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vit-KAIYI
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: 0.4642
- F1: 0.6998
- Roc Auc: 0.8236
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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc |
|---|---|---|---|---|---|
| 0.4021 | 1.0 | 506 | 0.3330 | 0.5653 | 0.7446 |
| 0.244 | 2.0 | 1012 | 0.2762 | 0.6360 | 0.7862 |
| 0.1793 | 3.0 | 1518 | 0.2503 | 0.6857 | 0.8154 |
| 0.1382 | 4.0 | 2024 | 0.2399 | 0.6991 | 0.8232 |
| 0.1089 | 5.0 | 2530 | 0.2336 | 0.7010 | 0.8244 |
| 0.0869 | 6.0 | 3036 | 0.2307 | 0.7092 | 0.8291 |
| 0.0698 | 7.0 | 3542 | 0.2318 | 0.7134 | 0.8316 |
| 0.0563 | 8.0 | 4048 | 0.2402 | 0.7076 | 0.8282 |
| 0.0459 | 9.0 | 4554 | 0.2402 | 0.7040 | 0.8261 |
| 0.0371 | 10.0 | 5060 | 0.2514 | 0.7053 | 0.8269 |
| 0.0306 | 11.0 | 5566 | 0.2582 | 0.7065 | 0.8275 |
| 0.0249 | 12.0 | 6072 | 0.2672 | 0.7050 | 0.8267 |
| 0.0206 | 13.0 | 6578 | 0.2735 | 0.7060 | 0.8273 |
| 0.0171 | 14.0 | 7084 | 0.2822 | 0.7039 | 0.8261 |
| 0.0142 | 15.0 | 7590 | 0.2934 | 0.7085 | 0.8288 |
| 0.0122 | 16.0 | 8096 | 0.2980 | 0.7066 | 0.8276 |
| 0.0104 | 17.0 | 8602 | 0.3126 | 0.6994 | 0.8234 |
| 0.0087 | 18.0 | 9108 | 0.3213 | 0.7040 | 0.8261 |
| 0.0074 | 19.0 | 9614 | 0.3245 | 0.7042 | 0.8262 |
| 0.0063 | 20.0 | 10120 | 0.3412 | 0.7038 | 0.8260 |
| 0.0053 | 21.0 | 10626 | 0.3525 | 0.7031 | 0.8256 |
| 0.0044 | 22.0 | 11132 | 0.3584 | 0.7038 | 0.8260 |
| 0.0039 | 23.0 | 11638 | 0.3710 | 0.7026 | 0.8253 |
| 0.0033 | 24.0 | 12144 | 0.3787 | 0.7020 | 0.8249 |
| 0.0029 | 25.0 | 12650 | 0.3867 | 0.7023 | 0.8251 |
| 0.0025 | 26.0 | 13156 | 0.3946 | 0.7015 | 0.8246 |
| 0.0023 | 27.0 | 13662 | 0.4052 | 0.7004 | 0.8240 |
| 0.002 | 28.0 | 14168 | 0.4115 | 0.6996 | 0.8235 |
| 0.0018 | 29.0 | 14674 | 0.4196 | 0.7005 | 0.8240 |
| 0.0016 | 30.0 | 15180 | 0.4360 | 0.7013 | 0.8245 |
| 0.0014 | 31.0 | 15686 | 0.4297 | 0.7008 | 0.8242 |
| 0.0013 | 32.0 | 16192 | 0.4444 | 0.6980 | 0.8226 |
| 0.0012 | 33.0 | 16698 | 0.4420 | 0.7010 | 0.8243 |
| 0.0011 | 34.0 | 17204 | 0.4468 | 0.6998 | 0.8237 |
| 0.001 | 35.0 | 17710 | 0.4529 | 0.7005 | 0.8240 |
| 0.001 | 36.0 | 18216 | 0.4566 | 0.7000 | 0.8237 |
| 0.0009 | 37.0 | 18722 | 0.4599 | 0.6997 | 0.8236 |
| 0.0009 | 38.0 | 19228 | 0.4621 | 0.6999 | 0.8237 |
| 0.0009 | 39.0 | 19734 | 0.4649 | 0.6993 | 0.8233 |
| 0.0008 | 40.0 | 20240 | 0.4642 | 0.6998 | 0.8236 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
