---
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-fullwear-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: 0.8229166666666666
    - name: Precision
      type: precision
      value: 0.8355769851835295
---

<!-- 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-fullwear-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: 0.5128
- Accuracy: 0.8229
- Precision: 0.8356

## 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   | 116  | 1.8085          | 0.5625   | 0.6486    |
| No log        | 2.0   | 232  | 1.2627          | 0.6771   | 0.7218    |
| No log        | 3.0   | 348  | 1.0071          | 0.6806   | 0.7166    |
| No log        | 4.0   | 464  | 0.8603          | 0.7188   | 0.7466    |
| 1.3688        | 5.0   | 580  | 0.7240          | 0.7708   | 0.8074    |
| 1.3688        | 6.0   | 696  | 0.7496          | 0.7535   | 0.7994    |
| 1.3688        | 7.0   | 812  | 0.5832          | 0.8056   | 0.8176    |
| 1.3688        | 8.0   | 928  | 0.5809          | 0.7986   | 0.8156    |
| 0.4904        | 9.0   | 1044 | 0.5456          | 0.7986   | 0.8052    |
| 0.4904        | 10.0  | 1160 | 0.5833          | 0.7951   | 0.8198    |
| 0.4904        | 11.0  | 1276 | 0.5782          | 0.7986   | 0.8069    |
| 0.4904        | 12.0  | 1392 | 0.5128          | 0.8229   | 0.8356    |
| 0.2966        | 13.0  | 1508 | 0.5421          | 0.8160   | 0.8319    |
| 0.2966        | 14.0  | 1624 | 0.6090          | 0.7847   | 0.8171    |
| 0.2966        | 15.0  | 1740 | 0.6090          | 0.8021   | 0.8135    |


### Framework versions

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