---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- vision
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: imagenet2012-1k-subsampling-50-vit-base-patch16-224-in21k
  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. -->

# imagenet2012-1k-subsampling-50-vit-base-patch16-224-in21k

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 imagenet2012-1k-subsampling-50 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8563
- Accuracy: 0.8109

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 5.7852        | 1.0   | 5313  | 5.7565          | 0.6867   |
| 4.4299        | 2.0   | 10626 | 4.2553          | 0.7315   |
| 2.7934        | 3.0   | 15939 | 2.7094          | 0.7547   |
| 1.5784        | 4.0   | 21252 | 1.6554          | 0.7728   |
| 0.7426        | 5.0   | 26565 | 1.1836          | 0.7896   |
| 0.8495        | 6.0   | 31878 | 0.9912          | 0.8013   |
| 0.575         | 7.0   | 37191 | 0.9112          | 0.8041   |
| 0.7981        | 8.0   | 42504 | 0.8853          | 0.8052   |
| 0.7448        | 9.0   | 47817 | 0.8613          | 0.8113   |
| 0.3953        | 10.0  | 53130 | 0.8563          | 0.8109   |


### Framework versions

- Transformers 4.38.0
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.15.2