Repository for the semantic WMI loss

Last update: Sep 15, 2022

Related tags

Overview

Installation:

pip install -e .

Installing DL2:

First clone DL2 in a separate directory and install it using the following commands:

git clone https://github.com/eth-sri/dl2
cd dl2
pip install -r requirements.txt

If you are using a virtual environment then make sure to install DL2 in that environment. Now DL2 can be imported as a python libary. To achieve this just extend the PYTHONPATH variable to also point to the DL2 directory:

export PYTHONPATH="${PYTHONPATH}:{path_to_dl2}"

Execution:

Run CIFAR10 experiment:

run_image_experiments.py cifar10 --layers=10 --widen_factor=1 run

Run CIFAR100 experiment:

run_image_experiments.py cifar100 --layers=10 --widen_factor=1 run

Generate experiment conditions:

cd scripts
python generate_experiments.py

Help functions:

run_image_experiments.py -- --help
run_image_experiments.py cifar10 -- --help
run_image_experiments.py cifar100 -- --help

Acknowledgements

densenet-pytorch
Wide Residual Networks (WideResNets) in PyTorch
Wide Residual Networks (BMVC 2016) http://arxiv.org/abs/1605.07146 by Sergey Zagoruyko and Nikos Komodakis.

You might also like...

This is the repository for CVPR2021 Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales

Intro This is the repository for CVPR2021 Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales Vehicle Sam

39 Jul 21, 2022

Code repository for Semantic Terrain Classification for Off-Road Autonomous Driving

BEVNet Datasets Datasets should be put inside data/. For example, data/semantic_kitti_4class_100x100. Training BEVNet-S Example: cd experiments bash t

24 Dec 12, 2022

Public repository of the 3DV 2021 paper "Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds"

Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds Björn Michele1), Alexandre Boulch1), Gilles Puy1), Maxime Bucher1) and Rena

15 Dec 22, 2022

This is the code repository for the paper A hierarchical semantic segmentation framework for computer-vision-based bridge column damage detection

Bridge-damage-segmentation This is the code repository for the paper A hierarchical semantic segmentation framework for computer-vision-based bridge c

5 Dec 7, 2022

Seach Losses of our paper 'Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search', accepted by ICLR 2021.

CSE-Autoloss Designing proper loss functions for vision tasks has been a long-standing research direction to advance the capability of existing models

54 Dec 17, 2022

Comments

Reproduce the results for CIFAR-100
Dear authors,

I tried to run the CIFAR-100 experiment with the provided command run_image_experiments.py cifar100 --layers=10 --widen_factor=1 run but the accuracy is as follows. (I tried this command again but the accuracy is even lower.)

... Train [200/200]: Loss 1.046 Acc 69.507 AccSC 79.418 Test [200/200]: Loss 1.646 Acc 58.16 AccSC 70.78 ======== TESTING ON UNSEEN DATA ========= ======== USE FINAL MODEL ========= Test [1/200]: Loss 1.668 Acc 58.43 AccSC 71.07 Test [200/200]: Loss 1.668 Acc 58.43 AccSC 71.07 Final Model accuracy ====> 2.739713430404663 ======== USE BEST MODEL ========= Test [1/200]: Loss 1.67 Acc 58.42 AccSC 71.14 Test [200/200]: Loss 1.67 Acc 58.42 AccSC 71.14 Final Model accuracy ====> 1.0581140518188477

Could you help me with the following questions? Thank you!

How can I reproduce the results in the paper? For example, I cannot get the 74.4 ± (0.2) class accuracy and 85.4 ± (0.3) super-class accuracy, and it's not clear how to print out the constraint satisfaction accuracy.

How can I interpret the Final Model accuracy ====> 1.0581140518188477 in the above result?

How can I run the baseline for comparison?

The notebook https://github.com/NickHoernle/semantic_loss/blob/master/notebooks/cifar100_experiment.ipynb only prints out some pre-defined numbers. When would this notebook be updated?

Your help and time are greatly appreciated!

Best, Zhun
opened by zhunyoung 0

Repository for the semantic WMI loss

Related tags

Overview

Installation:

Installing DL2:

Execution:

Generate experiment conditions:

Help functions:

Acknowledgements

You might also like...

This is the repository for CVPR2021 Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales

Code repository for Semantic Terrain Classification for Off-Road Autonomous Driving

Public repository of the 3DV 2021 paper "Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds"

This is the code repository for the paper A hierarchical semantic segmentation framework for computer-vision-based bridge column damage detection

Seach Losses of our paper 'Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search', accepted by ICLR 2021.

Official code for paper "Optimization for Oriented Object Detection via Representation Invariance Loss".

Let Python optimize the best stop loss and take profits for your TradingView strategy.

EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising

Official implementation of our paper "LLA: Loss-aware Label Assignment for Dense Pedestrian Detection" in Pytorch.

Comments

Reproduce the results for CIFAR-100

Owner

Nick Hoernle

Implement of "Training deep neural networks via direct loss minimization" in PyTorch for 0-1 loss

Siamese-nn-semantic-text-similarity - A repository containing comprehensive Neural Networks based PyTorch implementations for the semantic text similarity task

Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation

Confident Semantic Ranking Loss for Part Parsing

A curated list of awesome resources related to Semantic Search🔎 and Semantic Similarity tasks.

Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation, CVPR 2018

Build upon neural radiance fields to create a scene-specific implicit 3D semantic representation, Semantic-NeRF

This is a repository for a Semantic Segmentation inference API using the Gluoncv CV toolkit

This repository allows you to anonymize sensitive information in images/videos. The solution is fully compatible with the DL-based training/inference solutions that we already published/will publish for Object Detection and Semantic Segmentation.

This is a repository for a semantic segmentation inference API using the OpenVINO toolkit