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-v12
  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-v12

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: 0.5459
- Accuracy: 0.8521

## 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: 0.0001
- 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: 16
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6394        | 1.56  | 500  | 0.9654          | 0.6948   |
| 0.4475        | 3.12  | 1000 | 0.5877          | 0.7946   |
| 0.4785        | 4.67  | 1500 | 0.5604          | 0.7959   |
| 0.3922        | 6.23  | 2000 | 0.5464          | 0.8096   |
| 0.4523        | 7.79  | 2500 | 0.4912          | 0.8340   |
| 0.2246        | 9.35  | 3000 | 0.5322          | 0.8446   |
| 0.3309        | 10.9  | 3500 | 0.5195          | 0.8483   |
| 0.2458        | 12.46 | 4000 | 0.5645          | 0.8508   |
| 0.2353        | 14.02 | 4500 | 0.5430          | 0.8539   |
| 0.1077        | 15.58 | 5000 | 0.5459          | 0.8521   |


### Framework versions

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