fregu856/ebms_regression
Official implementation of "Energy-Based Models for Deep Probabilistic Regression" (ECCV 2020) and "How to Train Your Energy-Based Model for Regression" (BMVC 2020).
This project offers an advanced method for deep probabilistic regression, helping computer vision engineers get more precise and robust predictions from their models. It takes in raw image data and outputs predicted bounding box coordinates for object detection, or other continuous values like age or head-pose, along with a measure of uncertainty. This allows practitioners to understand not just 'what' but 'how sure' the model is, which is crucial for high-stakes applications.
106 stars. No commits in the last 6 months.
Use this if you need to perform precise object detection or other continuous value predictions from images and require a clear understanding of the model's confidence in its predictions.
Not ideal if you are looking for a simple, off-the-shelf image classification tool or a solution for non-image-based regression tasks.
Stars
106
Forks
19
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Aug 14, 2020
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/fregu856/ebms_regression"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
lululxvi/deepxde
A library for scientific machine learning and physics-informed learning
pnnl/neuromancer
Pytorch-based framework for solving parametric constrained optimization problems,...
wilsonrljr/sysidentpy
A Python Package For System Identification Using NARMAX Models
dynamicslab/pysindy
A package for the sparse identification of nonlinear dynamical systems from data
google-research/torchsde
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.