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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- renovation
metrics:
- accuracy
model-index:
- name: vit-base-renovation
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: renovation
type: renovation
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6470588235294118
---
<!-- 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-base-renovation
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 renovation dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2346
- Accuracy: 0.6471
## 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9092 | 1.67 | 100 | 0.8281 | 0.5686 |
| 0.3809 | 3.33 | 200 | 0.7651 | 0.6667 |
| 0.1873 | 5.0 | 300 | 1.0182 | 0.6667 |
| 0.019 | 6.67 | 400 | 1.2346 | 0.6471 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|