---
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-four-five
  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-four-five

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: 0.6029
- Accuracy: 0.6544
- F1: 0.6540
- Precision: 0.6564
- Recall: 0.6544

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7064        | 0.9455  | 13   | 0.7007          | 0.5023   | 0.3813 | 0.4662    | 0.5023 |
| 0.6997        | 1.9636  | 27   | 0.6937          | 0.5276   | 0.4406 | 0.6010    | 0.5276 |
| 0.6895        | 2.9818  | 41   | 0.6864          | 0.5276   | 0.4815 | 0.5549    | 0.5276 |
| 0.6862        | 4.0     | 55   | 0.6769          | 0.5922   | 0.5887 | 0.5983    | 0.5922 |
| 0.6745        | 4.9455  | 68   | 0.6455          | 0.6336   | 0.6336 | 0.6344    | 0.6336 |
| 0.6443        | 5.9636  | 82   | 0.6340          | 0.6406   | 0.6399 | 0.6406    | 0.6406 |
| 0.6243        | 6.9818  | 96   | 0.6220          | 0.6590   | 0.6534 | 0.6661    | 0.6590 |
| 0.6243        | 8.0     | 110  | 0.6151          | 0.6705   | 0.6668 | 0.6754    | 0.6705 |
| 0.6248        | 8.9455  | 123  | 0.6104          | 0.6567   | 0.6565 | 0.6566    | 0.6567 |
| 0.6149        | 9.9636  | 137  | 0.6100          | 0.6751   | 0.6734 | 0.6816    | 0.6751 |
| 0.5968        | 10.9818 | 151  | 0.6026          | 0.6705   | 0.6705 | 0.6713    | 0.6705 |
| 0.5813        | 12.0    | 165  | 0.6028          | 0.6590   | 0.6589 | 0.6602    | 0.6590 |
| 0.5892        | 12.9455 | 178  | 0.6059          | 0.6452   | 0.6420 | 0.6537    | 0.6452 |
| 0.5738        | 13.9636 | 192  | 0.6029          | 0.6544   | 0.6541 | 0.6561    | 0.6544 |
| 0.5738        | 14.1818 | 195  | 0.6029          | 0.6544   | 0.6540 | 0.6564    | 0.6544 |


### Framework versions

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