---
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: 0.4384
- Accuracy: 0.8814
- Precision: 0.8793
- Recall: 0.8814
- F1: 0.8772

## 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: 25

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 4.8445        | 1.0   | 944   | 4.7488          | 0.0919   | 0.0214    | 0.0919 | 0.0266 |
| 3.8243        | 2.0   | 1888  | 3.6914          | 0.2379   | 0.1520    | 0.2379 | 0.1447 |
| 2.8783        | 3.0   | 2832  | 2.7011          | 0.4105   | 0.3433    | 0.4105 | 0.3235 |
| 2.1348        | 4.0   | 3776  | 1.9752          | 0.5652   | 0.5279    | 0.5652 | 0.5069 |
| 1.6456        | 5.0   | 4720  | 1.5225          | 0.6529   | 0.6274    | 0.6529 | 0.6134 |
| 1.3835        | 6.0   | 5664  | 1.2167          | 0.7106   | 0.6996    | 0.7106 | 0.6845 |
| 1.1258        | 7.0   | 6608  | 1.0067          | 0.7491   | 0.7394    | 0.7491 | 0.7272 |
| 1.0181        | 8.0   | 7552  | 0.8722          | 0.7819   | 0.7755    | 0.7819 | 0.7678 |
| 0.7829        | 9.0   | 8496  | 0.7752          | 0.8018   | 0.7987    | 0.8018 | 0.7899 |
| 0.7503        | 10.0  | 9440  | 0.6983          | 0.8202   | 0.8189    | 0.8202 | 0.8121 |
| 0.6534        | 11.0  | 10384 | 0.6392          | 0.8301   | 0.8280    | 0.8301 | 0.8220 |
| 0.6108        | 12.0  | 11328 | 0.5941          | 0.8422   | 0.8384    | 0.8422 | 0.8343 |
| 0.5087        | 13.0  | 12272 | 0.5659          | 0.8487   | 0.8462    | 0.8487 | 0.8416 |
| 0.528         | 14.0  | 13216 | 0.5379          | 0.8554   | 0.8536    | 0.8554 | 0.8495 |
| 0.4489        | 15.0  | 14160 | 0.5189          | 0.8589   | 0.8566    | 0.8589 | 0.8528 |
| 0.4252        | 16.0  | 15104 | 0.5072          | 0.8626   | 0.8610    | 0.8626 | 0.8579 |
| 0.4239        | 17.0  | 16048 | 0.4857          | 0.8686   | 0.8678    | 0.8686 | 0.8645 |
| 0.3951        | 18.0  | 16992 | 0.4796          | 0.8695   | 0.8675    | 0.8695 | 0.8645 |
| 0.3679        | 19.0  | 17936 | 0.4685          | 0.8739   | 0.8724    | 0.8739 | 0.8695 |
| 0.3694        | 20.0  | 18880 | 0.4604          | 0.8751   | 0.8720    | 0.8751 | 0.8697 |
| 0.3435        | 21.0  | 19824 | 0.4555          | 0.8777   | 0.8755    | 0.8777 | 0.8739 |
| 0.3204        | 22.0  | 20768 | 0.4479          | 0.8783   | 0.8763    | 0.8783 | 0.8744 |
| 0.3475        | 23.0  | 21712 | 0.4433          | 0.8794   | 0.8773    | 0.8794 | 0.8753 |
| 0.338         | 24.0  | 22656 | 0.4408          | 0.8809   | 0.8785    | 0.8809 | 0.8767 |
| 0.3437        | 25.0  | 23600 | 0.4384          | 0.8814   | 0.8793    | 0.8814 | 0.8772 |


### Framework versions

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