MemoLib 0.1.7.2

A Python library for machine learning model training and inference

MemoLib

A comprehensive Python library for machine learning model training and inference, supporting both classification and object detection tasks with state-of-the-art models.

Features

• Multiple Model Architectures

  • EfficientNet (B0-B7, EfficientNetV2)
  • YOLO series for object detection

• Flexible Task Support

  • Image Classification
  • Object Detection

• Export Capabilities

  • ONNX format for cross-platform deployment
  • OpenVINO format for Intel hardware optimization

• Training Pipeline

  • Built-in training loops with customizable callbacks
  • Support for custom loss functions (FocalLoss, etc.)
  • Easy model configuration via YAML/JSON

• Utilities

  • Dataset format conversion tools
  • Data serialization and configuration management
  • Image preprocessing and augmentation

Installation

Install MemoLib using pip:

pip install MemoLib

For development with optional dependencies:

# With OpenVINO support
pip install MemoLib[openvino]

# All optional dependencies
pip install MemoLib[all]

# Development tools
pip install MemoLib[dev]

Quick Start

Classification Example

from MemoLib import MemoModel, eModelTask, eClassifyModel

# Create a classification model (EfficientNet-B0)
model = MemoModel(eModelTask.Classification, eClassifyModel.EfficientNetB0)

# Load pretrained weights and class labels
model.LoadWeight("path/to/weights.pth")
model.LoadLabelName("path/to/labels.txt")

# Make predictions on an image
result = model.Predict("path/to/image.jpg")
print(f"Prediction: {result.label}")
print(f"Confidence: {result.confidence:.2f}")

Object Detection Example

from MemoLib import MemoModel, eModelTask, eDetectModel

# Create a YOLO detection model
model = MemoModel(eModelTask.ObjectDetection, eDetectModel.YOLOv8)

# Load weights
model.LoadWeight("yolov8n.pt")

# Detect objects in image
results = model.Predict("path/to/image.jpg")
for detection in results:
    print(f"Class: {detection.label}, Confidence: {detection.confidence:.2f}")
    print(f"BBox: {detection.bbox}")

Model Export

# Export to ONNX
model.ExportToONNX("model.onnx")

# Export to OpenVINO (requires openvino package)
model.ExportToOpenVINO("model.xml")

Package Structure

MemoLib/
├── Model/              # Core model implementations
│   ├── Data/          # Data loading and preprocessing
│   ├── EfficientNet/  # EfficientNet model variants
│   └── YOLO/          # YOLO detection models
├── DataSerializer/     # Configuration management
├── DatasetFormatConvert/  # Dataset conversion utilities
└── Loss/              # Custom loss functions (FocalLoss, etc.)

Supported Models

Classification

• EfficientNet-B0 to B7
• EfficientNetV2-S, M, L

Object Detection

• YOLOv5
• YOLOv8
• YOLOv9
• YOLO11

Requirements

• Python >= 3.8
• PyTorch >= 1.12.0
• TorchVision >= 0.13.0
• OpenCV >= 4.5.0
• Ultralytics >= 8.0.0
• EfficientNet-PyTorch >= 0.7.0

See pyproject.toml for full dependency list.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Issues

If you encounter any problems or have suggestions, please open an issue.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

NghiaKTHP

• GitHub: @NghiaKTHP
• PyPI: MemoLib

Changelog

Version 0.1.5

• Added GitHub repository links
• Improved documentation
• Updated package metadata

Version 0.1.4

• Initial PyPI release
• Support for EfficientNet and YOLO models
• ONNX and OpenVINO export capabilities