Unofficial implementation of Fast-SCNN: Fast Semantic Segmentation Network

Last update: Aug 11, 2022

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Overview

Fast-SCNN: Fast Semantic Segmentation Network

Unofficial implementation of the model architecture of Fast-SCNN. Real-time Semantic Segmentation and mobile friendly memory consumption.

Tested with Python 3.6 and Pytorch '1.0.1.post2'

Example

from fast_scnn import Fast_SCNN
model = Fast_SCNN(input_channel=3, num_classes=10)

Results with Fast-SCNN

Inference Speed (fps) on Cityscapes

mIoU on Cityscapes

Owner

Philip Popien

Deep Learning Engineer focused on Computer Vision applications. Effective Altruist.

GitHub Repository

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Unofficial implementation of Fast-SCNN: Fast Semantic Segmentation Network

Related tags

Overview

Fast-SCNN: Fast Semantic Segmentation Network

Tested with Python 3.6 and Pytorch '1.0.1.post2'

Example

Results with Fast-SCNN

Inference Speed (fps) on Cityscapes

mIoU on Cityscapes

Owner

Philip Popien

A fast and easy to use, moddable, Python based Minecraft server!

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Unofficial implementation of Fast-SCNN: Fast Semantic Segmentation Network

Related tags

Overview

Fast-SCNN: Fast Semantic Segmentation Network

Tested with Python 3.6 and Pytorch '1.0.1.post2'

Example

Results with Fast-SCNN

Inference Speed (fps) on Cityscapes

mIoU on Cityscapes

Owner

Philip Popien

A fast and easy to use, moddable, Python based Minecraft server!

Official PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? (ICCV 2021), Dennis Park*, Rares Ambrus*, Vitor Guizilini, Jie Li, and Adrien Gaidon.

TensorFlow implementation for Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How

Official repository for "On Improving Adversarial Transferability of Vision Transformers" (2021)

This is a classifier which basically predicts whether there is a gun law in a state or not, depending on various things like murder rates etc.

IAUnet: Global Context-Aware Feature Learning for Person Re-Identification

pcnaDeep integrates cutting-edge detection techniques with tracking and cell cycle resolving models.

NeurIPS 2021 Datasets and Benchmarks Track

Pipeline for employing a Lightweight deep learning models for LOW-power systems

GluonMM is a library of transformer models for computer vision and multi-modality research

TLoL (Python Module) - League of Legends Deep Learning AI (Research and Development)

Best Practices on Recommendation Systems

OpenMMLab Video Perception Toolbox. It supports Video Object Detection (VID), Multiple Object Tracking (MOT), Single Object Tracking (SOT), Video Instance Segmentation (VIS) with a unified framework.

Multi-task head pose estimation in-the-wild

PyTorch implementation of "Learn to Dance with AIST++: Music Conditioned 3D Dance Generation."

Python project to take sound as input and output as RGB + Brightness values suitable for DMX

[NeurIPS 2021] SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning

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A python library for time-series smoothing and outlier detection in a vectorized way.

Official PyTorch implementation of DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? (ICCV 2021), Dennis Park, Rares Ambrus, Vitor Guizilini, Jie Li, and Adrien Gaidon.