---
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-upperfull-mix
  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: 0.8004385964912281
    - name: Precision
      type: precision
      value: 0.8160100686256399
---

<!-- 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-upperfull-mix

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: 0.6187
- Accuracy: 0.8004
- Precision: 0.8160

## 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   | 183  | 2.1471          | 0.4693   | 0.5128    |
| No log        | 2.0   | 366  | 1.4576          | 0.6579   | 0.6955    |
| 1.9821        | 3.0   | 549  | 1.1372          | 0.6754   | 0.7183    |
| 1.9821        | 4.0   | 732  | 0.9214          | 0.7303   | 0.7659    |
| 1.9821        | 5.0   | 915  | 0.7792          | 0.7478   | 0.7661    |
| 0.8885        | 6.0   | 1098 | 0.7455          | 0.7654   | 0.7780    |
| 0.8885        | 7.0   | 1281 | 0.6756          | 0.7873   | 0.8020    |
| 0.8885        | 8.0   | 1464 | 0.6787          | 0.7807   | 0.7932    |
| 0.5696        | 9.0   | 1647 | 0.6694          | 0.7982   | 0.8099    |
| 0.5696        | 10.0  | 1830 | 0.6799          | 0.7741   | 0.7930    |
| 0.4056        | 11.0  | 2013 | 0.6187          | 0.8004   | 0.8160    |
| 0.4056        | 12.0  | 2196 | 0.6868          | 0.7675   | 0.8063    |
| 0.4056        | 13.0  | 2379 | 0.7525          | 0.7544   | 0.7803    |
| 0.2904        | 14.0  | 2562 | 0.6572          | 0.7895   | 0.8093    |


### Framework versions

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