If you’re diving into the world of 3D object detection, you’re in for a treat with the Aggregate View Object Detection (AVOD) framework. This blog post guides you step-by-step on how to implement and use the AVOD Python network, making it as easy as pie! So, roll up your sleeves and let’s get started!

Getting Started

To start your AVOD journey, follow these steps:

• Clone the Repository:

  git clone git@github.com:kujasonavod.git --recurse-submodules

• Install Python Dependencies:

  cd avod
  pip3 install -r requirements.txt
  pip3 install tensorflow-gpu==1.3.0

• Add to PYTHONPATH:

  export PYTHONPATH=$PYTHONPATH:
  export PYTHONPATH=$PYTHONPATH:

• Compile Integral Image Library:

  cd wavedata
  sh scripts/install_build_integral_image_lib.sh

• Compile Protobufs:

  cd avod
  sh avod/protos/run_protoc.sh

Training the Model

Training the AVOD model requires a dataset, specifically the KITTI Object Detection Dataset. Here’s how to get it set up:

• Download the KITTI Data and place it in your home folder at ~Kitti/object.
• Prepare the Data Structure to mirror this layout:

Kitti
    └── object
        ├── testing
        └── training
            ├── calib
            ├── image_2
            ├── label_2
            ├── planes
            ├── velodyne
            ├── train.txt
            └── val.txt

Generating Mini-Batches

The training data needs to be pre-processed into mini-batches. This can be done by running the following:

cd avod
python scripts/preprocessing/gen_mini_batches.py

This configuration allows you to select which classes to process, focusing on Cars and People by default.

Running Training

To initiate the training process, use the command below:

python avod/experiments/run_training.py --pipeline_config=avod/configs/pyramid_cars_with_aug_example.config

Evaluating the Model

After training, it’s essential to evaluate the model:

python avod/experiments/run_evaluation.py --pipeline_config=avod/configs/pyramid_cars_with_aug_example.config

Analogy to Understand AVOD Model

Think of the AVOD model like a multi-faceted team effort in a sports competition. Each player (module) has a specific role but collaborates to achieve a common goal – detecting objects!

• The Training Phase is like practicing—each player hones their skill.
• The Evaluation Phase resembles a friendly match where strategies are tested and improved based on the outcome.
• Finally, the Inference is the championship game—utilizing everything learned to detect and identify objects in real scenarios!

Troubleshooting

While implementing, you might come across some bumps in the road. Here are some common issues and how to tackle them:

• Module Not Found Error: Ensure that the right directories are in your PYTHONPATH.
• Performance Issues: Make sure TensorFlow and all dependencies are correctly installed with the required versions.
• Data Processing Errors: Double-check the data structure and the presence of all required files in your KITTI dataset.

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Conclusion

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.