Line Art Detection

--- license: mit library_name: pytorch tags: - computer-vision - image-segmentation - edge-detection - line-art - anime datasets: - custom metrics: - dice - iou pipeline_tag: image-segmentation --- # Anime Line Art Extraction Segmentation Model <img src="https://cdn-uploads.huggingface.co/production/uploads/6972a2622ef5ed3b50628995/6kCBB668giXjJoCLXAzfy.png" width="100%"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6972a2622ef5ed3b50628995/Q5pwvCCAfhl5ctqgsVEPa.png" width="100%"> ## Model Description ### Overview This model performs automatic line art extraction from anime images using a deep learning segmentation approach. The goal of the model is to identify edge structures that form the visual outlines of characters and objects in anime frames. Extracting clean line art typically requires manual tracing by artists or complex rule-based algorithms. This project explores whether a deep learning segmentation model can learn pixel-level edge structures directly from images. The model takes an RGB anime frame as input and produces a binary edge mask representing the predicted line art structure. ### Problem & Context Problem: Extracting clean line art from images normally requires manual tracing by hand or specialized algorithms. Why this matters: - Speed up animation production pipelines - Assist manga and illustration workflows - Help beginners learn drawing by tracing outlines - Improve visual quality by upscaling blurry line art - Generate datasets for generative AI models How computer vision helps: Deep learning segmentation models can learn pixel-level edge structures directly from images. ### Training Approach The model was trained as a semantic segmentation model using the PyTorch segmentation framework. Frameworks used: - PyTorch - segmentation_models_pytorch Since no pretrained model exists specifically for anime line extraction, the model was trained using a custom dataset and automatically generated edge masks. <img src="https://cdn-uploads.huggingface.co/production/uploads/6972a2622ef5ed3b50628995/h06ej-ODkw5tDAx3X6KfL.png" width="100%"> ### Intended Use Cases Potential applications include: - Animation pipelines – converting frames into base line structures - Digital art tools – assisting artists by generating sketch outlines - Image upscaling workflows – improving visual quality of blurry lines - Dataset gene...

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