---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: Har_Finetuned-ViT-Hybrid
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: image_folder
      type: image_folder
      config: har
      split: train
      args: har
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8994708994708994
---

<!-- 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. -->

# Har_Finetuned-ViT-Hybrid

This model is a fine-tuned version of [google/vit-hybrid-base-bit-384](https://huggingface.co/google/vit-hybrid-base-bit-384) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3383
- Accuracy: 0.8995

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7923        | 1.0   | 167  | 0.4420          | 0.8698   |
| 0.5555        | 2.0   | 334  | 0.3811          | 0.8820   |
| 0.4734        | 3.0   | 501  | 0.3448          | 0.8958   |
| 0.4019        | 4.0   | 668  | 0.3521          | 0.8926   |
| 0.3622        | 5.0   | 835  | 0.3505          | 0.8926   |
| 0.2921        | 6.0   | 1002 | 0.3383          | 0.8995   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2