---
license: apache-2.0
base_model: facebook/convnextv2-base-22k-384
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: convnext-base-3e-5-wd-1e-8-raug
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9458333333333333
---

<!-- 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. -->

# convnext-base-3e-5-wd-1e-8-raug

This model is a fine-tuned version of [facebook/convnextv2-base-22k-384](https://huggingface.co/facebook/convnextv2-base-22k-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2296
- Accuracy: 0.9458

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6237        | 1.0   | 1099  | 0.3587          | 0.8994   |
| 0.4599        | 2.0   | 2198  | 0.2743          | 0.9213   |
| 0.359         | 3.0   | 3297  | 0.2579          | 0.9252   |
| 0.3047        | 4.0   | 4396  | 0.2404          | 0.9388   |
| 0.2869        | 5.0   | 5495  | 0.2348          | 0.9408   |
| 0.2468        | 6.0   | 6594  | 0.2276          | 0.9455   |
| 0.2098        | 7.0   | 7693  | 0.2303          | 0.9471   |
| 0.1944        | 8.0   | 8792  | 0.2244          | 0.9495   |
| 0.1739        | 9.0   | 9891  | 0.2247          | 0.9507   |
| 0.1508        | 10.0  | 10990 | 0.2243          | 0.9487   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2