Welcome to the world of AI and deep learning! In this post, we will explore how to implement PLOP (Learning without Forgetting) for continual semantic segmentation. This approach allows models to learn continuously without forgetting the previously learned information, which is crucial in various applications where data comes in streams over time.

Getting Started with PLOP

Let’s begin by setting up the environment for the PLOP project. This is akin to preparing the kitchen before cooking a delicious meal. Here’s what you need:

• Python (3.6)
• Pytorch (1.8.1+cu102)
• torchvision (0.9.1+cu102)
• tensorboardX (1.8)
• apex (0.1)
• matplotlib (3.3.1)
• numpy (1.17.2)
• inplace-abn (1.0.7)

Note: Ensure you have the right version of CUDA for compatibility.

Preparing Your Dataset

Before we jump into training, let’s get our dataset ready. PLOP can utilize datasets like ADE20k and Pascal-VOC 2012. Scripts are available in the data folder to download ADE20k and Pascal-VOC, but for Cityscapes, you need to request access. A little red tape, but it’s just a formality!

Training Your Model

Now that your environment is set and data is prepped, it’s time to run the training. Think of this like conducting an orchestra where each musician (code) plays its part to create a harmonious output (trained model).

The core of the training process lies in the run.py file. To kick off the training, use the following command:

python -m torch.distributed.launch --nproc_per_node=num_GPUs run.py --data_root data_folder --name exp_name .. other args ..

Here you can customize several parameters:

• Specify your data folder using: --data_root data_root
• Choose your dataset: --dataset voc (Pascal-VOC) or ade (ADE20K)
• Select your task and set learning rates as required.
• Select a method such as --method FT, LWF, etc.

Example Commands

Here are a few example commands to run specific tasks in PLOP:

# Example for LwF on ADE20K
python -m torch.distributed.launch --nproc_per_node=2 run.py --data_root data --batch_size 12 --dataset ade --name LWF --task 100-50 --step 0 --lr 0.01 --epochs 60 --method LWF

# Example for MIB on ADE20K
python -m torch.distributed.launch --nproc_per_node=2 run.py --data_root data --batch_size 12 --dataset ade --name MIB --task 100-50 --step 2 --lr 0.001 --epochs 60 --method MIB

Troubleshooting

If you encounter any issues during installation or training, here are some quick troubleshooting tips:

• Ensure that all required libraries are correctly installed.
• Check that your CUDA version is compatible with PyTorch.
• If errors arise regarding pretrained weights, ensure you’re using the correct model path and removing any incompatible prefixes.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

The PLOP approach allows continuous learning in semantic segmentation tasks by minimizing forgetting, a crucial capability in dynamic environments. With the setup explained and commands presented, you can embark on your PLOP journey.

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.