models/fast-sam/

[giscus[bot]]

models/fast-sam/

Discover FastSAM, a real-time CNN-based solution for segmenting any object in an image. Efficient, competitive, and ideal for various vision tasks.

https://docs.ultralytics.com/models/fast-sam/

[YuechengLiu]

Thanks the authors for the great work!

I'm using FastSAM-x for instance segmentation (bboxes as the prompt), but I find that the shape of the predicted masks mismatch with the bounding box number. (e.g. the model takes 18 bboxes as input, but the shape of the predicted mask is (17, H, W)) . I tried to set conf=0.01 and iou=0.999, but it doesn't work. How can I solve that? Thanks!

[glenn-jocher]

@YuechengLiu thank you for your kind words! The issue you're experiencing might be due to a mismatch in the bounding box prompts and the segmentation results. Please ensure you're using the latest version of the FastSAM model. If the problem persists, consider checking the input format and the bounding box coordinates. For further assistance, you can refer to the FastSAM documentation: https://docs.ultralytics.com/models/fast-sam/.

[YuechengLiu]

Thanks for your kind reply! The bounding boxes I'm using are predicted by yolov10x (result.boxes.xyxy), so I think the input format might be right. I also tried the same bounding boxes for mobile_sam, and the output dimension is as expected (same as the input dimension). The problem does not always occur. Do you have any idea about that? Thanks!

[YuechengLiu]

And I'm using the latest version of ultralytics

[glenn-jocher]

Thanks for confirming you're using the latest version. Given that the issue is intermittent, it might be related to the specific bounding boxes or the image content. Please try running the model with a different set of bounding boxes or images to see if the issue persists. If it does, consider opening an issue on the GitHub repository with detailed information for further investigation.

[feiwenxiong]

how many boxes are supported in thsi api, it cound only segment 2 boxes at most.

[glenn-jocher]

@feiwenxiong fastSAM should support multiple boxes. If you're experiencing limitations, please ensure your input format is correct. For further assistance, you might want to check the latest updates or documentation.

[chongkuiqi]

Thanks for your great work！
I'm using FastSAM-x with bboxes prompt, the original image shape is [720, 1280], and I set 'imgsz' 1024.
However, the results mask shape is [576, 1024], but I remembered in ultralytics-8.2.10, I can get masks with the same shape as the original image. May I ask why this change happened ?

[chongkuiqi]

Sorry, I forgot to set 'Retina_masks', problem solved, thanks again.

[glenn-jocher]

Glad to hear the issue is resolved! If you have any more questions, feel free to ask.

[ismailwale]

How can I make an inference of a video using frame by frame in loop using FastSam ?because I tried multiple times making inference directly with video predictor I constantly running out of memory despite setting stream =True since SAM is trying to keep the memory of 2000 frames of the video at ones . I will appreciate any ideal on how to predict and segment the video as a single frame image at every instance and put everything in a loop.

Similar question win MobileSam

[glenn-jocher]

@ismailwale to process a video frame by frame and avoid memory overload with FastSAM or MobileSAM, load the video as a frame sequence using a tool like OpenCV. Loop through each frame, run inference on it individually, and clear any cached results to free up memory between frames. For reference on frame-wise usage, consult OpenCV and FastSAM integration. Let me know if you need further clarifications!

[ismailwale]

Thanks so much for your guide .I appreciate it sir.

[glenn-jocher]

Thank you for your appreciation! I'm glad the guide was useful. Please feel free to reach out if you have further questions about utilizing FastSAM or MobileSAM effectively for your tasks.