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final-algae-swin-wirs
This model is a fine-tuned version of samitizerxu/final-algae-swin-wirs on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7645
- Accuracy: 0.6725
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-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|---|---|---|---|---|
| 1.3024 | 1.0 | 120 | 0.7645 | 0.6725 |
| 1.2375 | 2.0 | 240 | 0.8013 | 0.6673 |
| 1.1875 | 3.0 | 360 | 0.8251 | 0.6649 |
| 1.187 | 4.0 | 480 | 0.8960 | 0.6403 |
| 1.1472 | 5.0 | 600 | 0.9558 | 0.6244 |
| 1.1374 | 6.0 | 720 | 1.1951 | 0.4877 |
| 1.1114 | 7.0 | 840 | 1.1109 | 0.5358 |
| 1.1201 | 8.0 | 960 | 0.9724 | 0.6227 |
| 1.0801 | 9.0 | 1080 | 0.9913 | 0.5863 |
| 1.0995 | 10.0 | 1200 | 1.0117 | 0.5933 |
| 1.0817 | 11.0 | 1320 | 1.0239 | 0.5951 |
| 1.0679 | 12.0 | 1440 | 1.0381 | 0.5839 |
| 1.094 | 13.0 | 1560 | 1.0480 | 0.5910 |
| 1.0325 | 14.0 | 1680 | 1.0671 | 0.5839 |
| 1.0087 | 15.0 | 1800 | 1.0133 | 0.5892 |
| 1.0525 | 16.0 | 1920 | 1.0332 | 0.5775 |
| 1.0614 | 17.0 | 2040 | 1.0085 | 0.5939 |
| 1.0065 | 18.0 | 2160 | 1.0070 | 0.5974 |
| 1.0474 | 19.0 | 2280 | 1.0023 | 0.5898 |
| 1.0346 | 20.0 | 2400 | 1.0072 | 0.5839 |
| 1.0226 | 21.0 | 2520 | 1.0219 | 0.5792 |
| 1.0474 | 22.0 | 2640 | 1.0106 | 0.5880 |
| 0.983 | 23.0 | 2760 | 1.0020 | 0.5874 |
| 0.9997 | 24.0 | 2880 | 1.0838 | 0.5593 |
| 1.0074 | 25.0 | 3000 | 1.0781 | 0.5593 |
| 1.0 | 26.0 | 3120 | 1.0378 | 0.5751 |
| 1.0279 | 27.0 | 3240 | 1.0737 | 0.5604 |
| 0.9696 | 28.0 | 3360 | 1.1385 | 0.5123 |
| 0.9862 | 29.0 | 3480 | 1.1236 | 0.5282 |
| 1.0155 | 30.0 | 3600 | 1.0415 | 0.5798 |
| 0.9723 | 31.0 | 3720 | 1.1447 | 0.5258 |
| 0.9935 | 32.0 | 3840 | 1.1166 | 0.5323 |
| 0.9965 | 33.0 | 3960 | 1.0502 | 0.5716 |
| 0.9645 | 34.0 | 4080 | 1.1316 | 0.5329 |
| 0.9771 | 35.0 | 4200 | 1.1860 | 0.5170 |
| 0.9976 | 36.0 | 4320 | 1.2937 | 0.4906 |
| 0.9207 | 37.0 | 4440 | 1.2272 | 0.5135 |
| 0.9813 | 38.0 | 4560 | 1.2067 | 0.5258 |
| 0.9337 | 39.0 | 4680 | 1.2162 | 0.5282 |
| 0.9628 | 40.0 | 4800 | 1.2700 | 0.5059 |
| 0.9561 | 41.0 | 4920 | 1.2428 | 0.5094 |
| 0.9208 | 42.0 | 5040 | 1.2271 | 0.5158 |
| 0.9097 | 43.0 | 5160 | 1.2388 | 0.5182 |
| 0.9487 | 44.0 | 5280 | 1.1966 | 0.5264 |
| 0.9386 | 45.0 | 5400 | 1.2107 | 0.5258 |
| 0.9291 | 46.0 | 5520 | 1.2893 | 0.4977 |
| 0.9357 | 47.0 | 5640 | 1.2764 | 0.5041 |
| 0.9064 | 48.0 | 5760 | 1.2710 | 0.5012 |
| 0.9032 | 49.0 | 5880 | 1.2695 | 0.5 |
| 0.9423 | 50.0 | 6000 | 1.2703 | 0.4982 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
