convnextv2-nano-22k-224-finetuned-galaxy10-decals
This model is a fine-tuned version of facebook/convnextv2-nano-22k-224 on the matthieulel/galaxy10_decals dataset. It achieves the following results on the evaluation set:
- Loss: 0.4386
- Accuracy: 0.8687
- Precision: 0.8680
- Recall: 0.8687
- F1: 0.8662
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 |
|---|---|---|---|---|---|---|---|
| 1.7173 | 0.99 | 62 | 1.5497 | 0.4617 | 0.4305 | 0.4617 | 0.4119 |
| 0.9692 | 2.0 | 125 | 0.8180 | 0.7306 | 0.7295 | 0.7306 | 0.7246 |
| 0.7643 | 2.99 | 187 | 0.6075 | 0.7931 | 0.7921 | 0.7931 | 0.7880 |
| 0.6282 | 4.0 | 250 | 0.5506 | 0.8151 | 0.8112 | 0.8151 | 0.8068 |
| 0.5712 | 4.99 | 312 | 0.5769 | 0.7982 | 0.8129 | 0.7982 | 0.8002 |
| 0.5702 | 6.0 | 375 | 0.5195 | 0.8315 | 0.8351 | 0.8315 | 0.8225 |
| 0.5423 | 6.99 | 437 | 0.4890 | 0.8331 | 0.8296 | 0.8331 | 0.8303 |
| 0.4989 | 8.0 | 500 | 0.4764 | 0.8371 | 0.8361 | 0.8371 | 0.8342 |
| 0.4997 | 8.99 | 562 | 0.4725 | 0.8405 | 0.8393 | 0.8405 | 0.8365 |
| 0.476 | 10.0 | 625 | 0.4582 | 0.8467 | 0.8465 | 0.8467 | 0.8435 |
| 0.4603 | 10.99 | 687 | 0.4460 | 0.8489 | 0.8464 | 0.8489 | 0.8472 |
| 0.4318 | 12.0 | 750 | 0.4398 | 0.8534 | 0.8519 | 0.8534 | 0.8515 |
| 0.4387 | 12.99 | 812 | 0.4575 | 0.8613 | 0.8598 | 0.8613 | 0.8577 |
| 0.4357 | 14.0 | 875 | 0.4398 | 0.8568 | 0.8541 | 0.8568 | 0.8532 |
| 0.3944 | 14.99 | 937 | 0.4425 | 0.8540 | 0.8533 | 0.8540 | 0.8524 |
| 0.3961 | 16.0 | 1000 | 0.4394 | 0.8574 | 0.8555 | 0.8574 | 0.8542 |
| 0.3557 | 16.99 | 1062 | 0.4510 | 0.8523 | 0.8497 | 0.8523 | 0.8481 |
| 0.3881 | 18.0 | 1125 | 0.4399 | 0.8591 | 0.8590 | 0.8591 | 0.8577 |
| 0.3663 | 18.99 | 1187 | 0.4631 | 0.8546 | 0.8545 | 0.8546 | 0.8524 |
| 0.3691 | 20.0 | 1250 | 0.4439 | 0.8608 | 0.8585 | 0.8608 | 0.8577 |
| 0.3443 | 20.99 | 1312 | 0.4524 | 0.8568 | 0.8555 | 0.8568 | 0.8545 |
| 0.3728 | 22.0 | 1375 | 0.4386 | 0.8687 | 0.8680 | 0.8687 | 0.8662 |
| 0.3309 | 22.99 | 1437 | 0.4506 | 0.8585 | 0.8578 | 0.8585 | 0.8573 |
| 0.33 | 24.0 | 1500 | 0.4426 | 0.8630 | 0.8613 | 0.8630 | 0.8618 |
| 0.3541 | 24.99 | 1562 | 0.4625 | 0.8585 | 0.8561 | 0.8585 | 0.8560 |
| 0.2968 | 26.0 | 1625 | 0.4460 | 0.8613 | 0.8593 | 0.8613 | 0.8590 |
| 0.3031 | 26.99 | 1687 | 0.4492 | 0.8641 | 0.8630 | 0.8641 | 0.8628 |
| 0.3207 | 28.0 | 1750 | 0.4480 | 0.8664 | 0.8640 | 0.8664 | 0.8637 |
| 0.2949 | 28.99 | 1812 | 0.4478 | 0.8636 | 0.8614 | 0.8636 | 0.8615 |
| 0.2985 | 29.76 | 1860 | 0.4477 | 0.8636 | 0.8612 | 0.8636 | 0.8614 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1