| | --- |
| | license: apache-2.0 |
| | tags: |
| | - image-classification |
| | - generated_from_trainer |
| | datasets: |
| | - imagefolder |
| | metrics: |
| | - accuracy |
| | base_model: google/vit-base-patch16-224-in21k |
| | model-index: |
| | - name: vit-base-blur |
| | results: |
| | - task: |
| | type: image-classification |
| | name: Image Classification |
| | dataset: |
| | name: blurry images |
| | type: imagefolder |
| | config: default |
| | split: train |
| | args: default |
| | metrics: |
| | - type: accuracy |
| | value: 1.0 |
| | name: Accuracy |
| | --- |
| | |
| | <!-- 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 |
| |
|
