memolib 1.1.22

A comprehensive OCR and computer vision model library with support for SVTRV2, YOLO, EfficientNet, PPLCNet, and more.

MemoLib

A computer vision model library built on PyTorch, providing a unified interface for object detection, image segmentation, image classification, text recognition, and anomaly detection.

Model Status

Category	Model	Status
Detection	YOLOv5, YOLOv8, YOLO11, YOLO26	Stable
Detection	RF-DETR	⚠ Not fully tested
Segmentation	YOLO Segment (v8, 11, 26)	Stable
Segmentation	DINO UperNet	⚠ Not fully tested
Classification	PPLCNet (x0.25 – x1.0)	Stable
Classification	YOLO Classification (v8, 11, 26)	Stable
Classification	EfficientNet (B0–B7, V2S/M/L/XL)	⚠ Not fully tested
Recognition	SVTR2	🔧 Ongoing
Anomaly Detection	Dinomaly2	🔧 Ongoing
Anomaly Detection	INP Former	🔧 Ongoing

Unified Interface

All models share a consistent API:

model.LoadWeight(path)        # Load pretrained or custom weights
model.Train(callbacks)        # Start training
model.Predict(image)          # Single inference
model.BatchPredict(images)    # Batch inference
model.Export(path, format)    # Export to ONNX / OpenVINO / TensorRT
model.StopTraining()          # Stop training gracefully

Installation

pip install memolib

Optional dependencies

# For YOLO models
pip install memolib[yolo]

# For PPLCNet models
pip install memolib[pplcnet]

# For development
pip install memolib[dev]

Quick Start

Object Detection — YOLO

from MemoLib.Model.YOLO import Yolo
from MemoLib.Model.BaseModel.eDetectionModel import eYoloDetectionModel

model = Yolo()
model.cfg.Architecture = eYoloDetectionModel.Yolo11n
model.cfg.DatasetPath  = "path/to/dataset"
model.Train(callbacks=lambda level, msg: print(f"[{level}] {msg}"))

Classification — PPLCNet

from MemoLib.Model.PPLCNet import PPLCNet
from MemoLib.Model.BaseModel.eClassificationModel import ePPLCNetModel

model = PPLCNet()
model.cfg.Architecture = ePPLCNetModel.PPLCNetx50
model.cfg.DatasetPath  = "path/to/dataset"
model.Train()

Model Export

from MemoLib.Model.BaseModel.eModelBase import eModelExportType

model.Export("weights/best.pt", eModelExportType.ONNX)
model.Export("weights/best.pt", eModelExportType.OpenVINO)
model.Export("weights/best.pt", eModelExportType.TensorRT)

Project Structure

MemoLib/
└── Model/
    ├── BaseModel/       # Abstract interface (IModel), enums, export types
    ├── YOLO/            # YOLO detection, segmentation, classification
    ├── RFDETR/          # RF-DETR detection  [not fully tested]
    ├── DinoUperNet/     # DINO + UperNet semantic segmentation  [not fully tested]
    ├── Efficientnet/    # EfficientNet classification  [not fully tested]
    ├── PPLCNet/         # PPLCNet lightweight classification
    ├── SVTRV2/          # SVTR2 text recognition  [ongoing]
    └── Anomaly/         # Dinomaly2, INP Former  [ongoing]

Requirements

• Python 3.9+
• PyTorch 2.x (CUDA 12.x recommended)
• See requirements.txt for full dependency list

License

MIT License