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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-renovation2
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: 0.6666666666666666
---
<!-- 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-renovation2
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: 0.8273
- Accuracy: 0.6667
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.359 | 0.2 | 25 | 1.2074 | 0.4658 |
| 1.1384 | 0.4 | 50 | 1.1213 | 0.5205 |
| 1.0866 | 0.6 | 75 | 0.9746 | 0.6301 |
| 1.1787 | 0.81 | 100 | 1.0523 | 0.5662 |
| 0.9242 | 1.01 | 125 | 0.9543 | 0.6256 |
| 0.7945 | 1.21 | 150 | 0.9200 | 0.6119 |
| 0.8379 | 1.41 | 175 | 0.8447 | 0.6712 |
| 0.7253 | 1.61 | 200 | 0.8642 | 0.6575 |
| 0.6344 | 1.81 | 225 | 0.8443 | 0.6438 |
| 0.6521 | 2.02 | 250 | 0.8273 | 0.6667 |
| 0.3627 | 2.22 | 275 | 0.8653 | 0.6712 |
| 0.2523 | 2.42 | 300 | 0.8748 | 0.6895 |
| 0.363 | 2.62 | 325 | 0.8407 | 0.6849 |
| 0.3433 | 2.82 | 350 | 0.9696 | 0.6484 |
| 0.2874 | 3.02 | 375 | 0.9290 | 0.6804 |
| 0.1682 | 3.23 | 400 | 0.9713 | 0.6575 |
| 0.1575 | 3.43 | 425 | 0.9963 | 0.6804 |
| 0.0822 | 3.63 | 450 | 0.9473 | 0.7123 |
| 0.1678 | 3.83 | 475 | 0.9788 | 0.7032 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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