transformers safetensors vit image-classification vision 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:
- image-classification
- vision
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-mgas
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: ./mgr/dataset/HF_DS
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7322834645669292
---

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# vit-base-mgas

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 ./mgr/dataset/HF_DS dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8530
- Accuracy: 0.7323

## 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: 5.0

### Training results

| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 1.4331        | 1.0   | 143  | 0.4803   | 1.3804          |
| 1.1653        | 2.0   | 286  | 0.6850   | 1.0843          |
| 1.0919        | 3.0   | 429  | 0.7165   | 0.9539          |
| 0.9689        | 4.0   | 572  | 0.7323   | 0.8724          |
| 0.9175        | 5.0   | 715  | 0.8530   | 0.7323          |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.15.0
- Tokenizers 0.15.0
