FastSAM: Segment Anything in Real-Time

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In this video 📝 We’ll be taking a look at the new FastSAM model. This is a way faster model compared to the original SAM model from Meta AI. We can actually get up to 50x faster inference time. We can now do segmentation of anything in images in real-time. In this video, we are going to see how we can set it up in google colab and also in a custom python script locally.
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Timestamps:
0:00 Introduction
0:37 FastSAM GitHub Repo
5:08 Colab Setup
8:38 Local Installation
12:30 FastSAM VSCode
15:00 FastSAM Results
Tags:
#fastsam #sam #segmentanything #ai #computervision #yolov8 #objectsegmentation

[@NicolaiAI]

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[@hyunseungshin3955]

I have a question when I try this project it's work well thank you but I don't know how to get class name from annotations could you tell me this?

[@ZeynepC3]

Hello, first of all, thank you for the video. I'm wondering about something, is it possible to train on our own dataset with fast-sam?

[@autoboto]

Thanks for the instructional video and all worked as explained. The image output is nice but now I would like to process the segments highlighted and do additional processing of these segments based on other details and positional relationships relative to other segments.
def everything_prompt(self):
if self.results == None:
return []
return self.results[0].masks.data
With ann is the everything_prompt and the below tensor array. Should be 4 but has 6
4 segments are highlighted in output but 6 tensors show in the array.
The segment array data does not seem to correlate to the original image pixel coordinates from the data I see, but obviously the plot handled the conversion in the output image.
tensor([[7.8642e+02, 7.7507e+01, 9.9292e+02, 4.7635e+02, 9.1396e-01, 0.0000e+00],
[4.0099e+02, 7.8259e+01, 6.0955e+02, 4.7675e+02, 9.0963e-01, 0.0000e+00],
[0.0000e+00, 7.8047e+01, 2.2261e+02, 4.7691e+02, 8.4697e-01, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 1.0800e+03, 5.4800e+02, 7.3205e-01, 0.0000e+00],
[2.9916e+01, 8.8821e+01, 2.0837e+02, 4.6286e+02, 6.0771e-01, 0.0000e+00],
[7.9572e+02, 8.4327e+01, 9.8209e+02, 4.7095e+02, 4.5648e-01, 0.0000e+00]], device='cuda:0')

[@hello81642]

I wish SAM also classified detected masks (provided labels)

[@andresmora9157]

hi,I have a question, how can I obtain all the points that generate the segmented image

[@NicolaiAI]

It will output the masks which is basically just every single pixel in that class. Or did I not understand your question correctly?

[@gengar2904]

Is it not possible for us to get real time camera view instead of picture?

[@NicolaiAI]

Yeah ill do that in the next video

[@gengar2904]

@@NicolaiAI I am waiting...

[@nicks-fix]

@@NicolaiAI excited to see that!

[@mikearney01]

Me too! Maybe both with points and with masks, please

[@NicolaiAI]

Already up and running live! Will record a video over the weekend and upload start of next week

[@rayharli9157]

Hi @nicolaiAI love your videos, is there anyway you can share the py code for this video?

[@NicolaiAI]

Awesome man, thanks! The code is on my GitHub under fastSam live

[@rayharli9157]

@@NicolaiAI fantastic, thank you!