📚 A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.

Rahul Vigneswaran

Last update: Jan 17, 2022

Related tags

Deep Learning computer-vision deep-learning metrics dnn pytorch comparison pytorch-implementation

Overview

Weird Deep Learning Metrics

📚 A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.

Metric	Description	Notes
Churn	Shows the disagreement between two model's prediction. Higher the churn, more the disagreement. Bounds: [0,1]. Takes two prediction arrays as inputs and retuns churn percentage.	From this paper

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.

Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning Overview This code is for paper: Not All Unlabeled Data are Equa

22 Nov 23, 2022

A collection of loss functions for medical image segmentation

3.1k Jan 3, 2023

Nicely is a real-time Feedback and Intervention Program Depression is a prevalent issue across all age groups, socioeconomic classes, and cultural identities.

1 Jan 16, 2022

Flower classification model that classifies flowers in 10 classes made using transfer learning (~85% accuracy).

flower-classification-inceptionV3 Flower classification model that classifies flowers in 10 classes. Training and validation are done using a pre-anot

1 Dec 12, 2021

A Deep learning based streamlit web app which can tell with which bollywood celebrity your face resembles.

Project Name: Which Bollywood Celebrity You look like A Deep learning based streamlit web app which can tell with which bollywood celebrity your face

20 Dec 28, 2021

Ivy is a templated deep learning framework which maximizes the portability of deep learning codebases.

Ivy is a templated deep learning framework which maximizes the portability of deep learning codebases. Ivy wraps the functional APIs of existing frameworks. Framework-agnostic functions, libraries and layers can then be written using Ivy, with simultaneous support for all frameworks. Ivy currently supports Jax, TensorFlow, PyTorch, MXNet and Numpy. Check out the docs for more info!

8.2k Jan 2, 2023

Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds (CVPR 2022, Oral)

Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds (CVPR 2022, Oral) This is the official implementat

259 Dec 25, 2022

🔮 Execution time predictions for deep neural network training iterations across different GPUs.

Habitat: A Runtime-Based Computational Performance Predictor for Deep Neural Network Training Habitat is a tool that predicts a deep neural network's

44 Dec 27, 2022

Registration Loss Learning for Deep Probabilistic Point Set Registration

RLLReg This repository contains a Pytorch implementation of the point set registration method RLLReg. Details about the method can be found in the 3DV

35 Nov 2, 2022

Owner

Rahul Vigneswaran

GitHub

TorchMetrics is a collection of 25+ PyTorch metrics implementations and an easy-to-use API to create custom metrics.

Machine learning metrics for distributed, scalable PyTorch applications.

1.2k Jan 6, 2023

Covid-19 Test AI (Deep Learning - NNs) Software. Accuracy is the %96.5, loss is the 0.09 :)

Covid-19 Test AI (Deep Learning - NNs) Software I developed a segmentation algorithm to understand whether Covid-19 Test Photos are positive or negati

28 Dec 4, 2021

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering Papers with code | Paper Nikola Zubić Pietro Lio University

213 Dec 27, 2022

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

This is the implementation of "Training deep neural networks via direct loss minimization" published at ICML 2016 in PyTorch. The implementation targe

1 Jan 18, 2022

This is the unofficial code of Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes. which achieve state-of-the-art trade-off between accuracy and speed on cityscapes and camvid, without using inference acceleration and extra data

Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes Introduction This is the unofficial code of Deep Dual-re

113 Dec 23, 2022

Official implementation of NeurIPS 2021 paper "One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective"

71 Dec 22, 2022

📚 A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.

Related tags

Overview

Weird Deep Learning Metrics

You might also like...

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.

A collection of loss functions for medical image segmentation

Nicely is a real-time Feedback and Intervention Program Depression is a prevalent issue across all age groups, socioeconomic classes, and cultural identities.

Flower classification model that classifies flowers in 10 classes made using transfer learning (~85% accuracy).

A Deep learning based streamlit web app which can tell with which bollywood celebrity your face resembles.

Ivy is a templated deep learning framework which maximizes the portability of deep learning codebases.

Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds (CVPR 2022, Oral)

🔮 Execution time predictions for deep neural network training iterations across different GPUs.

Registration Loss Learning for Deep Probabilistic Point Set Registration

Owner

Rahul Vigneswaran

TorchMetrics is a collection of 25+ PyTorch metrics implementations and an easy-to-use API to create custom metrics.

Covid-19 Test AI (Deep Learning - NNs) Software. Accuracy is the %96.5, loss is the 0.09 :)

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

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

This is the unofficial code of Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes. which achieve state-of-the-art trade-off between accuracy and speed on cityscapes and camvid, without using inference acceleration and extra data

Official implementation of NeurIPS 2021 paper "One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective"

Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation)

An implementation for the loss function proposed in Decoupled Contrastive Loss paper.

A collection of metrics for evaluating timbre dissimilarity using the TorchMetrics API

LeafSnap replicated using deep neural networks to test accuracy compared to traditional computer vision methods.

📚 A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.

Related tags

Overview

Weird Deep Learning Metrics

You might also like...

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

A collection of loss functions for medical image segmentation

Nicely is a real-time Feedback and Intervention Program Depression is a prevalent issue across all age groups, socioeconomic classes, and cultural identities.

Flower classification model that classifies flowers in 10 classes made using transfer learning (~85% accuracy).

A Deep learning based streamlit web app which can tell with which bollywood celebrity your face resembles.

Ivy is a templated deep learning framework which maximizes the portability of deep learning codebases.

Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds (CVPR 2022, Oral)

🔮 Execution time predictions for deep neural network training iterations across different GPUs.

Registration Loss Learning for Deep Probabilistic Point Set Registration

Owner

Rahul Vigneswaran

TorchMetrics is a collection of 25+ PyTorch metrics implementations and an easy-to-use API to create custom metrics.

Covid-19 Test AI (Deep Learning - NNs) Software. Accuracy is the %96.5, loss is the 0.09 :)

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

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

This is the unofficial code of Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes. which achieve state-of-the-art trade-off between accuracy and speed on cityscapes and camvid, without using inference acceleration and extra data

Official implementation of NeurIPS 2021 paper "One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective"

Recall Loss for Semantic Segmentation (This repo implements the paper: Recall Loss for Semantic Segmentation)

An implementation for the loss function proposed in Decoupled Contrastive Loss paper.

A collection of metrics for evaluating timbre dissimilarity using the TorchMetrics API

LeafSnap replicated using deep neural networks to test accuracy compared to traditional computer vision methods.

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.