---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: MultiLabel_V3
  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. -->

# MultiLabel_V3

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9683
- Accuracy: 0.7370

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8572        | 0.1   | 100  | 1.1607          | 0.6466   |
| 0.8578        | 0.2   | 200  | 1.1956          | 0.6499   |
| 0.7362        | 0.3   | 300  | 1.1235          | 0.6885   |
| 0.8569        | 0.39  | 400  | 1.0460          | 0.6891   |
| 0.4851        | 0.49  | 500  | 1.1213          | 0.6891   |
| 0.7252        | 0.59  | 600  | 1.1512          | 0.6720   |
| 0.6333        | 0.69  | 700  | 1.1039          | 0.6913   |
| 0.6239        | 0.79  | 800  | 1.0636          | 0.7001   |
| 0.2768        | 0.89  | 900  | 1.0386          | 0.7073   |
| 0.4872        | 0.99  | 1000 | 1.0311          | 0.7062   |
| 0.3049        | 1.09  | 1100 | 1.0437          | 0.7155   |
| 0.1435        | 1.18  | 1200 | 1.0343          | 0.7222   |
| 0.2088        | 1.28  | 1300 | 1.0784          | 0.7194   |
| 0.4972        | 1.38  | 1400 | 1.1072          | 0.7166   |
| 0.3604        | 1.48  | 1500 | 1.0438          | 0.7150   |
| 0.2726        | 1.58  | 1600 | 1.0077          | 0.7293   |
| 0.3106        | 1.68  | 1700 | 1.0029          | 0.7326   |
| 0.3259        | 1.78  | 1800 | 0.9906          | 0.7310   |
| 0.3323        | 1.88  | 1900 | 0.9729          | 0.7359   |
| 0.2998        | 1.97  | 2000 | 0.9683          | 0.7370   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2