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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-renovation2
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: 0.6529680365296804
---
<!-- 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 renovation dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6878
- Accuracy: 0.6530
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.273 | 0.2 | 25 | 1.2384 | 0.6027 |
| 0.5153 | 0.4 | 50 | 1.4060 | 0.5845 |
| 0.2792 | 0.6 | 75 | 1.3026 | 0.5936 |
| 0.5516 | 0.81 | 100 | 1.3999 | 0.6027 |
| 0.4247 | 1.01 | 125 | 1.2621 | 0.5982 |
| 0.1556 | 1.21 | 150 | 1.5661 | 0.5571 |
| 0.1458 | 1.41 | 175 | 1.3459 | 0.6347 |
| 0.1595 | 1.61 | 200 | 1.5278 | 0.5982 |
| 0.1195 | 1.81 | 225 | 1.5303 | 0.6256 |
| 0.1507 | 2.02 | 250 | 1.7701 | 0.5845 |
| 0.023 | 2.22 | 275 | 1.5354 | 0.6301 |
| 0.028 | 2.42 | 300 | 1.6535 | 0.6301 |
| 0.0698 | 2.62 | 325 | 1.6772 | 0.6438 |
| 0.0516 | 2.82 | 350 | 1.4380 | 0.6804 |
| 0.0136 | 3.02 | 375 | 1.6561 | 0.6484 |
| 0.0325 | 3.23 | 400 | 1.6028 | 0.6621 |
| 0.0149 | 3.43 | 425 | 1.6261 | 0.6621 |
| 0.0082 | 3.63 | 450 | 1.6615 | 0.6621 |
| 0.0093 | 3.83 | 475 | 1.6878 | 0.6530 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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