Welcome to the exciting world of machine learning, where we can distinguish different types of cloth using binary segmentation! This guide will help you set up the binary segmentation model, train it, and run inference. Let’s dive into this step-by-step.

Installation

To begin, you’ll need to install the necessary package. Open your terminal and run the following command:

pip install -U cloths_segmentation

Example Inference

If you’re looking for a hands-on example, you can access a Jupyter notebook through the link below:

Open In Colab

Web Application

Ready to see it in action? You can use the hosted web application by following this link:

Cloths Segmentation WebApp

Data Preparation

In order to train your model effectively, you need to prepare your data correctly. Follow these steps:

1. Download the dataset from this Kaggle link.
2. Process the data using the provided script. This script will organize your images and generate binary masks.

Training the Model

Once your data is prepared, it’s time to move on to model training. Here’s how you can accomplish that:

Define the Configuration

You’ll need to set up a configuration file for the training process. You can find an example configuration at this link. Configurations allow you to enable or disable datasets for training and validation.

Set the Environmental Variables

Next, you need to define where your images and masks are stored using the following environmental variable commands:

export IMAGE_PATH=path to the folder with images
export MASK_PATH=path to the folder with masks

Start Training

Finally, initiate the training process by running this command:

python -m cloths_segmentation.train -c path to config

Performing Inference

Now that your model is trained, you can use it for inference. Run the following command to segment cloths:

bash python -m torch.distributed.launch --nproc_per_node=num_gpu cloths_segmentation/inference.py -i path to images -c path to config -w path to weights -o output-path --fp16

Troubleshooting

If you encounter issues throughout this process, consider the following troubleshooting tips:

• Ensure that all paths specified in the commands are correct and point to the intended folders.
• Check for any available updates to the package or your Python environment that might resolve compatibility issues.
• Review the configuration file for any missing or incorrect parameters that might affect training or inference.

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

Conclusion

Congratulations! You now have the foundational knowledge to perform binary segmentation of various cloths. 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.