---
license: apache-2.0
base_model: facebook/convnextv2-femto-1k-224
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: convnextv2-femto-1k-224-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-femto-1k-224-finetuned-galaxy10-decals

This model is a fine-tuned version of [facebook/convnextv2-femto-1k-224](https://huggingface.co/facebook/convnextv2-femto-1k-224) on the matthieulel/galaxy10_decals dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4463
- Accuracy: 0.8551
- Precision: 0.8509
- Recall: 0.8551
- F1: 0.8514

## 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