Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.

Overview

Fully Convolutional Networks for Semantic Segmentation

This is the reference implementation of the models and code for the fully convolutional networks (FCNs) in the PAMI FCN and CVPR FCN papers:

Fully Convolutional Models for Semantic Segmentation
Evan Shelhamer*, Jonathan Long*, Trevor Darrell
PAMI 2016
arXiv:1605.06211

Fully Convolutional Models for Semantic Segmentation
Jonathan Long*, Evan Shelhamer*, Trevor Darrell
CVPR 2015
arXiv:1411.4038

Note that this is a work in progress and the final, reference version is coming soon. Please ask Caffe and FCN usage questions on the caffe-users mailing list.

Refer to these slides for a summary of the approach.

These models are compatible with BVLC/caffe:master. Compatibility has held since [email protected] with the merge of PRs #3613 and #3570. The code and models here are available under the same license as Caffe (BSD-2) and the Caffe-bundled models (that is, unrestricted use; see the BVLC model license).

PASCAL VOC models: trained online with high momentum for a ~5 point boost in mean intersection-over-union over the original models. These models are trained using extra data from Hariharan et al., but excluding SBD val. FCN-32s is fine-tuned from the ILSVRC-trained VGG-16 model, and the finer strides are then fine-tuned in turn. The "at-once" FCN-8s is fine-tuned from VGG-16 all-at-once by scaling the skip connections to better condition optimization.

  • FCN-32s PASCAL: single stream, 32 pixel prediction stride net, scoring 63.6 mIU on seg11valid
  • FCN-16s PASCAL: two stream, 16 pixel prediction stride net, scoring 65.0 mIU on seg11valid
  • FCN-8s PASCAL: three stream, 8 pixel prediction stride net, scoring 65.5 mIU on seg11valid and 67.2 mIU on seg12test
  • FCN-8s PASCAL at-once: all-at-once, three stream, 8 pixel prediction stride net, scoring 65.4 mIU on seg11valid

FCN-AlexNet PASCAL: AlexNet (CaffeNet) architecture, single stream, 32 pixel prediction stride net, scoring 48.0 mIU on seg11valid. Unlike the FCN-32/16/8s models, this network is trained with gradient accumulation, normalized loss, and standard momentum. (Note: when both FCN-32s/FCN-VGG16 and FCN-AlexNet are trained in this same way FCN-VGG16 is far better; see Table 1 of the paper.)

To reproduce the validation scores, use the seg11valid split defined by the paper in footnote 7. Since SBD train and PASCAL VOC 2011 segval intersect, we only evaluate on the non-intersecting set for validation purposes.

NYUDv2 models: trained online with high momentum on color, depth, and HHA features (from Gupta et al. https://github.com/s-gupta/rcnn-depth). These models demonstrate FCNs for multi-modal input.

SIFT Flow models: trained online with high momentum for joint semantic class and geometric class segmentation. These models demonstrate FCNs for multi-task output.

Note: in this release, the evaluation of the semantic classes is not quite right at the moment due to an issue with missing classes. This will be corrected soon. The evaluation of the geometric classes is fine.

PASCAL-Context models: trained online with high momentum on an object and scene labeling of PASCAL VOC.

Frequently Asked Questions

Is learning the interpolation necessary? In our original experiments the interpolation layers were initialized to bilinear kernels and then learned. In follow-up experiments, and this reference implementation, the bilinear kernels are fixed. There is no significant difference in accuracy in our experiments, and fixing these parameters gives a slight speed-up. Note that in our networks there is only one interpolation kernel per output class, and results may differ for higher-dimensional and non-linear interpolation, for which learning may help further.

Why pad the input?: The 100 pixel input padding guarantees that the network output can be aligned to the input for any input size in the given datasets, for instance PASCAL VOC. The alignment is handled automatically by net specification and the crop layer. It is possible, though less convenient, to calculate the exact offsets necessary and do away with this amount of padding.

Why are all the outputs/gradients/parameters zero?: This is almost universally due to not initializing the weights as needed. To reproduce our FCN training, or train your own FCNs, it is crucial to transplant the weights from the corresponding ILSVRC net such as VGG16. The included surgery.transplant() method can help with this.

What about FCN-GoogLeNet?: a reference FCN-GoogLeNet for PASCAL VOC is coming soon.

