transformers tensorboard safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:google/vit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
model-index:
- name: Fine-Tuned_Model3
  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: 0.608
    - name: F1
      type: f1
      value: 0.5096170704866357
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Fine-Tuned_Model3

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7362
- Accuracy: 0.608
- F1: 0.5096

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 3.2255        | 5.0   | 20   | 1.9574          | 0.512    | 0.3083 |
| 1.3773        | 10.0  | 40   | 0.8854          | 0.584    | 0.4617 |
| 0.869         | 15.0  | 60   | 0.7880          | 0.608    | 0.4795 |
| 0.7966        | 20.0  | 80   | 0.7732          | 0.6      | 0.4846 |
| 0.8458        | 25.0  | 100  | 0.7795          | 0.576    | 0.4112 |
| 0.8135        | 30.0  | 120  | 0.7362          | 0.608    | 0.5096 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
