metadata

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

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