metadata
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: 0.6831683168316832
vit-base-renovation
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the renovations dataset. It achieves the following results on the evaluation set:
- Loss: 0.8944
- Accuracy: 0.6832
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 |
|---|---|---|---|---|
| 0.8483 | 1.75 | 100 | 0.9965 | 0.5446 |
| 0.3474 | 3.51 | 200 | 0.8944 | 0.6832 |
| 0.0328 | 5.26 | 300 | 1.1583 | 0.6634 |
| 0.0176 | 7.02 | 400 | 1.0845 | 0.6832 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3