hushem_5x_beit_base_adamax_00001_fold5
This model is a fine-tuned version of microsoft/beit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.6249
- Accuracy: 0.8049
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
1.2175 | 1.0 | 28 | 1.0638 | 0.5610 |
0.735 | 2.0 | 56 | 0.7985 | 0.6585 |
0.4163 | 3.0 | 84 | 0.6796 | 0.7561 |
0.279 | 4.0 | 112 | 0.5555 | 0.7805 |
0.1477 | 5.0 | 140 | 0.5113 | 0.7805 |
0.1105 | 6.0 | 168 | 0.4149 | 0.8049 |
0.0639 | 7.0 | 196 | 0.4515 | 0.8049 |
0.0449 | 8.0 | 224 | 0.4623 | 0.8049 |
0.0253 | 9.0 | 252 | 0.4728 | 0.8049 |
0.0316 | 10.0 | 280 | 0.4972 | 0.8049 |
0.0123 | 11.0 | 308 | 0.4732 | 0.8049 |
0.0128 | 12.0 | 336 | 0.4924 | 0.8049 |
0.0101 | 13.0 | 364 | 0.4570 | 0.8049 |
0.0111 | 14.0 | 392 | 0.4394 | 0.8049 |
0.0107 | 15.0 | 420 | 0.4434 | 0.8293 |
0.0064 | 16.0 | 448 | 0.5061 | 0.8049 |
0.0038 | 17.0 | 476 | 0.4264 | 0.8049 |
0.0038 | 18.0 | 504 | 0.4542 | 0.8049 |
0.0106 | 19.0 | 532 | 0.5345 | 0.8049 |
0.0043 | 20.0 | 560 | 0.5084 | 0.8049 |
0.0022 | 21.0 | 588 | 0.5182 | 0.8049 |
0.0136 | 22.0 | 616 | 0.4661 | 0.8049 |
0.005 | 23.0 | 644 | 0.4938 | 0.8293 |
0.0094 | 24.0 | 672 | 0.5151 | 0.8293 |
0.0106 | 25.0 | 700 | 0.5393 | 0.8049 |
0.0023 | 26.0 | 728 | 0.5196 | 0.8293 |
0.0018 | 27.0 | 756 | 0.5228 | 0.8293 |
0.0039 | 28.0 | 784 | 0.5509 | 0.8049 |
0.002 | 29.0 | 812 | 0.5472 | 0.8049 |
0.0023 | 30.0 | 840 | 0.5687 | 0.8049 |
0.0017 | 31.0 | 868 | 0.5888 | 0.8049 |
0.0023 | 32.0 | 896 | 0.5665 | 0.8049 |
0.0021 | 33.0 | 924 | 0.5478 | 0.8049 |
0.002 | 34.0 | 952 | 0.5621 | 0.8049 |
0.0027 | 35.0 | 980 | 0.5915 | 0.8049 |
0.0012 | 36.0 | 1008 | 0.6391 | 0.8049 |
0.0008 | 37.0 | 1036 | 0.6817 | 0.8049 |
0.0029 | 38.0 | 1064 | 0.6733 | 0.8049 |
0.0009 | 39.0 | 1092 | 0.6240 | 0.8049 |
0.0018 | 40.0 | 1120 | 0.6057 | 0.8049 |
0.0019 | 41.0 | 1148 | 0.6204 | 0.8049 |
0.0009 | 42.0 | 1176 | 0.6350 | 0.8049 |
0.0017 | 43.0 | 1204 | 0.6368 | 0.8049 |
0.006 | 44.0 | 1232 | 0.6329 | 0.8049 |
0.0022 | 45.0 | 1260 | 0.6324 | 0.8049 |
0.0014 | 46.0 | 1288 | 0.6308 | 0.8049 |
0.0013 | 47.0 | 1316 | 0.6209 | 0.8049 |
0.0019 | 48.0 | 1344 | 0.6248 | 0.8049 |
0.0007 | 49.0 | 1372 | 0.6249 | 0.8049 |
0.0012 | 50.0 | 1400 | 0.6249 | 0.8049 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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