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 | Stable |
| Segmentation | YOLO Segment (v8, 11, 26) | Stable |
| Segmentation | DINO UperNet | Stable |
| Classification | PPLCNet (x0.25 – x1.0) | Stable |
| Classification | YOLO Classification (v8, 11, 26) | Stable |
| Classification | EfficientNet (B0–B7, V2S/M/L/XL) | Stable |
| Recognition | SVTR2 | Stable |
| Anomaly Detection | Dinomaly2 | ⚠ Not fully tested |
| 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
├── DinoUperNet/ # DINO + UperNet semantic segmentation
├── Efficientnet/ # EfficientNet classification
├── PPLCNet/ # PPLCNet lightweight classification
├── SVTRV2/ # SVTR2 text recognition
└── Anomaly/ # Dinomaly2, INP Former
Dataset Format
Classification — YOLO / EfficientNet / PPLCNet
ImageFolder structure (same as torchvision ImageFolder):
dataset/
├── train/
│ ├── cat/
│ │ ├── img001.jpg
│ │ └── img002.jpg
│ └── dog/
│ ├── img003.jpg
│ └── img004.jpg
└── val/
├── cat/
└── dog/
Detection — YOLO (detect / segment)
YOLO label format. data.yaml is auto-generated if missing.
dataset/
├── train/
│ ├── images/
│ │ ├── img001.jpg
│ └── labels/
│ ├── img001.txt # <class> <x_c> <y_c> <w> <h> (normalized 0-1)
├── val/
│ ├── images/
│ └── labels/
└── data.yaml
data.yaml:
path: /path/to/dataset
nc: 2
names: [cat, dog]
train: train/images
val: val/images
For segmentation, the label format uses polygon points:
# <class> <x1> <y1> <x2> <y2> ... <xn> <yn> (normalized 0-1)
0 0.1 0.2 0.3 0.4 0.5 0.6
Detection — RF-DETR
Supports two formats. Auto-detected from directory structure.
COCO format (recommended — exported directly from Roboflow/CVAT):
dataset/
├── train/
│ ├── img001.jpg
│ └── _annotations.coco.json
└── val/
├── img002.jpg
└── _annotations.coco.json
YOLO format (same structure as YOLO detection above):
dataset/
├── train/
│ ├── images/
│ └── labels/
├── val/
│ ├── images/
│ └── labels/
└── data.yaml
Semantic Segmentation — DinoUperNet
Mask images must be single-channel PNG where each pixel value = class index.
dataset/
├── train/
│ ├── images/
│ │ ├── img001.jpg
│ └── masks/
│ ├── img001.png # pixel value = class index (0, 1, 2, ...)
├── val/
│ ├── images/
│ └── masks/
ReduceZeroLabel: if
True, pixel value0is treated as background/unlabeled (ignored in loss), and class indices shift by -1. Use this for datasets like ADE20K where class 0 = unlabeled.
Requirements
- Python 3.9+
- PyTorch 2.x (CUDA 12.x recommended)
- See
requirements.txtfor full dependency list
License
MIT License
1 maintainer
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