---
license: apache-2.0
base_model: facebook/convnextv2-base-22k-384
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: convnext-base-wd1e-8-4e-5
  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: 0.9468253968253968
---

<!-- 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-wd1e-8-4e-5

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: 0.2294
- Accuracy: 0.9468

## 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: 4e-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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6271        | 1.0   | 1099  | 0.3436          | 0.9054   |
| 0.4528        | 2.0   | 2198  | 0.2767          | 0.9256   |
| 0.3762        | 3.0   | 3297  | 0.2519          | 0.9268   |
| 0.2979        | 4.0   | 4396  | 0.2414          | 0.9372   |
| 0.2901        | 5.0   | 5495  | 0.2389          | 0.9427   |
| 0.2381        | 6.0   | 6594  | 0.2408          | 0.9419   |
| 0.2084        | 7.0   | 7693  | 0.2312          | 0.9463   |
| 0.1742        | 8.0   | 8792  | 0.2359          | 0.9451   |
| 0.1582        | 9.0   | 9891  | 0.2364          | 0.9479   |
| 0.1451        | 10.0  | 10990 | 0.2357          | 0.9495   |


### Framework versions

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