Comments (4)
Hi there;
The steps for recreating the skin segmentation code is in the readme. First start by creating your enviroment, then follow the section on running the pretrained projects.
This code was created as a machine-learning version of the original SkinDetector project. That project was created to show how multi-colour space thresholding is used in traditional image processing to create segmentation masks.
from semanticsegmentation.
Thank you very much for your quick response. I have run your code for an image with the FCNResNet101 model. It works great!
But what I do not fully understand is the BiSeNetV2, for the real-time models. What does it mean being real time? Does it also process an image, or a video instead? What is the difference between both models?
Thank you, Lucia
from semanticsegmentation.
On a HD image; BiSeNet can run at 60fps while ResNet101 can barely handle 1fps on a 1070. This code is only written for single images, but you can run BiSeNet in "real-time"
from semanticsegmentation.
Perfect! Thank you!
from semanticsegmentation.
Related Issues (20)
- Assertion Error when replicating this code with Google Colab HOT 2
- pickle.UnpicklingError: invalid load key, 'v'. HOT 2
- Semantic Segmentation Tool HOT 1
- Error in training HOT 3
- CPU HOT 4
- CUDA error: no kernel image HOT 3
- Cannot launch "labelme" HOT 1
- Input Shape HOT 2
- coremltools conversion from pytorch to coreml HOT 1
- coreml conversion error HOT 1
- segmented images HOT 1
- Training error HOT 4
- Skin segmentation dataset HOT 1
- How did I get SS to show black and white skin mask HOT 1
- Tranfer learning HOT 1
- Understanding the terminal output during training HOT 4
- Multiple Classes
- Running out of memory HOT 2
- Unable to find fcn_resnet101_coco, and BiSeNetV2 HOT 1
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from semanticsegmentation.