---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- feature-extraction
- zero-shot-image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-finetune-scrap
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: d071696/scraps1
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9
---

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

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 d071696/scraps1 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5546
- Accuracy: 0.9

## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results



### Framework versions

- Transformers 4.39.0
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2