vit-base-blur
vit-base-blur
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the blurry images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0008
- Accuracy: 1.0
Model description
Model trained for binary classification between ‘noisy’ (blurry) and clean images, where ‘noisy’ images are the result of unfinished/insufficient passes from an LDM for image generation
Intended uses & limitations
More information needed
Training and evaluation data
1000ish clean and blurry images using 30 and 10 steps respectively on SD2.1
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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: 12
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0082 | 1.02 | 100 | 0.0107 | 1.0 |
| 0.0079 | 2.04 | 200 | 0.0052 | 1.0 |
| 0.0029 | 3.06 | 300 | 0.0028 | 1.0 |
| 0.002 | 4.08 | 400 | 0.0020 | 1.0 |
| 0.0016 | 5.1 | 500 | 0.0015 | 1.0 |
| 0.0013 | 6.12 | 600 | 0.0013 | 1.0 |
| 0.0011 | 7.14 | 700 | 0.0011 | 1.0 |
| 0.001 | 8.16 | 800 | 0.0010 | 1.0 |
| 0.0009 | 9.18 | 900 | 0.0009 | 1.0 |
| 0.0008 | 10.2 | 1000 | 0.0008 | 1.0 |
| 0.0008 | 11.22 | 1100 | 0.0008 | 1.0 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3