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rembg is a pre-trained neural network that is designed to remove the background from an image. The model takes in an image as input and uses its understanding of various visual features to segment the image into foreground and background regions. It then creates a pixel-level mask that indicates which parts of the image belong to the foreground, allowing for easy removal of the background. The model is trained on a large dataset of images with ground truth foreground-background segmentation masks, allowing it to accurately remove backgrounds from a wide range of images.

Use cases

rembg has a range of potential use cases for technical users. One application is in the field of e-commerce, where businesses often need to remove the background of product images to create clean and professional-looking product shots. By using rembg, e-commerce platforms can automate the background removal process, saving time and effort for their sellers. Another possible use case is in graphic design and photo editing software, where users often want to isolate objects from their backgrounds for creative purposes. rembg could be integrated into these tools, allowing users to easily and accurately remove backgrounds from their images. Additionally, rembg could be utilized in the development of image recognition systems, where removing the background of an image can help improve the accuracy of object detection algorithms. Overall, rembg has the potential to simplify and enhance various processes that involve removing backgrounds from images.



Cost per run
Avg run time
Nvidia T4 GPU

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Summary of this model and related resources.

Model NameRembg
Removes Background From Image
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkNo paper link provided


How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?

Model Rank
Creator Rank


How much does it cost to run this model? How long, on average, does it take to complete a run?

Cost per Run$0.00165
Prediction HardwareNvidia T4 GPU
Average Completion Time3 seconds