More than 50 object detection datasets from different industries

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author |Abhishek Annamraju compile |Flin source |medium

Computer vision is a rapidly developing field , A large number of new technologies and algorithms appear in different conferences and journals every day . Speaking of target detection , In theory, you'll learn a lot of algorithms , such as Faster-rcnn、Mask rcnn、Yolo、SSD、Retinenet、 cascade rcnn、Peleenet、EfficientDet、CornerNet…. This algorithm list is never finished ！

It is always beneficial to consolidate your learning experience by applying it to different data sets ！！！

thus , You tend to understand algorithms better , And we can intuitively understand which algorithms can run on which dataset .

We are Monk Computer Vision Org The open source team at the , List of image segmentation and action recognition data sets , A short tutorial is created for each object , You can use these datasets and try different object detection algorithms

The following is a short list of object detection datasets , Brief details about them and how to use them . Data sets come from the following areas ：

* Agriculture * Advanced driver assistance and automatic driving vehicle system * fashion , Retail and marketing * Wild animal * sports * Satellite imaging * Medical imaging * Security and surveillance * Underwater imaging

….. And more ！！！！！

The complete list can be found in github Find the relevant instructions and training code on

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo

Data sets related to agriculture

A）Winegrape Test data set

• https://github.com/thsant/wgisd

* The goal is ： Detection of grape clusters in vineyards

* application ： Monitor growth and analyze yield

* Details ：300 Images , with 5 Grape varieties 4400 A bounding box

* How to utilize data sets and use YoloV3 Pipeline build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Wine%20Grape%20Instance%20Detection%20Dataset.ipynb

B） Global wheat detection data set

• https://www.kaggle.com/c/global-wheat-detection/data

* The goal is ： Testing wheat crops in the field

* application ： Monitor growth and analyze yield

* Details ： with 100K + endorsed 3430 Images

* How to utilize data sets and use EfficientDet-D4 Pipeline build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Global%20Wheat%20Detection%20Kaggle%20(Starter%20Code)%20.ipynb

Advanced data set for driver assistance and autopilot vehicle systems

A）LISA Traffic sign detection data set

• http://cvrr.ucsd.edu/LISA/lisa-traffic-sign-dataset.html

* The goal is ： It is used to detect and classify traffic signs in dash cam images

* application ： Traffic sign recognition is a rule setting program for automatic driving

* Details ： stay 47 On the type of American logo 6610 There are on the frame 7855 A note

* How to make use of data set and establish custom usage EfficientDet-D3 Detector of pipeline

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/example_zoo/Example%20-%20LISA%20Traffic%20Sign%20Recognition%20(Multi-GPU).ipynb

* Another dataset is added to this repository

• LISA Vehicle detection data
  • http://cvrr.ucsd.edu/LISA/vehicledetection.html

B） Object detection under low illumination

• https://github.com/cs-chan/Exclusively-Dark-Image-Dataset

* The goal is ： Detection of objects on the road under low light conditions —— Fog , smog , Rain, etc

* application ： This is an important part of autopilot. , Because it can detect objects , Therefore, it is a safer vehicle under adverse conditions

* details ： stay 12 On different object types 7500 On frame 15K + notes

* How to utilize data sets and use EfficientDet-D3 Pipeline build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/example_zoo/Example%20-%20Object%20Detection%20in%20low%20lighting%20conditions.ipynb

C）LARA Traffic light detection data set

• http://www.lara.prd.fr/benchmarks/trafficlightsrecognition

* The goal is ： Detect traffic lights and classify them as red , Green and yellow

* Applications ： It can be used for the intersection of road network adas Auto driving system rules

* Details ： Three types of traffic 11K The frame and 20K + Annotation lights

* How to make use of data sets and establish the use of Mmdet-Faster-Rcnn-fpn50 Pipeline custom detection

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Lara%20Traffic%20Lights%20Detection%20Dataset.ipynb

D） Human detection using infrared image

• https://camel.ece.gatech.edu/

* The goal is ： Used to detect people in infrared images

* application ： The autopilot car is equipped with infrared cameras to detect objects in bad conditions.

* Details ：30 One with 1K + Annotated video sequence

* How to utilize data sets and use Mx-Rcnn Pipeline build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20person%20detection%20in%20infrared%20images.ipynb

E） Data set of pothole detection

• https://www.kaggle.com/chitholian/annotated-potholes-dataset

* The goal is ： Detecting potholes from road images

* application ： Smooth driving can be achieved by detecting road topography and potholes .

