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sdss-cnn
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1573
- Accuracy: 0.9505
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: 0.0001
- train_batch_size: 100
- eval_batch_size: 100
- 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 | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 80 | 0.4954 | 0.8635 |
| No log | 2.0 | 160 | 0.2788 | 0.9055 |
| No log | 3.0 | 240 | 0.2239 | 0.9085 |
| No log | 4.0 | 320 | 0.1991 | 0.9325 |
| No log | 5.0 | 400 | 0.1954 | 0.94 |
| No log | 6.0 | 480 | 0.1854 | 0.9445 |
| 0.3543 | 7.0 | 560 | 0.1891 | 0.9375 |
| 0.3543 | 8.0 | 640 | 0.1777 | 0.943 |
| 0.3543 | 9.0 | 720 | 0.1780 | 0.9415 |
| 0.3543 | 10.0 | 800 | 0.1804 | 0.942 |
| 0.3543 | 11.0 | 880 | 0.1734 | 0.9475 |
| 0.3543 | 12.0 | 960 | 0.1689 | 0.947 |
| 0.2022 | 13.0 | 1040 | 0.1698 | 0.9445 |
| 0.2022 | 14.0 | 1120 | 0.1689 | 0.9405 |
| 0.2022 | 15.0 | 1200 | 0.1650 | 0.9475 |
| 0.2022 | 16.0 | 1280 | 0.1755 | 0.934 |
| 0.2022 | 17.0 | 1360 | 0.1635 | 0.944 |
| 0.2022 | 18.0 | 1440 | 0.1711 | 0.942 |
| 0.1836 | 19.0 | 1520 | 0.1604 | 0.9485 |
| 0.1836 | 20.0 | 1600 | 0.1595 | 0.95 |
| 0.1836 | 21.0 | 1680 | 0.1613 | 0.9475 |
| 0.1836 | 22.0 | 1760 | 0.1579 | 0.949 |
| 0.1836 | 23.0 | 1840 | 0.1593 | 0.946 |
| 0.1836 | 24.0 | 1920 | 0.1579 | 0.945 |
| 0.167 | 25.0 | 2000 | 0.1584 | 0.9495 |
| 0.167 | 26.0 | 2080 | 0.1573 | 0.9505 |
| 0.167 | 27.0 | 2160 | 0.1596 | 0.945 |
| 0.167 | 28.0 | 2240 | 0.1599 | 0.9435 |
| 0.167 | 29.0 | 2320 | 0.1565 | 0.9485 |
| 0.167 | 30.0 | 2400 | 0.1582 | 0.946 |
| 0.167 | 31.0 | 2480 | 0.1563 | 0.95 |
| 0.1568 | 32.0 | 2560 | 0.1563 | 0.95 |
| 0.1568 | 33.0 | 2640 | 0.1573 | 0.9495 |
| 0.1568 | 34.0 | 2720 | 0.1564 | 0.9465 |
| 0.1568 | 35.0 | 2800 | 0.1557 | 0.95 |
| 0.1568 | 36.0 | 2880 | 0.1554 | 0.949 |
| 0.1568 | 37.0 | 2960 | 0.1562 | 0.948 |
| 0.1515 | 38.0 | 3040 | 0.1555 | 0.948 |
| 0.1515 | 39.0 | 3120 | 0.1557 | 0.95 |
| 0.1515 | 40.0 | 3200 | 0.1559 | 0.9485 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
