瞿梦

CONTENTS

1Classification

2Detection

3

Segmentation

Classification

Detection

Segmentation

1Classification

Evaluation index

percentage error=1-accuracy

MNISTCIFAR 10ILSVRC CLS-LOC

CIFAR 100PlacesMNIST
The MNIST database of handwritten digits has a training set of 60,000 examples,and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The
digits have been size-normalized and centered in a fixed-size image （28*28）.

CIFAR 10·60000 32x32 colour images·10 classes·6000 images per class.
·50000 training images·10000 test images·The dataset is divided into
five training batches andone test batch, each with
10000 images.
·The test batch contains
exactly 1000 randomlyselected images from eachclass.

CIFAR 100
100 classes
600 images each class
500 training images
100 testing images per class
20 superclasses.
Each image comes with a"fine" label (the class towhich it belongs) and a"coarse" label (thesuperclass to which itbelongs).

Places&Places2
Places(Places1 or Places205),
with 205 scene categories and2.5 millions of images with acategory label
Places2(Places365) contains
more than 10 million images
comprising 400+ uniquescene categories. Thedataset features 5000 to30,000 training images perclass, consistent with realworld frequencies ofoccurrencehttp://places.csail.mit.edu/user/leaderboard.php

Download Places365: http://places2.csail.mit.edu/download.html

Places Challenge 2015 result: http://places2.csail.mit.edu/results2015.html

ImageNet· 图像分类与目标定位（CLS-LOC）· 目标检测（DET）· 视频目标检测（VID）

ILSVRC（ImageNet Large Scale Visual Recognition Challenge） · 场景分类（Scene）
ImageNet contains over 14 million full-size annotated images, 21,841 Synsets, WordNete.g.in 2012: train 1281167，val 50000，test 100000。

Image Classification on ImageNet

Dataset download: http://image-net.org/download-images

2Detection

Evaluation index：mAPP-R曲线Pascal VOC

MS COCOKITTIILSVRC-DETPascal VOC
VOC2007:

Include 20 classes:
Person: person
Animal: bird, cat, cow, dog, horse, sheep
Vehicle: aeroplane, bicycle, boat, bus, car, motorbike, train
Indoor: bottle, chair, dining table, potted plant, sofa, tv/monitor

Train/validation/test: 9,963 images containing 24,640 annotated objects.
Train：5011 Test：4952

VOC2012: 20 classes. The train/val data has 11,530 images containing 27,450 ROI
annotated objects and 6,929 segmentations.
Dataset download: http://host.robots.ox.ac.uk/pascal/VOC

MS COCO
（1）Object segmentation（2）Recognition in Context（3）Multiple objects per image（4）More than 300,000 images（5）More than 2 Million instances（6）80 object categories（7）5 captions per image（8）Keypoints on 100,000 people

labelKITTI

Object detection：·Car·Van·Truck·Pedestrian·Person (sit- ting)·Cyclist·Tram·MiscImage Detection on ImageNet3Segmentation

Evaluation index：mIoU(mean IoU)

Pascal VOC

MS COCOADE20K

CityscapesPascal VOC

MS COCOADE20K·Invlude over 25,000 images（20ktrain，2k val，3ktest）·same scene categories than the Places Database·each image has the object and part segmentations·All object and part instances are annotated sparately

Dataset download: http://groups.csail.mit.edu/vision/datasets/ADE20K/

Cityscapes

Features•30 classes•50 cities•Several months (spring, summer, fall)•Daytime•Good/medium weather conditions•Manually selected frames
Volume•5 000 annotated images with fine annotations（2975train，500 val,1525test）•20 000 annotated images with coarse

Benchmark suite and evaluation server

annotations•Pixel-level semantic labeling•Instance-level semantic labeling

Dataset download: https://www.cityscapes-dataset.com/

Fine annotations

Coarse annotations

https://paperswithcode.com/sotahttps://www.visualdata.io/discovery

Thanks for Listening