transformers tensorboard safetensors vit image-classification generated_from_trainer base_model:google/vit-base-patch16-224-in21k license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-dropout-v5
  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. -->

# vit-dropout-v5

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5648
- Accuracy: 0.8670

## 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: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6877        | 1.56  | 500  | 0.8813          | 0.7185   |
| 0.509         | 3.12  | 1000 | 0.5928          | 0.7834   |
| 0.4729        | 4.67  | 1500 | 0.5438          | 0.8015   |
| 0.5043        | 6.23  | 2000 | 0.5439          | 0.8102   |
| 0.456         | 7.79  | 2500 | 0.4814          | 0.8246   |
| 0.2336        | 9.35  | 3000 | 0.5169          | 0.8452   |
| 0.3513        | 10.9  | 3500 | 0.4543          | 0.8527   |
| 0.2456        | 12.46 | 4000 | 0.5365          | 0.8583   |
| 0.2029        | 14.02 | 4500 | 0.5582          | 0.8489   |
| 0.0715        | 15.58 | 5000 | 0.5121          | 0.8633   |
| 0.0561        | 17.13 | 5500 | 0.5564          | 0.8702   |
| 0.035         | 18.69 | 6000 | 0.5648          | 0.8670   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