* Details ： 700 One in the pit with 3K + Annotated images

* How to utilize data sets and use M-Rcnn Pipeline build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Pothole%20detection%20on%20roads.ipynb

F）Nexet Vehicle detection data set

• https://www.kaggle.com/solesensei/nexet-original

* The goal is ： Detect road image of vehicle

* application ： Vehicle detection is the main part of automatic driving

* Details ：7000 Images , stay 6 There are 15K + notes

* How to utilize data sets and use Tensorflow Object Detection Building custom detectors API

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Nexet%20Dataset%20Vehicle%20Detection.ipynb

G）BDD100K Adas Data sets

• https://www.kaggle.com/solesensei/solesensei_bdd100k

* The goal is ： Detecting objects on the road

* application ： Testing vehicles , Traffic signs and people are the main components of automatic driving

* Details ：100K Images , Yes 10 Types of objects 250K + notes

* How to make use of data set and establish custom usage Tensorflow Object detection API The detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20BDD100K%20dataset%20with%20TensorRT%20optimization.ipynb

H）Linkopings Traffic sign data set

• http://www.cvl.isy.liu.se/research/datasets/traffic-signs-dataset/

* The goal is ： Detection of traffic signs in images

* application ： Detection of traffic signs is the first step to understand the traffic rules

* Details ：3K Images , Yes 40 Various types of traffic signs are provided 5K + notes

** How to utilize data sets and use Mmdet-Cascade Mask-Rcnn Building custom detectors

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Linkopings%20Traffic%20Sign%20Dataset.ipynb

fashion 、 Retail and marketing related data sets

A） Billboard detection （ Second sampling OpenImages Data sets ） Data sets

• https://storage.googleapis.com/openimages/web/index.html

* The goal is ： Detecting billboards in images

* Applications ： The detection of billboards is a key part of automatic analysis of the whole city's marketing activities

* Details ：2K Images , On the billboard 5K + notes

* How to utilize data sets and use Retinanet Building custom detectors

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Billboard%20(Hoarding%20detection).ipynb

B）DeepFashion2 Fashion element detection data set

• https://github.com/switchablenorms/DeepFashion2

* The goal is ： Detection of fashion products in images , clothes& decoration

* Applications ： Application fashion detection has a huge application from data sorting to recommendation engine

* Details ：490K Images , With about 100 Annotation object classes

* How to make use of data set and build customization CornetNet-Lite Pipeline detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Fashion%20detector%20on%20DeepFashion2%20Dataset.ipynb

* Another data set related to fashion is Taobao commodity data set

• http://www.sysu-hcp.net/taobao-commodity-dataset/

C）Qmul-OpenLogo Logo detection dataset

• https://qmul-openlogo.github.io/

* The goal is ： Detect different logos in natural images

* application ： Analyzing the frequency of logos in videos and natural scenes is crucial to marketing

* Details ：16K Training pictures , Including the logo of various brands —— food 、 vehicle 、 Chain restaurant 、 Delivery Service 、 Airlines, etc

* How to utilize data sets and use mx-rcnn Pipeline build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20logo%20detection.ipynb

Sports related data sets

A） Football detection data set （ from OpenImages The data set was resampled ）

• https://storage.googleapis.com/openimages/web/index.html

* The goal is ： Cross frame detection of football in video

* application ： The detection of football position is very important in the automatic analysis of offside

* Details ： about 3K Training images .

* How to utilize data sets and use yolo-v3 Pipeline build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20FootBall%20detection.ipynb

B） Card type detection

• https://www.kaggle.com/luantm/playing-card

* The goal is ： Detection of cards in natural images and classification of card types

* application ： The possible application is to analyze the winning probability of different card games

* Details ：52 In card types 500 More than images

* How to make use of data set and establish custom usage mx-rcnn Pipeline detector

• https://www.kaggle.com/luantm/playing-card

C） Football player detection in thermal image

• https://www.kaggle.com/aalborguniversity/thermal-soccer-dataset

* The goal is ： Using thermal images to locate and track players

* application ： Tracking the players in the game is a key part of generation analysis

* Details ： exceed 5K + The annotations 3K + Images .

