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---
license: apache-2.0
base_model: facebook/convnextv2-tiny-22k-384
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: convnextv2-tiny-22k-384-finetuned-galaxy10-decals
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# convnextv2-tiny-22k-384-finetuned-galaxy10-decals
This model is a fine-tuned version of [facebook/convnextv2-tiny-22k-384](https://huggingface.co/facebook/convnextv2-tiny-22k-384) on the matthieulel/galaxy10_decals dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4641
- Accuracy: 0.8675
- Precision: 0.8664
- Recall: 0.8675
- F1: 0.8661
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.5875 | 0.99 | 62 | 1.3967 | 0.5423 | 0.5237 | 0.5423 | 0.5001 |
| 0.8561 | 2.0 | 125 | 0.7084 | 0.7773 | 0.7769 | 0.7773 | 0.7692 |
| 0.7139 | 2.99 | 187 | 0.5607 | 0.8230 | 0.8201 | 0.8230 | 0.8148 |
| 0.5799 | 4.0 | 250 | 0.4982 | 0.8410 | 0.8428 | 0.8410 | 0.8324 |
| 0.5352 | 4.99 | 312 | 0.4781 | 0.8461 | 0.8470 | 0.8461 | 0.8446 |
| 0.539 | 6.0 | 375 | 0.4538 | 0.8523 | 0.8578 | 0.8523 | 0.8482 |
| 0.5129 | 6.99 | 437 | 0.4496 | 0.8472 | 0.8486 | 0.8472 | 0.8468 |
| 0.4685 | 8.0 | 500 | 0.4458 | 0.8551 | 0.8589 | 0.8551 | 0.8542 |
| 0.4675 | 8.99 | 562 | 0.4352 | 0.8613 | 0.8651 | 0.8613 | 0.8579 |
| 0.441 | 10.0 | 625 | 0.4076 | 0.8636 | 0.8616 | 0.8636 | 0.8607 |
| 0.4214 | 10.99 | 687 | 0.4346 | 0.8517 | 0.8556 | 0.8517 | 0.8522 |
| 0.4016 | 12.0 | 750 | 0.4300 | 0.8591 | 0.8597 | 0.8591 | 0.8573 |
| 0.3913 | 12.99 | 812 | 0.4164 | 0.8625 | 0.8624 | 0.8625 | 0.8601 |
| 0.3882 | 14.0 | 875 | 0.4246 | 0.8591 | 0.8618 | 0.8591 | 0.8570 |
| 0.3341 | 14.99 | 937 | 0.4321 | 0.8574 | 0.8555 | 0.8574 | 0.8555 |
| 0.3522 | 16.0 | 1000 | 0.4322 | 0.8568 | 0.8561 | 0.8568 | 0.8542 |
| 0.2824 | 16.99 | 1062 | 0.4364 | 0.8608 | 0.8606 | 0.8608 | 0.8586 |
| 0.315 | 18.0 | 1125 | 0.4495 | 0.8579 | 0.8581 | 0.8579 | 0.8559 |
| 0.3084 | 18.99 | 1187 | 0.4536 | 0.8608 | 0.8593 | 0.8608 | 0.8590 |
| 0.2864 | 20.0 | 1250 | 0.4417 | 0.8630 | 0.8621 | 0.8630 | 0.8607 |
| 0.2654 | 20.99 | 1312 | 0.4585 | 0.8630 | 0.8628 | 0.8630 | 0.8610 |
| 0.3067 | 22.0 | 1375 | 0.4673 | 0.8557 | 0.8562 | 0.8557 | 0.8538 |
| 0.2771 | 22.99 | 1437 | 0.4679 | 0.8596 | 0.8577 | 0.8596 | 0.8577 |
| 0.2588 | 24.0 | 1500 | 0.4616 | 0.8653 | 0.8646 | 0.8653 | 0.8633 |
| 0.2583 | 24.99 | 1562 | 0.4726 | 0.8591 | 0.8572 | 0.8591 | 0.8567 |
| 0.2517 | 26.0 | 1625 | 0.4618 | 0.8630 | 0.8625 | 0.8630 | 0.8619 |
| 0.2454 | 26.99 | 1687 | 0.4612 | 0.8641 | 0.8630 | 0.8641 | 0.8629 |
| 0.259 | 28.0 | 1750 | 0.4685 | 0.8613 | 0.8595 | 0.8613 | 0.8594 |
| 0.2388 | 28.99 | 1812 | 0.4668 | 0.8636 | 0.8622 | 0.8636 | 0.8620 |
| 0.2414 | 29.76 | 1860 | 0.4641 | 0.8675 | 0.8664 | 0.8675 | 0.8661 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1
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