Bridging the gap between Research Papers and Production-Ready Code.
OpenMLLab is born from the belief that to truly master Artificial Intelligence, one must move beyond import sklearn. This library is a documentation of a journey from raw mathematical equations found in research papers to optimized, vectorized Python implementations.
- Zero Abstraction: Understand exactly how gradients flow through tensors.
- Algorithmic Grit: Solve numerical instability (Overflow/Underflow) without safety nets.
- Scalable Systems: Learn the architectural patterns used by industry legends.
We follow a modular, strictly-typed structure to ensure scalability and ease of contribution.
openmllab/linear_model/: Optimized regression and classification (Logistics, Ridge, Lasso).openmllab/metrics/: Evaluation suite implementing standard industry benchmarks.openmllab/preprocessing/: Data normalization and transformation pipelines.tests/: Unit tests for mathematical correctness and shape-safety.
Designed to be intuitive for those familiar with the standard ML ecosystem.
import numpy as np
from openmllab.metrics import accuracy_score
# Sample Data
y_true = np.array([0, 1, 1, 0])
y_pred = np.array([0, 1, 0, 0])
# Evaluation
score = accuracy_score(y_true, y_pred)
print(f"Model Accuracy: {score * 100}%")