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---
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
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