Instructions to use LoliRimuru/AAT-JPEG-Artefact-Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use LoliRimuru/AAT-JPEG-Artefact-Detection with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy), and from_pretrained_keras was removed in huggingface_hub 1.0. # See https://github.com/keras-team/tf-keras for more details. # !pip install "huggingface_hub<1.0" tf_keras from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("LoliRimuru/AAT-JPEG-Artefact-Detection") - Notebooks
- Google Colab
- Kaggle
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# AAT JPEG Artefact Detection
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Autodetects JPEG artefacts in categories of C100 (no artefacts), C95, C90, C85, C80, C70, C60, C40 and C20 (really terrible artefacts). The input is a 1024x1024 large image. The image is converted to gray scale to omit redundant informations and reduce overall training and inference time. Model is standalone.
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# AAT JPEG Artefact Detection (OUTDATED use https://huggingface.co/LoliRimuru/AAL-Plus_Image_Quality_Assessment)
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Autodetects JPEG artefacts in categories of C100 (no artefacts), C95, C90, C85, C80, C70, C60, C40 and C20 (really terrible artefacts). The input is a 1024x1024 large image. The image is converted to gray scale to omit redundant informations and reduce overall training and inference time. Model is standalone.
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