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

This model is a fine-tuned version of [facebook/convnextv2-nano-1k-224](https://huggingface.co/facebook/convnextv2-nano-1k-224) on the matthieulel/galaxy10_decals dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7023
- Accuracy: 0.8591
- Precision: 0.8565
- Recall: 0.8591
- F1: 0.8566

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0509        | 0.99  | 62   | 0.9876          | 0.6561   | 0.6692    | 0.6561 | 0.6355 |
| 0.8315        | 2.0   | 125  | 0.6745          | 0.7638   | 0.7726    | 0.7638 | 0.7581 |
| 0.7659        | 2.99  | 187  | 0.6651          | 0.7897   | 0.8092    | 0.7897 | 0.7858 |
| 0.6645        | 4.0   | 250  | 0.6068          | 0.8027   | 0.8117    | 0.8027 | 0.8031 |
| 0.6212        | 4.99  | 312  | 0.5895          | 0.8061   | 0.8158    | 0.8061 | 0.8058 |
| 0.624         | 6.0   | 375  | 0.5128          | 0.8269   | 0.8223    | 0.8269 | 0.8220 |
| 0.5648        | 6.99  | 437  | 0.5219          | 0.8196   | 0.8239    | 0.8196 | 0.8171 |
| 0.5235        | 8.0   | 500  | 0.5652          | 0.8275   | 0.8303    | 0.8275 | 0.8270 |
| 0.5316        | 8.99  | 562  | 0.4804          | 0.8427   | 0.8431    | 0.8427 | 0.8396 |
| 0.4756        | 10.0  | 625  | 0.5345          | 0.8213   | 0.8188    | 0.8213 | 0.8169 |
| 0.4758        | 10.99 | 687  | 0.5560          | 0.8326   | 0.8324    | 0.8326 | 0.8302 |
| 0.4499        | 12.0  | 750  | 0.5283          | 0.8416   | 0.8458    | 0.8416 | 0.8423 |
| 0.4274        | 12.99 | 812  | 0.5347          | 0.8348   | 0.8364    | 0.8348 | 0.8337 |
| 0.4148        | 14.0  | 875  | 0.5326          | 0.8422   | 0.8396    | 0.8422 | 0.8383 |
| 0.3822        | 14.99 | 937  | 0.5116          | 0.8410   | 0.8442    | 0.8410 | 0.8412 |
| 0.3613        | 16.0  | 1000 | 0.6081          | 0.8230   | 0.8245    | 0.8230 | 0.8210 |
| 0.2903        | 16.99 | 1062 | 0.6212          | 0.8298   | 0.8289    | 0.8298 | 0.8275 |
| 0.3405        | 18.0  | 1125 | 0.6093          | 0.8377   | 0.8394    | 0.8377 | 0.8368 |
| 0.2999        | 18.99 | 1187 | 0.6482          | 0.8393   | 0.8352    | 0.8393 | 0.8356 |
| 0.2792        | 20.0  | 1250 | 0.6473          | 0.8484   | 0.8482    | 0.8484 | 0.8419 |
| 0.2681        | 20.99 | 1312 | 0.6710          | 0.8467   | 0.8428    | 0.8467 | 0.8425 |
| 0.2966        | 22.0  | 1375 | 0.6355          | 0.8534   | 0.8513    | 0.8534 | 0.8514 |
| 0.2609        | 22.99 | 1437 | 0.6850          | 0.8399   | 0.8406    | 0.8399 | 0.8397 |
| 0.2281        | 24.0  | 1500 | 0.7124          | 0.8444   | 0.8444    | 0.8444 | 0.8440 |
| 0.2354        | 24.99 | 1562 | 0.7317          | 0.8427   | 0.8394    | 0.8427 | 0.8395 |
| 0.2188        | 26.0  | 1625 | 0.6753          | 0.8512   | 0.8490    | 0.8512 | 0.8489 |
| 0.2118        | 26.99 | 1687 | 0.6865          | 0.8506   | 0.8495    | 0.8506 | 0.8494 |
| 0.2232        | 28.0  | 1750 | 0.7098          | 0.8557   | 0.8531    | 0.8557 | 0.8535 |
| 0.2104        | 28.99 | 1812 | 0.7023          | 0.8591   | 0.8565    | 0.8591 | 0.8566 |
| 0.1936        | 29.76 | 1860 | 0.7043          | 0.8557   | 0.8540    | 0.8557 | 0.8541 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1