---
license: apache-2.0
base_model: NekoFi/content-manage-exp2
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: portrait_cosu_exp3
  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: 0.9148936170212766
    - name: Precision
      type: precision
      value: 0.9189941972920695
    - name: Recall
      type: recall
      value: 0.9148936170212766
    - name: F1
      type: f1
      value: 0.9152832982620216
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# portrait_cosu_exp3

This model is a fine-tuned version of [NekoFi/content-manage-exp2](https://huggingface.co/NekoFi/content-manage-exp2) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2524
- Accuracy: 0.9149
- Precision: 0.9190
- Recall: 0.9149
- F1: 0.9153
- Confusion Matrix: [[19, 1], [3, 24]]

## 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   |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------------------:|
| No log        | 0.9231 | 6    | 0.2921          | 0.8511   | 0.8527    | 0.8511 | 0.8515 | [[17, 3], [4, 23]] |
| 0.5415        | 2.0    | 13   | 0.2564          | 0.9362   | 0.9426    | 0.9362 | 0.9353 | [[17, 3], [0, 27]] |
| 0.5415        | 2.9231 | 19   | 0.3605          | 0.8723   | 0.8864    | 0.8723 | 0.8730 | [[19, 1], [5, 22]] |
| 0.378         | 3.6923 | 24   | 0.2524          | 0.9149   | 0.9190    | 0.9149 | 0.9153 | [[19, 1], [3, 24]] |


### Framework versions

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