---
license: apache-2.0
base_model: facebook/convnextv2-tiny-1k-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
model-index:
- name: convnextv2-tiny-1k-224-finetuned-pattern-v2
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8075
    - name: Precision
      type: precision
      value: 0.815145699366171
---

<!-- 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-pattern-v2

This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5496
- Accuracy: 0.8075
- Precision: 0.8151

## 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: 2e-05
- train_batch_size: 10
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|
| No log        | 1.0   | 320  | 0.8829          | 0.7275   | 0.7493    |
| 1.2279        | 2.0   | 640  | 0.7396          | 0.7412   | 0.7517    |
| 1.2279        | 3.0   | 960  | 0.6526          | 0.7775   | 0.7902    |
| 0.6811        | 4.0   | 1280 | 0.5722          | 0.7975   | 0.8076    |
| 0.5073        | 5.0   | 1600 | 0.5496          | 0.8075   | 0.8151    |
| 0.5073        | 6.0   | 1920 | 0.6014          | 0.7887   | 0.7991    |
| 0.4098        | 7.0   | 2240 | 0.5759          | 0.8125   | 0.8171    |
| 0.3357        | 8.0   | 2560 | 0.6241          | 0.7987   | 0.8126    |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1