Principle:Obss Sahi Model Initialization
| Knowledge Sources | |
|---|---|
| Domains | Object_Detection, Computer_Vision, Model_Management |
| Last Updated | 2026-02-08 12:00 GMT |
Overview
A factory-based model initialization pattern that instantiates framework-specific object detection models through a unified interface, decoupling the application from any single detection framework.
Description
Model initialization in the context of sliced inference is the process of loading a pre-trained object detection model into memory and preparing it for inference. The challenge is that modern object detection spans many frameworks (Ultralytics YOLO, Detectron2, MMDetection, HuggingFace Transformers, TorchVision, etc.), each with different APIs, weight formats, and configuration requirements.
The Factory Pattern solves this by providing a single entry point that accepts a framework name string and dispatches to the correct framework-specific model wrapper. This abstraction allows the rest of the inference pipeline to operate on a uniform DetectionModel interface regardless of which detection backend is used.
The abstract DetectionModel base class defines the contract: load_model() to initialize weights, perform_inference() to run forward pass, and convert_original_predictions() to normalize outputs into a common ObjectPrediction format.
Usage
Use this principle when you need to initialize any supported detection model for inference, whether for sliced prediction, full-image prediction, or batch processing. This is always the first step in any SAHI inference pipeline. Choose this approach when you want framework-agnostic code that can switch between detection backends without changing downstream logic.
Theoretical Basis
The design follows two classical software engineering patterns:
Factory Method Pattern:
A static factory method maps a string identifier to a concrete class, hiding instantiation complexity from the caller.
# Abstract algorithm (NOT real implementation)
def create_model(model_type: str) -> DetectionModel:
class_map = {"ultralytics": UltralyticsModel, "huggingface": HFModel, ...}
ModelClass = class_map[model_type]
return ModelClass(model_path, device, ...)
Strategy Pattern:
Each framework wrapper implements the same abstract interface (DetectionModel), so the inference pipeline can treat all models identically:
# Abstract interface contract
class DetectionModel:
def load_model(self): ...
def perform_inference(self, image: np.ndarray): ...
def convert_original_predictions(self, shift_amount, full_shape): ...
def object_prediction_list -> List[ObjectPrediction]: ...
This dual-pattern approach enables open/closed extension: new frameworks are supported by adding a new wrapper class without modifying existing code.