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--- |
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license: apache-2.0 |
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tags: |
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- image-classification |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-base-blur |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: blurry images |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 1.0 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# vit-base-blur |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0008 |
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- Accuracy: 1.0 |
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## Model description |
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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 |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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1000ish clean and blurry images using 30 and 10 steps respectively on SD2.1 |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 12 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.0082 | 1.02 | 100 | 0.0107 | 1.0 | |
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| 0.0079 | 2.04 | 200 | 0.0052 | 1.0 | |
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| 0.0029 | 3.06 | 300 | 0.0028 | 1.0 | |
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| 0.002 | 4.08 | 400 | 0.0020 | 1.0 | |
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| 0.0016 | 5.1 | 500 | 0.0015 | 1.0 | |
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| 0.0013 | 6.12 | 600 | 0.0013 | 1.0 | |
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| 0.0011 | 7.14 | 700 | 0.0011 | 1.0 | |
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| 0.001 | 8.16 | 800 | 0.0010 | 1.0 | |
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| 0.0009 | 9.18 | 900 | 0.0009 | 1.0 | |
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| 0.0008 | 10.2 | 1000 | 0.0008 | 1.0 | |
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| 0.0008 | 11.22 | 1100 | 0.0008 | 1.0 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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