tracer_b7 import TracerUniversalB7 from carvekit. fba_matting import FBAMatting from carvekit. interface import Interface from carvekit. The project supports python versions from 3.8 to 3.10.4 □ Interact via code: If you don't need deep configuration or don't want to deal with it Install CUDA Toolkit and Video Driver for your GPU.Make sure you have an NVIDIA GPU with 8 GB VRAM.The project supports python versions from 3.8 to 3.10.4 □ Setup for GPU processing: pip install carvekit -extra-index-url.This method gives the best result in combination with u2net without any preprocessing methods. fba (default) - This algorithm improves the borders of the image when removing the background from images with hair, etc.none - No post-processing methods used.□️ Image pre-processing and post-processing methods: □ Preprocessing methods: It is very important for final quality! Example images was taken by using U2-Net and FBA post-processing. Use U2-Net for hairs and Tracer-B7 for general images and correct parameters.The final quality may depend on the resolution of your image, the type of scene or object.Recommended parameters for different models Networks Image post-processing to improve the quality of the processed image.Using machine learning technology, the background of the image is removed.The photo is preprocessed to ensure the best quality of the output image.The user selects a picture or a folder with pictures for processing.⛱ Try yourself on Google Colab ⛓️ How does it work? 100% remove.bg compatible FastAPI HTTP API.FP16 inference: Fast inference with low memory usage.□ README LanguageĪutomated high-quality background removal framework for an image using neural networks. The higher resolution images from the picture above can be seen in the docs/imgs/compare/ and docs/imgs/input folders.
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