* How to utilize data sets and use mmdet quick-rcnn Pipeline build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Thermal%20Soccer%20-%20(Player%20Detection).ipynb

Security and monitoring related data sets

A）CCTV In traffic cameras MIO-TCD Vehicle detection

* The goal is ： Detecting vehicles in CCTV cameras

* application ： Detection of vehicles in CCTV cameras is a key part of security monitoring applications

* Details ：113K Images , stay 5 On more than two types of vehicles 200K + notes

* How to utilize data sets and use Mmdet-Retinanet Pipeline build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20MIO-TCD%20Vehicle%20Localization%20Dataset.ipynb

B）WIDER Personnel detection data set

• https://wider-challenge.org/2019.html

* The goal is ： Detection of personnel in CCTV and natural scene images and videos

* application ： be based on CCTV People detection is the core of security and monitoring applications

* Details ：10K + Images and 20K + Notes can detect pedestrians

* How to make use of data set and establish custom usage Cornernet-Lite Pipe detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Fashion%20detector%20on%20DeepFashion2%20Dataset.ipynb

C） Protective equipment - Helmet and vest inspection

• https://github.com/ciber-lab/pictor-ppe

* The goal is ： Helmets and vests of inspectors

* application ： This is an integral part of security compliance monitoring

* Details ：1.5K + Images and 2K + Notes can detect people , Helmets and vests

* How to use data sets and build custom detectors Mmdet — Cascade RPN

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Protective%20gear%20-%20helmet%20and%20vest%20detection.ipynb

D） Anomaly detection in video

• https://www.crcv.ucf.edu/projects/real-world/

* The goal is ： The video is classified according to the operations performed in the video

* application ： Real time detection of anomalies helps prevent crime

* Details ： Corresponding to 10 Of exception categories 1K + video .

* How to utilize data sets and use mmaction-tsn50 Pipeline construction custom classifier

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20UCF101%20Action%20Recognition.ipynb

Medical image data set

A） Ultrasound brachial plexus （BP） Neural segmentation dataset

• https://www.kaggle.com/c/ultrasound-nerve-segmentation/data

* The goal is ： Segmentation of some nerve types in ultrasound images

* application ： By using a indwelling catheter that can block or relieve pain at the source , Helps improve pain management .

* Details ： 11K + Image and related example mask , Used to detect nerves

* How to use datasets and build custom detectors

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Ultrasound%20nerve%20segmentation.ipynb

B） In cells PanNuke Cancer case segmentation

• https://www.kaggle.com/andrewmvd/cancer-inst-segmentation-and-classification

* The goal is ： Segmentation of different cell types in slide images

* Applications ： The existence of cancer cells and dead cells in megabyte data

* Details ：3K+ Images , With association instance mask for detecting different unit types

* How to use datasets and build custom detectors

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20PanNuke%20Dataset%20CellType%20Instance%20Segmentation.ipynb

Satellite imaging data set

A） Road segmentation in satellite image

• https://www.kaggle.com/insaff/massachusetts-roads-dataset

* The goal is ： Segmentation of trace routes in satellite images

* application ： Help urban planning and road monitoring

* Details ：1K + Image and related instance masks can detect different road regions

* How to use datasets and build custom detectors

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Road%20segmentation%20on%20Satellite%20Imageset.ipynb

B） Traversable region segmentation in synthetic lunar image

• https://www.kaggle.com/romainpessia/artificial-lunar-rocky-landscape-dataset

* The goal is ： The rock is segmented and traversable areas are found in the lunar image

* application ： Basic elements in path planning of autonomous Rover

* Details ： With related instance mask 10K + Images to detect different rocks and flat ground

* How to utilize data sets and build custom detectors

C） Vehicle and swimming pool detection in satellite images

• https://www.kaggle.com/kbhartiya83/swimming-pool-and-car-detection

* The goal is ： Vehicle and swimming pool detection in satellite images

* application ： This is a key part of the property tax estimate

* Details ：3.5K+ picture , There are cars and swimming pools 5K+ Comment tags

* How to utilize data sets and use cornernet lite Pipeline build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Car%20and%20Pool%20Detection.ipynb

D） Road and residential area segmentation in aerial images

• https://www.kaggle.com/cceekkigg/berlin-aoi-dataset

* The goal is ： Segmentation of roads and residential areas in satellite images

* application ： This is a key part of the property tax estimate

* Details ： With split mask 100 Ultra high resolution images

* How to use datasets and build custom detectors

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20CitySeg%20Dataset%20Road%20and%20Houses%20Segmentation.ipynb

* Another similar road segmentation dataset and related training code

• Data sets ：https://www.kaggle.com/srikaranand/road-segmentation-dataset
• Training code ：https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Road%20Segmentation%20in%20Satellite%20Images%20-%202.ipynb

E） Water segmentation in satellite images

• https://www.kaggle.com/franciscoescobar/satellite-images-of-water-bodies

* The goal is ： Segmentation of water body in satellite image

* application ： It is important to understand how water bodies change and evolve over time

- With split mask 100 Ultra high resolution images

* How to use datasets and build custom detectors

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Water%20Body%20Segmentation.ipynb

* Another such dataset is DeepGlobe Land cover classification and its related use criteria

• Data sets ：https://competitions.codalab.org/competitions/18468#participate-get_starting_kit
• Relevant usage criteria ：https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20DeepGlobe%20Land%20Cover%20Classification.ipynb

Wildlife related data sets

A） Tiger detection data set （ from OpenImages sampling ）

• https://storage.googleapis.com/openimages/web/index.html

* The goal is ： Detect tigers in nature and UAV images

* application ： Monitoring endangered species

* Details ： with 4k + The annotations 2K + Images .

