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---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-blur
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: blurry images
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
<!-- 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-blur
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 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
|