---
license: apache-2.0
base_model: NekoFi/portrait_cosu_exp3
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: portrait_cosu_exp4
  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: 0.9037037037037037
    - name: Precision
      type: precision
      value: 0.9042846124813338
    - name: Recall
      type: recall
      value: 0.9037037037037037
    - name: F1
      type: f1
      value: 0.9035443167305236
---

<!-- 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. -->

# portrait_cosu_exp4

This model is a fine-tuned version of [NekoFi/portrait_cosu_exp3](https://huggingface.co/NekoFi/portrait_cosu_exp3) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2432
- Accuracy: 0.9037
- Precision: 0.9043
- Recall: 0.9037
- F1: 0.9035
- Confusion Matrix: [[66, 5], [8, 56]]

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Confusion Matrix    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------------------:|
| 0.3876        | 1.0   | 19   | 0.3650          | 0.8370   | 0.8555    | 0.8370 | 0.8336 | [[68, 3], [19, 45]] |
| 0.2696        | 2.0   | 38   | 0.2479          | 0.8963   | 0.8965    | 0.8963 | 0.8962 | [[65, 6], [8, 56]]  |
| 0.2143        | 3.0   | 57   | 0.2665          | 0.8889   | 0.8906    | 0.8889 | 0.8885 | [[66, 5], [10, 54]] |
| 0.1629        | 4.0   | 76   | 0.2432          | 0.9037   | 0.9043    | 0.9037 | 0.9035 | [[66, 5], [8, 56]]  |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1