* How to utilize data sets and use Cornernet-lite Pipeline build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Tiger%20detection%20using%20Cornernet-Saccade%20(No%20Val%20Dataset).ipynb

* Another such dataset could be a monkey detection dataset and related tutorials

• Monkey detection data set ：https://storage.googleapis.com/openimages/web/index.html
• Related tutorials ：https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Monkey%20detection%20in%20the%20wild.ipynb

B） Zebra and giraffe detection data set

• https://lev.cs.rpi.edu/public/datasets/wild.tar.gz

* The goal is ： Detection of zebra and giraffe species in natural and UAV images

* application ： Monitoring endangered species

* Details ： with 5k + The annotations 5K + Images .

* How to use datasets and use efficiencydet-d3 Pipeline build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Wildlife%20Localization%20-%20(Giraffes%2C%20Zebras%2C%20Impalas).ipynb

C） Caltech camera trap dataset

• https://beerys.github.io/CaltechCameraTraps/

* The goal is ： Detect animals in trap camera type images

* application ： Monitoring endangered species

* Details ： with 8k + The annotations 10K + Images .

* How to utilize data sets and use Retinanet Pipeline build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Caltech%20cameratrap%20ECCV%20animal%20detection.ipynb

* Another such camera dataset and related training code

• Data sets ：https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Cameratrap%20Animals%20Detection%20-%201.ipynb
• Training code ：https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Cameratrap%20Animals%20Detection%20-%201.ipynb

D） Elephant detection data set （ from COCO Data set sampling ）

• https://cocodataset.org/#download

* The goal is ： Detection of elephant species in natural and UAV images

* application ： Monitoring endangered species

* Details ： with 5k + The annotations 5K + Images .

* How to utilize data sets and use mmdet-maskrcnn Building custom detectors

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Elephant%20segmentation%20in%20the%20wild.ipynb

Underwater data set

A） Sea turtles found in the wild

• https://lev.cs.rpi.edu/public/datasets/wild.tar.gz

* The goal is ： Detecting turtles in underwater images

* application ： Monitoring endangered species

* Details ： with 5k + The annotations 5K + Images .

* How to use data set and use effective data to build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Wildlife%20Localization%20-%20(Extended%20version%20with%20sea%20turtles).ipynb

* Similar datasets , Monitoring of underwater fish

• http://groups.inf.ed.ac.uk/f4k/GROUNDTRUTH/RECOG/

Related codes

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Underwater%20Fish%20Segmentation.ipynb

B） Underwater waste detection data set

• https://conservancy.umn.edu/handle/11299/214366

* The goal is ： Detection of marine waste

* application ： Monitoring and control of marine litter

* Details ： with 5k + The annotations 2K + Images .

* How to use data set and use effective data to build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Underwater%20Trash%20Detection.ipynb

* More complex pixel based garbage classification data sets and related codes

• Garbage classification data set ：https://conservancy.umn.edu/handle/11299/214865
• Related codes ：https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20SUIM%20Dataset%20Underwater%20Object%20Segmentation.ipynb

C）SUIM Underwater object detection data set

• http://irvlab.cs.umn.edu/resources/suim-dataset

* The goal is ： Segmentation of underwater objects

* application ： Path planning of autonomous underwater vehicle , Tracking divers and monitoring marine species

* Details ：1.5K + Images and 1.5k + Notes mask .

* How to use datasets and build custom detectors

• http://irvlab.cs.umn.edu/resources/suim-dataset

D） Salty underwater fish recognition data set

• https://www.kaggle.com/aalborguniversity/brackish-dataset/data

* The goal is ： Detection of marine species in underwater images .

* Applications ： Monitoring marine species

* Details ：89 Videos to detect fish , Crab , shrimp , jellyfish , Starfish

* How to utilize data sets and use mmdet Building custom detectors ——Faster-rcnn The Conduit

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Brackish%20Water%20Creatures%20Identification.ipynb

Data sets related to text analysis

A） Document layout detection dataset

• https://www.primaresearch.org/datasets/Layout_Analysis

* The goal is ： Examine document layout for further analysis

* application ： It is necessary to segment the image into different parts , In order to further apply the rule-based NLP And text recognition .

