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

Overview

Weird Deep Learning Metrics

πŸ“š 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
Owner
Rahul Vigneswaran
Rahul Vigneswaran
Blender Add-On for slicing meshes with planes

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Project Tugas Besar pertama Pengenalan Komputasi Institut Teknologi Bandung

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the code for our CVPR 2021 paper Bilateral Grid Learning for Stereo Matching Network [BGNet]

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GNEE - GAT Neural Event Embeddings

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hySLAM is a hybrid SLAM/SfM system designed for mapping

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The code for our paper "NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task β€”β€” Next Sentence Prediction"

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Sun Yi 201 Nov 21, 2022
License Plate Detection Application

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traiNNer is an open source image and video restoration (super-resolution, denoising, deblurring and others) and image to image translation toolbox based on PyTorch.

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[ICCV 2021] Official Pytorch implementation for Discriminative Region-based Multi-Label Zero-Shot Learning SOTA results on NUS-WIDE and OpenImages

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Modular Gaussian Processes

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Explaining Hyperparameter Optimization via PDPs

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PyTorch reimplementation of REALM and ORQA

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Breast Cancer Classification Model is applied on a different dataset

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Script that attempts to force M1 macs into RGB mode when used with monitors that are defaulting to YPbPr.

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SeqFormer: a Frustratingly Simple Model for Video Instance Segmentation

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Fully Convlutional Neural Networks for state-of-the-art time series classification

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Torchserve server using a YoloV5 model running on docker with GPU and static batch inference to perform production ready inference.

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