metadata
license: apache-2.0
base_model: facebook/convnextv2-femto-1k-224
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: convnextv2-femto-1k-224-finetuned-galaxy10-decals
results: []
convnextv2-femto-1k-224-finetuned-galaxy10-decals
This model is a fine-tuned version of facebook/convnextv2-femto-1k-224 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4423
- Accuracy: 0.8517
- Precision: 0.8483
- Recall: 0.8517
- F1: 0.8489
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 |
|---|---|---|---|---|---|---|---|
| 1.7326 | 0.99 | 62 | 1.6140 | 0.4758 | 0.4530 | 0.4758 | 0.4312 |
| 1.1706 | 2.0 | 125 | 1.0827 | 0.6218 | 0.6294 | 0.6218 | 0.5983 |
| 0.9046 | 2.99 | 187 | 0.7418 | 0.7542 | 0.7566 | 0.7542 | 0.7351 |
| 0.7305 | 4.0 | 250 | 0.6540 | 0.7880 | 0.7823 | 0.7880 | 0.7789 |
| 0.6378 | 4.99 | 312 | 0.5903 | 0.8089 | 0.8054 | 0.8089 | 0.8047 |
| 0.6447 | 6.0 | 375 | 0.5915 | 0.7954 | 0.8041 | 0.7954 | 0.7865 |
| 0.6228 | 6.99 | 437 | 0.5513 | 0.8162 | 0.8201 | 0.8162 | 0.8164 |
| 0.5758 | 8.0 | 500 | 0.5553 | 0.8078 | 0.8094 | 0.8078 | 0.8033 |
| 0.5831 | 8.99 | 562 | 0.5207 | 0.8191 | 0.8246 | 0.8191 | 0.8162 |
| 0.537 | 10.0 | 625 | 0.4981 | 0.8286 | 0.8233 | 0.8286 | 0.8222 |
| 0.5322 | 10.99 | 687 | 0.4830 | 0.8337 | 0.8340 | 0.8337 | 0.8332 |
| 0.5171 | 12.0 | 750 | 0.4931 | 0.8253 | 0.8258 | 0.8253 | 0.8233 |
| 0.5092 | 12.99 | 812 | 0.4891 | 0.8360 | 0.8360 | 0.8360 | 0.8325 |
| 0.5245 | 14.0 | 875 | 0.4585 | 0.8450 | 0.8452 | 0.8450 | 0.8431 |
| 0.4585 | 14.99 | 937 | 0.4682 | 0.8422 | 0.8407 | 0.8422 | 0.8407 |
| 0.455 | 16.0 | 1000 | 0.4659 | 0.8388 | 0.8370 | 0.8388 | 0.8357 |
| 0.4175 | 16.99 | 1062 | 0.4633 | 0.8382 | 0.8363 | 0.8382 | 0.8351 |
| 0.4574 | 18.0 | 1125 | 0.4479 | 0.8450 | 0.8435 | 0.8450 | 0.8428 |
| 0.4593 | 18.99 | 1187 | 0.4577 | 0.8439 | 0.8446 | 0.8439 | 0.8430 |
| 0.4423 | 20.0 | 1250 | 0.4589 | 0.8461 | 0.8426 | 0.8461 | 0.8413 |
| 0.4141 | 20.99 | 1312 | 0.4732 | 0.8326 | 0.8339 | 0.8326 | 0.8299 |
| 0.4534 | 22.0 | 1375 | 0.4477 | 0.8461 | 0.8422 | 0.8461 | 0.8433 |
| 0.4011 | 22.99 | 1437 | 0.4614 | 0.8399 | 0.8403 | 0.8399 | 0.8390 |
| 0.4162 | 24.0 | 1500 | 0.4576 | 0.8450 | 0.8443 | 0.8450 | 0.8437 |
| 0.4291 | 24.99 | 1562 | 0.4609 | 0.8472 | 0.8441 | 0.8472 | 0.8439 |
| 0.3698 | 26.0 | 1625 | 0.4469 | 0.8506 | 0.8484 | 0.8506 | 0.8482 |
| 0.3957 | 26.99 | 1687 | 0.4488 | 0.8478 | 0.8464 | 0.8478 | 0.8464 |
| 0.4053 | 28.0 | 1750 | 0.4463 | 0.8551 | 0.8509 | 0.8551 | 0.8514 |
| 0.377 | 28.99 | 1812 | 0.4429 | 0.8540 | 0.8504 | 0.8540 | 0.8508 |
| 0.381 | 29.76 | 1860 | 0.4423 | 0.8517 | 0.8483 | 0.8517 | 0.8489 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1