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---
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: 0.9260450160771704
---
<!-- 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: 0.3599
- Accuracy: 0.9260
## 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0021 | 3.22 | 1000 | 0.3599 | 0.9260 |
### Framework versions
- Transformers 4.39.0
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|