 0.2569 |
| No log | 2.8571 | 4 | 1.0400 | 0.4097 |
| No log | 3.5714 | 5 | 0.9960 | 0.5486 |
| No log | 5.0 | 7 | 0.8868 | 0.7292 |
| No log | 5.7143 | 8 | 0.8263 | 0.7778 |
| No log | 6.4286 | 9 | 0.7651 | 0.8056 |
| 0.9808 | 7.8571 | 11 | 0.6521 | 0.8125 |
| 0.9808 | 8.5714 | 12 | 0.6052 | 0.8125 |
| 0.9808 | 10.0 | 14 | 0.5388 | 0.8125 |
| 0.9808 | 10.7143 | 15 | 0.5174 | 0.8125 |
| 0.9808 | 11.4286 | 16 | 0.5032 | 0.8125 |
| 0.9808 | 12.8571 | 18 | 0.5022 | 0.8125 |
| 0.9808 | 13.5714 | 19 | 0.5044 | 0.8194 |
| 0.5431 | 15.0 | 21 | 0.4773 | 0.8264 |
| 0.5431 | 15.7143 | 22 | 0.4439 | 0.8333 |
| 0.5431 | 16.4286 | 23 | 0.4198 | 0.8403 |
| 0.5431 | 17.8571 | 25 | 0.3873 | 0.8819 |
| 0.5431 | 18.5714 | 26 | 0.3730 | 0.8889 |
| 0.5431 | 20.0 | 28 | 0.3774 | 0.9028 |
| 0.5431 | 20.7143 | 29 | 0.3705 | 0.9097 |
| 0.4028 | 21.4286 | 30 | 0.3587 | 0.9097 |
| 0.4028 | 22.8571 | 32 | 0.3662 | 0.8958 |
| 0.4028 | 23.5714 | 33 | 0.3779 | 0.8681 |
| 0.4028 | 25.0 | 35 | 0.4322 | 0.8264 |
| 0.4028 | 25.7143 | 36 | 0.3944 | 0.8333 |
| 0.4028 | 26.4286 | 37 | 0.3585 | 0.8889 |
| 0.4028 | 27.8571 | 39 | 0.3608 | 0.8889 |
| 0.3497 | 28.5714 | 40 | 0.3972 | 0.8472 |
| 0.3497 | 30.0 | 42 | 0.3805 | 0.8611 |
| 0.3497 | 30.7143 | 43 | 0.3611 | 0.8819 |
| 0.3497 | 31.4286 | 44 | 0.3267 | 0.9167 |
| 0.3497 | 32.8571 | 46 | 0.3403 | 0.9028 |
| 0.3497 | 33.5714 | 47 | 0.3751 | 0.875 |
| 0.3497 | 35.0 | 49 | 0.3801 | 0.8681 |
| 0.3278 | 35.7143 | 50 | 0.3499 | 0.8958 |
| 0.3278 | 36.4286 | 51 | 0.3384 | 0.8958 |
| 0.3278 | 37.8571 | 53 | 0.3642 | 0.8542 |
| 0.3278 | 38.5714 | 54 | 0.3997 | 0.8194 |
| 0.3278 | 40.0 | 56 | 0.3843 | 0.8403 |
| 0.3278 | 40.7143 | 57 | 0.3676 | 0.8681 |
| 0.3278 | 41.4286 | 58 | 0.3464 | 0.9028 |
| 0.3334 | 42.8571 | 60 | 0.3618 | 0.8819 |
| 0.3334 | 43.5714 | 61 | 0.4006 | 0.8194 |
| 0.3334 | 45.0 | 63 | 0.4931 | 0.7639 |
| 0.3334 | 45.7143 | 64 | 0.4845 | 0.7708 |
| 0.3334 | 46.4286 | 65 | 0.4485 | 0.7917 |
| 0.3334 | 47.8571 | 67 | 0.3783 | 0.8472 |
| 0.3334 | 48.5714 | 68 | 0.3723 | 0.8472 |
| 0.3334 | 50.0 | 70 | 0.4077 | 0.8125 |
| 0.3334 | 50.7143 | 71 | 0.4381 | 0.7986 |
| 0.3334 | 51.4286 | 72 | 0.4627 | 0.7847 |
| 0.3334 | 52.8571 | 74 | 0.4445 | 0.7986 |
| 0.3334 | 53.5714 | 75 | 0.4141 | 0.8125 |
| 0.3334 | 55.0 | 77 | 0.3489 | 0.8681 |
| 0.3334 | 55.7143 | 78 | 0.3371 | 0.8958 |
| 0.3334 | 56.4286 | 79 | 0.3358 | 0.8889 |
| 0.3105 | 57.8571 | 81 | 0.3539 | 0.8681 |
| 0.3105 | 58.5714 | 82 | 0.3678 | 0.8542 |
| 0.3105 | 60.0 | 84 | 0.3931 | 0.8264 |
| 0.3105 | 60.7143 | 85 | 0.3938 | 0.8264 |
| 0.3105 | 61.4286 | 86 | 0.3897 | 0.8472 |
| 0.3105 | 62.8571 | 88 | 0.3638 | 0.8611 |
| 0.3105 | 63.5714 | 89 | 0.3496 | 0.875 |
| 0.3061 | 65.0 | 91 | 0.3305 | 0.8958 |
| 0.3061 | 65.7143 | 92 | 0.3284 | 0.9028 |
| 0.3061 | 66.4286 | 93 | 0.3284 | 0.8958 |
| 0.3061 | 67.8571 | 95 | 0.3337 | 0.8958 |
| 0.3061 | 68.5714 | 96 | 0.3374 | 0.8889 |
| 0.3061 | 70.0 | 98 | 0.3442 | 0.875 |
| 0.3061 | 70.7143 | 99 | 0.3452 | 0.875 |
| 0.3137 | 71.4286 | 100 | 0.3460 | 0.875 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1