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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- renovation
metrics:
- accuracy
model-index:
- name: vit-base-renovation
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: renovations
type: renovation
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6909090909090909
---
<!-- 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 renovations dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7101
- Accuracy: 0.6909
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0036 | 0.2 | 25 | 0.9849 | 0.5 |
| 0.8051 | 0.4 | 50 | 0.9106 | 0.5545 |
| 0.8336 | 0.6 | 75 | 0.9004 | 0.5955 |
| 0.786 | 0.81 | 100 | 0.7701 | 0.6455 |
| 0.7854 | 1.01 | 125 | 0.7561 | 0.6227 |
| 0.4603 | 1.21 | 150 | 0.8105 | 0.6409 |
| 0.4934 | 1.41 | 175 | 0.8746 | 0.6182 |
| 0.5315 | 1.61 | 200 | 0.8267 | 0.6636 |
| 0.5251 | 1.81 | 225 | 0.8585 | 0.65 |
| 0.4386 | 2.02 | 250 | 0.7101 | 0.6909 |
| 0.2627 | 2.22 | 275 | 1.0042 | 0.6409 |
| 0.1524 | 2.42 | 300 | 0.9489 | 0.6545 |
| 0.1272 | 2.62 | 325 | 1.0663 | 0.65 |
| 0.186 | 2.82 | 350 | 1.0831 | 0.6545 |
| 0.1544 | 3.02 | 375 | 1.1153 | 0.6364 |
| 0.0803 | 3.23 | 400 | 1.0399 | 0.6409 |
| 0.041 | 3.43 | 425 | 1.0911 | 0.6818 |
| 0.0685 | 3.63 | 450 | 1.1890 | 0.6591 |
| 0.0475 | 3.83 | 475 | 1.1725 | 0.6682 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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