The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
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Tutorial on how to correctly install Segment Anything Model 2 in Windows and how to fix issues with CUDA, compilers, etc. #80
This is post is not about a code issue, but instead, it gives a tutorial on how to solve code issues that people raised in this group:
Hello friends, colleagues, and other people interested in this amazing software. I spent a day yesterday trying to install Segment Anything Model 2 (SAM2) on my Windows computer with NVIDIA 3090 GPU. I was experiencing challenges and compiler errors. Some of these challenges are also raised in this group. Finally, I succeded, and I decided to create a video tutorial on how to install SAM2 in Python on a Windows machine. Also, I explain how to run a program that is based on the official tutorial file. I made a number of modifications to the official tutorial file, and now the program will load images, perform object detection, and save the masked images (with masked objects) in separate files. Then, I explain how to use ffmpeg to create a video out of saved files, such that you can nicely visualize the results. The video tutorial is given here:
This is post is not about a code issue, but instead, it gives a tutorial on how to solve code issues that people raised in this group:
Hello friends, colleagues, and other people interested in this amazing software. I spent a day yesterday trying to install Segment Anything Model 2 (SAM2) on my Windows computer with NVIDIA 3090 GPU. I was experiencing challenges and compiler errors. Some of these challenges are also raised in this group. Finally, I succeded, and I decided to create a video tutorial on how to install SAM2 in Python on a Windows machine. Also, I explain how to run a program that is based on the official tutorial file. I made a number of modifications to the official tutorial file, and now the program will load images, perform object detection, and save the masked images (with masked objects) in separate files. Then, I explain how to use ffmpeg to create a video out of saved files, such that you can nicely visualize the results. The video tutorial is given here:
https://www.youtube.com/watch?v=MIUxiLjoA1g