---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: convnext-tiny-224_album_vitVMMRdb_make_model_album_pred
  results: []
---

<!-- 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-tiny-224_album_vitVMMRdb_make_model_album_pred

This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7064
- Accuracy: 0.8177
- Precision: 0.8089
- Recall: 0.8177
- F1: 0.8067

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 4.6105        | 1.0   | 839   | 4.5248          | 0.1097   | 0.0579    | 0.1097 | 0.0403 |
| 3.4711        | 2.0   | 1678  | 3.3162          | 0.3000   | 0.2302    | 0.3000 | 0.2097 |
| 2.6202        | 3.0   | 2517  | 2.4445          | 0.4709   | 0.4120    | 0.4709 | 0.3939 |
| 2.0614        | 4.0   | 3356  | 1.8839          | 0.5742   | 0.5389    | 0.5742 | 0.5168 |
| 1.7026        | 5.0   | 4195  | 1.5247          | 0.6436   | 0.6180    | 0.6436 | 0.6013 |
| 1.4288        | 6.0   | 5034  | 1.2768          | 0.6979   | 0.6810    | 0.6979 | 0.6686 |
| 1.1953        | 7.0   | 5873  | 1.0960          | 0.7323   | 0.7218    | 0.7323 | 0.7077 |
| 1.058         | 8.0   | 6712  | 0.9828          | 0.7548   | 0.7441    | 0.7548 | 0.7350 |
| 0.9691        | 9.0   | 7551  | 0.9018          | 0.7718   | 0.7616    | 0.7718 | 0.7536 |
| 0.8757        | 10.0  | 8390  | 0.8380          | 0.7893   | 0.7806    | 0.7893 | 0.7756 |
| 0.8446        | 11.0  | 9229  | 0.7905          | 0.7982   | 0.7913    | 0.7982 | 0.7859 |
| 0.7711        | 12.0  | 10068 | 0.7524          | 0.8069   | 0.7995    | 0.8069 | 0.7950 |
| 0.7689        | 13.0  | 10907 | 0.7283          | 0.8123   | 0.8043    | 0.8123 | 0.8009 |
| 0.6919        | 14.0  | 11746 | 0.7133          | 0.8148   | 0.8061    | 0.8148 | 0.8036 |
| 0.694         | 15.0  | 12585 | 0.7064          | 0.8177   | 0.8089    | 0.8177 | 0.8067 |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2