---
license: apache-2.0
base_model: facebook/convnextv2-tiny-1k-224
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: convnextv2-tiny-1k-224-finetuned-two-four
  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-1k-224-finetuned-two-four

This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5662
- Accuracy: 0.7352
- F1: 0.7327
- Precision: 0.7370
- Recall: 0.7352

## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6983        | 0.9655  | 14   | 0.6720          | 0.5974   | 0.5694 | 0.6054    | 0.5974 |
| 0.6804        | 2.0     | 29   | 0.6609          | 0.6324   | 0.6190 | 0.6374    | 0.6324 |
| 0.6796        | 2.9655  | 43   | 0.6634          | 0.6083   | 0.6084 | 0.6084    | 0.6083 |
| 0.6886        | 4.0     | 58   | 0.6547          | 0.6171   | 0.6104 | 0.6161    | 0.6171 |
| 0.6577        | 4.9655  | 72   | 0.6577          | 0.6127   | 0.5724 | 0.6407    | 0.6127 |
| 0.6439        | 6.0     | 87   | 0.6196          | 0.6477   | 0.6339 | 0.6559    | 0.6477 |
| 0.602         | 6.9655  | 101  | 0.6125          | 0.6652   | 0.6585 | 0.6986    | 0.6652 |
| 0.5974        | 8.0     | 116  | 0.6224          | 0.6696   | 0.6601 | 0.7141    | 0.6696 |
| 0.5841        | 8.9655  | 130  | 0.5800          | 0.7002   | 0.7005 | 0.7011    | 0.7002 |
| 0.581         | 10.0    | 145  | 0.5822          | 0.7265   | 0.7262 | 0.7262    | 0.7265 |
| 0.5716        | 10.9655 | 159  | 0.5812          | 0.7068   | 0.7035 | 0.7083    | 0.7068 |
| 0.5611        | 12.0    | 174  | 0.5778          | 0.7221   | 0.7150 | 0.7319    | 0.7221 |
| 0.5411        | 12.9655 | 188  | 0.5652          | 0.7352   | 0.7341 | 0.7351    | 0.7352 |
| 0.5361        | 14.0    | 203  | 0.5670          | 0.7374   | 0.7347 | 0.7395    | 0.7374 |
| 0.5416        | 14.4828 | 210  | 0.5662          | 0.7352   | 0.7327 | 0.7370    | 0.7352 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1