---
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-crop-neck-style
  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: 0.8127853881278538
    - name: Precision
      type: precision
      value: 0.8388735739429154
---

<!-- 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-crop-neck-style

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: 0.7591
- Accuracy: 0.8128
- Precision: 0.8389

## 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   | 88   | 0.7865          | 0.7717   | 0.8100    |
| No log        | 2.0   | 176  | 0.7591          | 0.8128   | 0.8389    |
| No log        | 3.0   | 264  | 0.8601          | 0.7854   | 0.8206    |
| No log        | 4.0   | 352  | 0.8518          | 0.8082   | 0.8454    |
| No log        | 5.0   | 440  | 0.7959          | 0.8128   | 0.8295    |


### Framework versions

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