convnextv2-tiny-1k-224-finetuned-galaxy10-decals
This model is a fine-tuned version of facebook/convnextv2-tiny-1k-224 on the matthieulel/galaxy10_decals dataset. It achieves the following results on the evaluation set:
- Loss: 0.3993
- Accuracy: 0.8732
- Precision: 0.8714
- Recall: 0.8732
- F1: 0.8715
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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 1.8139 | 0.99 | 62 | 1.6803 | 0.4628 | 0.4589 | 0.4628 | 0.3836 |
| 1.0894 | 2.0 | 125 | 0.9304 | 0.6984 | 0.6965 | 0.6984 | 0.6800 |
| 0.8423 | 2.99 | 187 | 0.6630 | 0.7880 | 0.7858 | 0.7880 | 0.7826 |
| 0.6564 | 4.0 | 250 | 0.5769 | 0.8055 | 0.8091 | 0.8055 | 0.7970 |
| 0.5927 | 4.99 | 312 | 0.5283 | 0.8241 | 0.8276 | 0.8241 | 0.8240 |
| 0.5853 | 6.0 | 375 | 0.5106 | 0.8303 | 0.8342 | 0.8303 | 0.8237 |
| 0.5757 | 6.99 | 437 | 0.4490 | 0.8540 | 0.8514 | 0.8540 | 0.8521 |
| 0.5235 | 8.0 | 500 | 0.4651 | 0.8546 | 0.8578 | 0.8546 | 0.8536 |
| 0.5166 | 8.99 | 562 | 0.4501 | 0.8563 | 0.8551 | 0.8563 | 0.8523 |
| 0.486 | 10.0 | 625 | 0.4352 | 0.8647 | 0.8624 | 0.8647 | 0.8626 |
| 0.4882 | 10.99 | 687 | 0.4296 | 0.8613 | 0.8594 | 0.8613 | 0.8597 |
| 0.4426 | 12.0 | 750 | 0.4314 | 0.8579 | 0.8614 | 0.8579 | 0.8566 |
| 0.457 | 12.99 | 812 | 0.4226 | 0.8641 | 0.8642 | 0.8641 | 0.8624 |
| 0.4512 | 14.0 | 875 | 0.4319 | 0.8619 | 0.8653 | 0.8619 | 0.8591 |
| 0.4059 | 14.99 | 937 | 0.4124 | 0.8692 | 0.8675 | 0.8692 | 0.8681 |
| 0.4147 | 16.0 | 1000 | 0.3993 | 0.8732 | 0.8714 | 0.8732 | 0.8715 |
| 0.3721 | 16.99 | 1062 | 0.4116 | 0.8636 | 0.8609 | 0.8636 | 0.8604 |
| 0.3908 | 18.0 | 1125 | 0.4098 | 0.8675 | 0.8663 | 0.8675 | 0.8665 |
| 0.3836 | 18.99 | 1187 | 0.4188 | 0.8670 | 0.8651 | 0.8670 | 0.8651 |
| 0.3716 | 20.0 | 1250 | 0.4172 | 0.8681 | 0.8653 | 0.8681 | 0.8661 |
| 0.3484 | 20.99 | 1312 | 0.4404 | 0.8653 | 0.8649 | 0.8653 | 0.8628 |
| 0.3895 | 22.0 | 1375 | 0.4194 | 0.8698 | 0.8689 | 0.8698 | 0.8688 |
| 0.3452 | 22.99 | 1437 | 0.4447 | 0.8630 | 0.8634 | 0.8630 | 0.8621 |
| 0.341 | 24.0 | 1500 | 0.4253 | 0.8720 | 0.8722 | 0.8720 | 0.8712 |
| 0.3481 | 24.99 | 1562 | 0.4325 | 0.8681 | 0.8656 | 0.8681 | 0.8658 |
| 0.3115 | 26.0 | 1625 | 0.4340 | 0.8619 | 0.8609 | 0.8619 | 0.8603 |
| 0.313 | 26.99 | 1687 | 0.4329 | 0.8653 | 0.8644 | 0.8653 | 0.8644 |
| 0.3362 | 28.0 | 1750 | 0.4329 | 0.8653 | 0.8636 | 0.8653 | 0.8639 |
| 0.3056 | 28.99 | 1812 | 0.4342 | 0.8658 | 0.8645 | 0.8658 | 0.8644 |
| 0.3206 | 29.76 | 1860 | 0.4343 | 0.8664 | 0.8648 | 0.8664 | 0.8649 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1