hushem_1x_beit_base_rms_001_fold2
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: 1.5076
- Accuracy: 0.4222
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: 0.001
- 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 |
---|---|---|---|---|
No log | 1.0 | 6 | 3.8156 | 0.2444 |
3.6272 | 2.0 | 12 | 1.5830 | 0.2444 |
3.6272 | 3.0 | 18 | 1.3920 | 0.3556 |
1.5643 | 4.0 | 24 | 1.3724 | 0.4667 |
1.4312 | 5.0 | 30 | 1.3703 | 0.4 |
1.4312 | 6.0 | 36 | 1.5314 | 0.2444 |
1.3956 | 7.0 | 42 | 1.3355 | 0.3111 |
1.3956 | 8.0 | 48 | 1.4411 | 0.3111 |
1.3396 | 9.0 | 54 | 1.4040 | 0.2667 |
1.3553 | 10.0 | 60 | 1.2838 | 0.5111 |
1.3553 | 11.0 | 66 | 1.2321 | 0.4889 |
1.3209 | 12.0 | 72 | 1.1729 | 0.4667 |
1.3209 | 13.0 | 78 | 1.2333 | 0.3556 |
1.2531 | 14.0 | 84 | 1.5215 | 0.3111 |
1.2528 | 15.0 | 90 | 1.2938 | 0.3778 |
1.2528 | 16.0 | 96 | 1.3032 | 0.3333 |
1.2233 | 17.0 | 102 | 1.2266 | 0.4667 |
1.2233 | 18.0 | 108 | 1.4102 | 0.3778 |
1.176 | 19.0 | 114 | 1.5633 | 0.2889 |
1.185 | 20.0 | 120 | 1.2459 | 0.4444 |
1.185 | 21.0 | 126 | 1.2338 | 0.4444 |
1.1231 | 22.0 | 132 | 1.2463 | 0.3333 |
1.1231 | 23.0 | 138 | 1.1738 | 0.5333 |
1.1314 | 24.0 | 144 | 1.3614 | 0.3778 |
1.083 | 25.0 | 150 | 1.1802 | 0.5556 |
1.083 | 26.0 | 156 | 1.2064 | 0.4444 |
1.0558 | 27.0 | 162 | 1.2033 | 0.5111 |
1.0558 | 28.0 | 168 | 1.2614 | 0.4444 |
1.0238 | 29.0 | 174 | 1.1933 | 0.4667 |
1.0431 | 30.0 | 180 | 1.2411 | 0.3333 |
1.0431 | 31.0 | 186 | 1.1551 | 0.5111 |
0.9703 | 32.0 | 192 | 1.2204 | 0.5778 |
0.9703 | 33.0 | 198 | 1.2367 | 0.4667 |
0.9502 | 34.0 | 204 | 1.2552 | 0.4222 |
0.8685 | 35.0 | 210 | 1.2938 | 0.4889 |
0.8685 | 36.0 | 216 | 1.3314 | 0.4889 |
0.8441 | 37.0 | 222 | 1.4307 | 0.5111 |
0.8441 | 38.0 | 228 | 1.3667 | 0.5111 |
0.7558 | 39.0 | 234 | 1.4572 | 0.4444 |
0.7537 | 40.0 | 240 | 1.4773 | 0.4667 |
0.7537 | 41.0 | 246 | 1.5046 | 0.4222 |
0.7017 | 42.0 | 252 | 1.5076 | 0.4222 |
0.7017 | 43.0 | 258 | 1.5076 | 0.4222 |
0.7145 | 44.0 | 264 | 1.5076 | 0.4222 |
0.7028 | 45.0 | 270 | 1.5076 | 0.4222 |
0.7028 | 46.0 | 276 | 1.5076 | 0.4222 |
0.6738 | 47.0 | 282 | 1.5076 | 0.4222 |
0.6738 | 48.0 | 288 | 1.5076 | 0.4222 |
0.7171 | 49.0 | 294 | 1.5076 | 0.4222 |
0.6462 | 50.0 | 300 | 1.5076 | 0.4222 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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