How to Utilize TensorFlow Advanced Segmentation Models

Welcome to the guide on using TensorFlow Advanced Segmentation Models, a Python library that provides high-level semantic segmentation models. Semantic segmentation plays a crucial role in computer vision, helping machines understand the content of images by categorizing each pixel. It’s worth exploring the features and setting up this library to enhance your AI models.

Preface

The field of computer vision has seen significant advancements in recent years, especially in semantic segmentation. This guide is designed to help you implement and utilize advanced models, facilitating the training process with this library. Let’s dive in!

Installation and Setup

To get started, ensure your system meets the following requirements:

• **Windows or Linux:**
  • Python 3.6 or higher
  • TensorFlow 2.3.0 (2.0.0 if EfficientNet backbone is not used)
  • NumPy
  • Matplotlib
• **MacOS:**
  • Python 3.9 or higher
  • TensorFlow-MacOS 2.5.0
  • NumPy 1.21.0
  • Matplotlib

To clone the repository, run the following command:

$ git clone https://github.com/JanMarcelKezmann/TensorFlow-Advanced-Segmentation-Models.git

Alternatively, you can directly install it with:

$ pip install git+https://github.com/JanMarcelKezmann/TensorFlow-Advanced-Segmentation-Models.git

Training Pipeline

Follow these steps to set up your training pipeline:

1. **Check TensorFlow Installation:** Ensure that TensorFlow is installed on your machine. 2. **Import the Library:** Use the standard Python import statement:

import tensorflow_advanced_segmentation_models as tasm

3. **Define Model Parameters:** – Select a model backbone, set weights, height, and width like so:

BACKBONE_NAME = 'efficientnetb3'
WEIGHTS = 'imagenet'
HEIGHT = 320
WIDTH = 320

4. **Load Your Data:** – Create training and validation generators with your dataset.

TrainingGenerator, ValidationGenerator = get_data(...)

5. **Create Base Model:** Generate the base model for the segmentation:

base_model, layers, layer_names = tasm.create_base_model(name=BACKBONE_NAME, weights=WEIGHTS, height=HEIGHT, width=WIDTH)

6. **Define and Compile Your Model:** – Set the model, loss function, and optimizer:

model = tasm.DANet(n_classes=3, base_model=base_model, output_layers=layers, backbone_trainable=False)
model.compile(tf.keras.optimizers.Adam(0.0001), loss=tasm.losses.CategoricalFocalLoss, metrics=[tasm.metrics.IOUScore(threshold=0.5)])

7. **Train Your Model:** – Finally, train your model with:

history = model.fit(TrainingGenerator, batch_size=8, epochs=50, validation_data=ValidationGenerator)

Understanding the Training Pipeline Analogy

Imagine you’re building a recipe in a kitchen. Each ingredient (like the model parameters) adds flavor and texture to the dish (your model). You meticulously gather your ingredients (data). Next, you follow a procedure—first preparing the base (base model), then adding the spices (compiling the model with loss functions), and finally putting everything together (training the model). Just like in cooking, the outcome will vary based on each step you meticulously follow!

Examples

To see the library in action, refer to some example Jupyter Notebooks:

• Multi-class segmentation (3 classes) on the CamVid dataset: Example 1
• Multi-class segmentation (11 classes) on the CamVid dataset: Example 2
• Two-class segmentation on Caltech-Birds 2010: Example 4

Models and Backbones

This library supports a variety of models and backbones, such as:

• FCN
• U-Net
• DeepLabV3+
• HRNet + OCR (NEW!)

These models can be defined as subclassed or functional models to allow easy adjustments to your architecture.

Troubleshooting

If you encounter issues during setup or training, consider the following troubleshooting steps:

• Ensure that you have met the Python and library requirements.
• Verify that your dataset paths are correct and accessible.
• Explore different model parameters for compatibility with your dataset.
• Refer to example notebooks for a better understanding of the setup.

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

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.