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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: 0.6712328767123288
---
<!-- 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: 1.1227
- Accuracy: 0.6712
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3065 | 0.2 | 25 | 1.2953 | 0.4201 |
| 1.1693 | 0.4 | 50 | 1.2178 | 0.4749 |
| 1.1812 | 0.6 | 75 | 1.1296 | 0.4932 |
| 1.0392 | 0.81 | 100 | 1.0653 | 0.5936 |
| 0.9393 | 1.01 | 125 | 1.0614 | 0.5936 |
| 0.7521 | 1.21 | 150 | 1.1803 | 0.5342 |
| 0.6482 | 1.41 | 175 | 0.9854 | 0.6210 |
| 0.6643 | 1.61 | 200 | 1.0757 | 0.5616 |
| 0.7273 | 1.81 | 225 | 1.0664 | 0.5662 |
| 0.6387 | 2.02 | 250 | 0.9146 | 0.6575 |
| 0.3924 | 2.22 | 275 | 0.9536 | 0.6530 |
| 0.3131 | 2.42 | 300 | 1.0534 | 0.6347 |
| 0.299 | 2.62 | 325 | 1.0690 | 0.6256 |
| 0.296 | 2.82 | 350 | 1.1816 | 0.6027 |
| 0.1765 | 3.02 | 375 | 0.9577 | 0.6667 |
| 0.1152 | 3.23 | 400 | 1.0853 | 0.6712 |
| 0.112 | 3.43 | 425 | 1.0749 | 0.6849 |
| 0.1083 | 3.63 | 450 | 1.1111 | 0.6804 |
| 0.0969 | 3.83 | 475 | 1.1227 | 0.6712 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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