sacmehta/ESPNetv2
A light-weight, power efficient, and general purpose convolutional neural network
This project offers a lightweight and power-efficient convolutional neural network, ESPNetv2, designed for image analysis tasks. It takes raw image data as input and can output classifications (like identifying objects in an image) or detailed semantic segmentations (like outlining different elements within an image, such as roads, cars, or pedestrians). This is ideal for developers and researchers building applications that require real-time image processing on devices with limited power, such as mobile phones or embedded systems.
454 stars. No commits in the last 6 months.
Use this if you are a developer or researcher building computer vision applications and need a highly efficient neural network model for image classification or semantic segmentation that consumes minimal power and runs effectively on edge devices.
Not ideal if you are looking for an out-of-the-box application for end-users, or if you require a solution that is actively maintained and regularly updated in this specific repository.
Stars
454
Forks
73
Language
Python
License
MIT
Category
Last pushed
Jan 29, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/sacmehta/ESPNetv2"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
MLSTRUCT/MLStructFP
Multi-unit floor plan dataset for architectural analysis and recognition
yassouali/pytorch-segmentation
:art: Semantic segmentation models, datasets and losses implemented in PyTorch.
wkentaro/pytorch-fcn
PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original...
meetps/pytorch-semseg
Semantic Segmentation Architectures Implemented in PyTorch
fregu856/deeplabv3
PyTorch implementation of DeepLabV3, trained on the Cityscapes dataset.