* Details ：5K + Images , with 10k + The label of the comment , Such as paragraph , Images , title .

* How to utilize data sets and use mx-rcnn Building custom detectors

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Document%20Layout%20Analysis%20(FasterRCNN).ipynb

* In a IIIT-AR-13K There are very similar datasets for graphic component detection in the document of , This is how to use data sets and train models on them

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Graphical%20figures%20detection%20in%20documents.ipynb

B） Total text dataset

• https://github.com/cs-chan/Total-Text-Dataset

* The goal is ： Locating text in natural scenes

* Applications ： Use OCR Basic components identified

* Details ： with 5K + Polygon annotated 1.5K + Images

* How to utilize data sets and use Text-Snake Pipeline build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Text%20Localization%20over%20Total-Text%20Dataset.ipynb

C）YY-Mnist Simple OCR Data sets

* The goal is ： Locate and classify the numbers in the white background image

* Applications ： Use OCR Basic components identified

* Details ： exceed 10 Class has 2K + endorsed 1K Images

* How to utilize data sets and use Retinanet Pipeline build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20OCR%20over%20YYMnist%20Dataset.ipynb

Other datasets

A）TACO Garbage detection data set

• http://tacodataset.org/

* The goal is – Locating and segmenting all kinds of rubbish in image

* Applications ： Key components of automated robots trying to solve the garbage problem in public places

* Details ： contain 20 More than three types of garbage objects 15K + The annotations 10K Images

* How to utilize data sets and use Retinanet Pipeline build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Trash%20(Waste)%20Detection.ipynb

B） General object detection data set for indoor scenes

• https://storage.googleapis.com/openimages/web/index.html

* The goal is ： Locating and detecting indoor objects in images

* Applications ： Auto tag images in real estate and rental sites with Amenities

* Details ： exceed 10 Different types of indoor objects （ For example, electrical appliances , The bed , The curtain , Chairs, etc )

* How to utilize data sets and use Retinanet Pipeline build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Indoor%20Image%20Object%20Detection%20and%20Tagging.ipynb

C）EgoHands Hand partial cut dataset

• http://vision.soic.indiana.edu/projects/egohands/

* The goal is ： Segmentation of hands in natural scenes

* application ： The first step in understanding gestures , And in human-computer interaction , Application of sign language recognition

* Details ：4.8K + Image and corresponding hand mask .

* How to utilize data sets and use Retinanet Pipeline build custom detector

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Hand%20segmentation%20(Ego-Hands%20Dataset).ipynb

D）UCF Action recognition data set

• https://www.crcv.ucf.edu/data/UCF101.php

* The goal is ： The video is classified according to the operations performed in the video

* application ： Marking video is very important for storing and retrieving a large number of videos

* Details ： Corresponding to 101 Of action categories 1K + video .

* How to utilize data sets and use mmaction-tsn50 Pipeline construction custom classifier

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20UCF101%20Action%20Recognition.ipynb

E） Oil tank data set

• https://www.kaggle.com/towardsentropy/oil-storage-tanks

* The goal is ： Oil tank detection in satellite image

* application ： Tracking tanks

* Details ： have 10K + The annotations 10K + Images .

* How to utilize data sets and use Retinanet Pipeline construction custom classifier

• https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20Oil%20Tanks%20Detection%20in%20Satellite%20Imagery.ipynb

Other action recognition data sets

A） Stair action recognition data set and how to train model on it

• Data sets ：https://actions.stair.center/videos.html

• Training models ：https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20STAIRS%20Action%20Recognition%20Dataset.ipynb

B）A2D Action recognition data set and how to train model on it

• Data sets ：http://web.eecs.umich.edu/~jjcorso/r/a2d/

• Training models ：https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20A2D%20Action%20Recognition%20Dataset.ipynb

C）KTH Action recognition data set and how to train model on it

• Data sets ：https://www.csc.kth.se/cvap/actions/

• Training models ：https://github.com/Tessellate-Imaging/Monk_Object_Detection/blob/master/application_model_zoo/Example%20-%20KTH%20Action%20Recognition%20Dataset.ipynb

appendix

More details about the tutorial , Please visit our Github page

• https://github.com/Tessellate-Imaging/Monk_Object_Detection

Link to the original text ：https://medium.com/towards-artificial-intelligence/50-object-detection-datasets-from-different-industry-domains-1a53342ae13d

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