---
base_model: d071696/vit-finetune-scrap
tags:
- image-classification
- image-feature-extraction
- image-to-text
- generated_from_trainer
datasets:
- arrow
metrics:
- accuracy
model-index:
- name: vit-finetune-scrap
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: d071696/scraps1
      type: arrow
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.954983922829582
---

<!-- 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-finetune-scrap

This model is a fine-tuned version of [d071696/vit-finetune-scrap](https://huggingface.co/d071696/vit-finetune-scrap) on the d071696/scraps1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1588
- Accuracy: 0.9550

## 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: 16
- 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

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1672        | 0.64  | 100  | 0.2250          | 0.9486   |
| 0.1277        | 1.28  | 200  | 0.2467          | 0.9373   |
| 0.0253        | 1.92  | 300  | 0.1588          | 0.9550   |
| 0.0224        | 2.56  | 400  | 0.1691          | 0.9534   |
| 0.0321        | 3.21  | 500  | 0.1751          | 0.9566   |
| 0.0112        | 3.85  | 600  | 0.1805          | 0.9550   |


### Framework versions

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