SEMANTIC SEGMENTATION

Live workings of the semantic segmentation model.

https://github.com/ANARCHY-ME-205/semseg/assets/129314735/22f779cf-a043-4b29-839f-3818c6688f16

https://github.com/ANARCHY-ME-205/semseg/assets/129314735/8bcab38b-7e29-457a-bbd4-2185c43b5570

Prerequisites

THANK, WORSHIP, PRAY to Tamoghna!!!

This requires Python 3.7+, CUDA 10.2+ and PyTorch 1.8+.

Installing pytorch

shell pip install torch; pip install torchvision

Installing dependencies :

shell pip install testresources ; pip install launchpadlib ; pip install --upgrade pip setuptools ; pip install --upgrade six

Installation

Step 1. Installing mmengine and mmcv using openmim

shell pip install -U openmim ; mim install mmengine ; mim install "mmcv>=2.0.0" Step 2. Install MMSegmentation.

```shell cd semseg ; pip install -v -e .

'-v' means verbose, or more output

'-e' means installing a project in editable mode,

thus any local modifications made to the code will take effect without reinstallation.

```

Verify the installation

To verify whether MMSegmentation is installed correctly, we provide some sample codes to run an inference demo.

Step 0. Checking mmseg version.

```python import mmseg print(mmseg.version)

Example output: 1.0.0

```

The following steps to verify installation are optional

Step 1. We need to download config and checkpoint files.

shell mim download mmsegmentation --config pspnet_r50-d8_4xb2-40k_cityscapes-512x1024 --dest .

The downloading will take several seconds or more, depending on your network environment. When it is done, you will find two files pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py and pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth in your current folder.

Step 2. Verify the inference demo.

Option (a). If you install mmsegmentation from source, just run the following command.

shell python demo/image_demo.py demo/demo.png configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth --device cuda:0 --out-file result.jpg

You will see a new image result.jpg on your current folder, where segmentation masks are covered on all objects.

Running semantic segmentation.

Run semseg.py

Plausible semseg models :

Option(A). configs/bisenetv1/bisenetv1r18-d32-in1k-pre4xb4-160kcityscapes-1024x1024.py && bisenetv1r18-d32in1k-pre4x41024x1024160kcityscapes20210905_220251-8ba80eff.pth :
gives a decent computational speed of semseg although the accuracy is compromised a bit (70%).

Option(B). configs/pspnet/pspnetr50b-d84xb2-80kcityscapes-512x1024.py && pspnetr50b-d8512x102480kcityscapes20201225_094315-6344287a.pth :
this one is found to have the highest amount accuracy(81%) so far but very very slow computational speed something around 3 fps which is very bad.

Option(C). configs/ddrnet/ddrnet23-slimin1k-pre2xb6-120kcityscapes-1024x1024.py && ddrnet23-slimin1k-pre2xb6-120kcityscapes-1024x102420230426145312-6a5e5174.pth :
model link : https://download.openmmlab.com/mmsegmentation/v0.5/ddrnet/ddrnet23-slimin1k-pre2xb6-120kcityscapes-1024x1024/ddrnet23-slimin1k-pre2xb6-120kcityscapes-1024x102420230426145312-6a5e5174.pth works with moderate accuracy and moderate speed. I will be using this for now. DISCLAIMER : Need the bolt zed cam param tuning for this to work well!!!