transformers tensorboard safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:google/vit-base-patch16-224-in21k license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-finetuned-mobile-eye-tracking-dataset-v2
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9230769230769231
---

<!-- 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-base-patch16-224-in21k-finetuned-mobile-eye-tracking-dataset-v2

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2615
- Accuracy: 0.9231

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 2    | 0.6641          | 0.6154   |
| No log        | 2.0   | 4    | 0.6343          | 0.6154   |
| No log        | 3.0   | 6    | 0.5990          | 0.6154   |
| No log        | 4.0   | 8    | 0.5438          | 0.8462   |
| No log        | 5.0   | 10   | 0.5108          | 0.9231   |
| No log        | 6.0   | 12   | 0.4413          | 0.8462   |
| No log        | 7.0   | 14   | 0.3947          | 0.8462   |
| No log        | 8.0   | 16   | 0.3568          | 0.9231   |
| No log        | 9.0   | 18   | 0.3297          | 0.9231   |
| 0.4923        | 10.0  | 20   | 0.3110          | 0.9231   |
| 0.4923        | 11.0  | 22   | 0.2988          | 0.9231   |
| 0.4923        | 12.0  | 24   | 0.2836          | 0.9231   |
| 0.4923        | 13.0  | 26   | 0.2702          | 0.9231   |
| 0.4923        | 14.0  | 28   | 0.2636          | 0.9231   |
| 0.4923        | 15.0  | 30   | 0.2615          | 0.9231   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
