methodology-4

General Classification

3260 papers • 126 benchmarks • 313 datasets

Algorithms trying to solve the general task of classification.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in general-classification-4

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Libraries

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Use these libraries to find general-classification-4 models and implementations

osmr/imgclsmob

18 papers 2,911

Datasets

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Most implemented papers

Very Deep Convolutional Networks for Large-Scale Image Recognition

Andrew Zisserman, K. Simonyan•Wed Sep 03 2014

This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.

107446

Content

Wine

CVE to CWE mapping

MNIST

Fashion-MNIST

Mice Protein

ISOLET

Activity

CVR

benedekrozemberczki/karateclub

9 papers 2,072

open-mmlab/mmdetection

7 papers 27,479

rwightman/pytorch-image-models

6 papers 29,411

PaddlePaddle/PaddleDetection

6 papers 11,961

open-mmlab/mmpose

6 papers 4,887

Deci-AI/super-gradients

6 papers 4,284

makcedward/nlpaug

6 papers 4,280

open-mmlab/mmaction2

6 papers 3,837

songyouwei/ABSA-PyTorch

6 papers 1,929

sicara/easy-few-shot-learning

6 papers 867

yangheng95/LC-ABSA

6 papers 838

pytorch/vision

5 papers 15,292

pytorch/captum

4 papers 4,504

Westlake-AI/openmixup

4 papers 560

kevinmusgrave/pytorch-adapt

4 papers 315

4 papers 266

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YOLO9000: Better, Faster, Stronger

Ali Farhadi, Joseph Redmon•Sat Dec 24 2016

YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories, is introduced and a method to jointly train on object detection and classification is proposed, both novel and drawn from prior work.

17159 0

Paper Graph

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Dmitry Kalenichenko, Andrew G. Howard, Hartwig Adam, Tobias Weyand, Menglong Zhu, Weijun Wang, M. Andreetto•Sun Apr 16 2017

This work introduces two simple global hyper-parameters that efficiently trade off between latency and accuracy and demonstrates the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.

23871 0

Paper Graph

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

Devi Parikh, Dhruv Batra, Ramakrishna Vedantam, Ramprasaath R. Selvaraju, Abhishek Das, Michael Cogswell•Thu Oct 06 2016

The proposed Grad-CAM technique uses the gradients of any target concept flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept, and shows that even non-attention based models learn to localize discriminative regions of input image.

23616 0

Paper Graph

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Sergey Ioffe, Christian Szegedy, Vincent Vanhoucke, Alexander A. Alemi•Mon Feb 22 2016

Clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly is given and several new streamlined architectures for both residual and non-residual Inception Networks are presented.

15173 0

Paper Graph

Convolutional Neural Networks for Sentence Classification

Yoon Kim•Sun Aug 24 2014

The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification, and are proposed to allow for the use of both task-specific and static vectors.

13985 0

Paper Graph

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Chelsea Finn, P. Abbeel, S. Levine•Wed Mar 08 2017

An algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning is proposed.

13278 0

Paper Graph

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Alex Kendall, Vijay Badrinarayanan, R. Cipolla•Sun Nov 01 2015

Quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures, including FCN and DeconvNet.

17098 0

Paper Graph

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Sergey Ioffe, Christian Szegedy•Mon Feb 09 2015

Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.

45445 0

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Going deeper with convolutions

Christian Szegedy, D. Erhan, Vincent Vanhoucke, Yangqing Jia, P. Sermanet, Andrew Rabinovich, Scott E. Reed, Dragomir Anguelov, Wei Liu•Mon Sep 15 2014

A deep convolutional neural network architecture codenamed Inception is proposed that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).

46416 0

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