---
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-neckline
  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: 0.8095238095238095
    - name: Precision
      type: precision
      value: 0.8100590473699718
---

<!-- 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-neckline

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: 0.6160
- Accuracy: 0.8095
- Precision: 0.8101

## 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   | 84   | 1.4226          | 0.5619   | 0.5870    |
| No log        | 2.0   | 168  | 1.1924          | 0.5619   | 0.5663    |
| No log        | 3.0   | 252  | 0.9542          | 0.6952   | 0.7317    |
| No log        | 4.0   | 336  | 0.8255          | 0.7143   | 0.7224    |
| No log        | 5.0   | 420  | 0.7614          | 0.7190   | 0.7378    |
| 1.1937        | 6.0   | 504  | 0.7303          | 0.7381   | 0.7454    |
| 1.1937        | 7.0   | 588  | 0.6770          | 0.7667   | 0.7772    |
| 1.1937        | 8.0   | 672  | 0.6849          | 0.7667   | 0.7748    |
| 1.1937        | 9.0   | 756  | 0.6720          | 0.7381   | 0.7532    |
| 1.1937        | 10.0  | 840  | 0.7036          | 0.7286   | 0.7429    |
| 1.1937        | 11.0  | 924  | 0.6752          | 0.7619   | 0.7827    |
| 0.6846        | 12.0  | 1008 | 0.6399          | 0.7810   | 0.7860    |
| 0.6846        | 13.0  | 1092 | 0.6860          | 0.7381   | 0.7553    |
| 0.6846        | 14.0  | 1176 | 0.6827          | 0.7476   | 0.7644    |
| 0.6846        | 15.0  | 1260 | 0.6160          | 0.8095   | 0.8101    |
| 0.6846        | 16.0  | 1344 | 0.7032          | 0.7619   | 0.7695    |
| 0.6846        | 17.0  | 1428 | 0.6916          | 0.8048   | 0.8197    |
| 0.5051        | 18.0  | 1512 | 0.7070          | 0.7810   | 0.7891    |


### Framework versions

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