How to Implement SqueezeSegV2 for Road-Object Segmentation

Welcome to this step-by-step guide on utilizing SqueezeSegV2, a cutting-edge convolutional neural network model designed for LiDAR segmentation and unsupervised domain adaptation. If you want to get your hands dirty with LiDAR point clouds and identify road objects like pedestrians and vehicles, this guide will walk you through the setup, training, and troubleshooting processes.

Getting Started

First, we need to clone the SqueezeSegV2 repository and install the required packages. Follow these straightforward instructions:

• Clone the SqueezeSegV2 repository:

git clone https://github.com/xuanyuzhou98/SqueezeSegV2.git

• Set the root directory:

We will refer to the root directory as $SQSG_ROOT.

Setting Up the Virtual Environment

Now, let’s create a virtual environment for Python 2.7 (since that is a requirement). Here’s how:

• Create the virtual environment:

virtualenv env

• Activate the virtual environment:

source env/bin/activate

• Install the required Python packages:

pip install -r requirements.txt

Running the Demo

Once everything is installed correctly, you can run the demo script:

cd $SQSG_ROOT
python src/demo.py

If executed successfully, the script should write detection results and 2D label maps to $SQSG_ROOT/data/samples_out. You will observe green masks for cars and blue masks for cyclists, beautifully overlapping the projected LiDAR signal.

Downloading the Dataset

To train and validate your model, download the following datasets:

• Training and validation data (3.9 GB):

cd $SQSG_ROOT/data
wget https://www.dropbox.com/spnzgcitvppmwfu/flidar_2d.tgz
tar -xzvf lidar_2d.tgz
rm lidar_2d.tgz

• If you wish to acquire the largest synthetic LiDAR dataset for road scenes:

Fill out the request for the dataset through this link.

Training and Evaluation

To start training your model, execute the following command:

cd $SQSG_ROOT
./scripts/train.sh -gpu 0,1,2 -image_set train -log_dir ./log

Once the training starts, you can also run the evaluation script simultaneously:

cd $SQSG_ROOT
./scripts/eval.sh -gpu 1 -image_set val -log_dir ./log

Monitoring the Training Process

Use TensorBoard to monitor the training process where you can visualize metrics such as training loss and evaluation accuracy:

tensorboard --logdir=$SQSG_ROOT/log

Understanding the Code: An Analogy

Imagine SqueezeSegV2 as a well-orchestrated team of chefs in a kitchen. Each chef specializes in a different task within the cooking process. The main chef (the neural network) delegates tasks (features) such as chopping, frying, or seasoning (layers) to his sous chefs (neurons). They work together efficiently to prepare a delicious meal (final segmentation output). This collaboration ensures every element combines perfectly to identify objects from a chaotic LiDAR environment.

Troubleshooting Tips

If you encounter issues while setting up the SqueezeSegV2 model, here are some troubleshooting ideas:

• Ensure you are using the correct Python version (2.7) and have activated the virtual environment prior to running commands.
• Check your GPU compatibility and availability. Make sure TensorFlow has access to the GPUs specified.
• Verify the downloaded dataset files are located in the correct directories mentioned in the commands.

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Conclusion

Now that you have everything set up, you are ready to embark on a journey of object segmentation from LiDAR point clouds. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.