A PyTorch Implementation of PGL-SUM from "Combining Global and Local Attention with Positional Encoding for Video Summarization", Proc. IEEE ISM 2021

PGL-SUM: Combining Global and Local Attention with Positional Encoding for Video Summarization PyTorch Implementation of PGL-SUM From "PGL-SUM: Combin

Evlampios Apostolidis 35 Dec 22, 2022
A Python library for working with arbitrary-dimension hypercomplex numbers following the Cayley-Dickson construction of algebras.

Hypercomplex A Python library for working with quaternions, octonions, sedenions, and beyond following the Cayley-Dickson construction of hypercomplex

7 Nov 04, 2022
Official implementation of "Open-set Label Noise Can Improve Robustness Against Inherent Label Noise" (NeurIPS 2021)

Open-set Label Noise Can Improve Robustness Against Inherent Label Noise NeurIPS 2021: This repository is the official implementation of ODNL. Require

Hongxin Wei 12 Dec 07, 2022
Baseline powergrid model for NY

Baseline-powergrid-model-for-NY Table of Contents About The Project Built With Usage License Contact Acknowledgements About The Project As the urgency

Anderson Energy Lab at Cornell 6 Nov 24, 2022
Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

258 Dec 29, 2022
PRIME: A Few Primitives Can Boost Robustness to Common Corruptions

PRIME: A Few Primitives Can Boost Robustness to Common Corruptions This is the official repository of PRIME, the data agumentation method introduced i

Apostolos Modas 34 Oct 30, 2022
Fast and Easy Infinite Neural Networks in Python

Neural Tangents ICLR 2020 Video | Paper | Quickstart | Install guide | Reference docs | Release notes Overview Neural Tangents is a high-level neural

Google 1.9k Jan 09, 2023
My personal Home Assistant configuration.

About This is my personal Home Assistant configuration. My guiding princile is to have full local control of all my devices. I intend everything to ru

Chris Turra 13 Jun 07, 2022
This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR)

CEDR This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR) introduced in the following paper

phoenix 3 Feb 27, 2022
Shuffle Attention for MobileNetV3

SA-MobileNetV3 Shuffle Attention for MobileNetV3 Train Run the following command for train model on your own dataset: python train.py --dataset mnist

Sajjad Aemmi 36 Dec 28, 2022
A standard framework for modelling Deep Learning Models for tabular data

PyTorch Tabular aims to make Deep Learning with Tabular data easy and accessible to real-world cases and research alike.

801 Jan 08, 2023
Codes for "Template-free Prompt Tuning for Few-shot NER".

EntLM The source codes for EntLM. Dependencies: Cuda 10.1, python 3.6.5 To install the required packages by following commands: $ pip3 install -r requ

77 Dec 27, 2022
Light-Head R-CNN

Light-head R-CNN Introduction We release code for Light-Head R-CNN. This is my best practice for my research. This repo is organized as follows: light

jemmy li 835 Dec 06, 2022
IsoGCN code for ICLR2021

IsoGCN The official implementation of IsoGCN, presented in the ICLR2021 paper Isometric Transformation Invariant and Equivariant Graph Convolutional N

horiem 39 Nov 25, 2022
Graph Representation Learning via Graphical Mutual Information Maximization

GMI (Graphical Mutual Information) Graph Representation Learning via Graphical Mutual Information Maximization (Peng Z, Huang W, Luo M, et al., WWW 20

93 Dec 29, 2022
PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

Sharpness-aware Quantization for Deep Neural Networks Recent Update 2021.11.23: We release the source code of SAQ. Setup the environments Clone the re

Zhuang AI Group 30 Dec 19, 2022
"Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback"

This is code repo for our EMNLP 2017 paper "Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback", which implements the A2C algorithm on top of a neural encoder-

Khanh Nguyen 131 Oct 21, 2022
A Repository of Community-Driven Natural Instructions

A Repository of Community-Driven Natural Instructions TLDR; this repository maintains a community effort to create a large collection of tasks and the

AI2 244 Jan 04, 2023
Pytorch reimplementation of the Vision Transformer (An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale)

Vision Transformer Pytorch reimplementation of Google's repository for the ViT model that was released with the paper An Image is Worth 16x16 Words: T

Eunkwang Jeon 1.4k Dec 28, 2022
A Simple and Versatile Framework for Object Detection and Instance Recognition

SimpleDet - A Simple and Versatile Framework for Object Detection and Instance Recognition Major Features FP16 training for memory saving and up to 2.

TuSimple 3k Dec 12, 2022