Dataset's COCO dataset: Introduction to COCO dataset, download, and detailed guide on how to use it

Introduction to the COCO dataset

The full name of MS COCO is Microsoft Common Objects in Context, which originated from the Microsoft COCO dataset funded by Microsoft in 2014. Like the ImageNet competition, it is regarded as one of the most concerned and authoritative competitions in the field of computer vision .
The COCO dataset is a large, rich dataset for object detection, segmentation and captioning. This data set is aimed at scene understanding, mainly intercepted from complex daily scenes, and the position of the target in the image is calibrated through precise segmentation. The images include 91 types of objects, 328,000 images and 2,500,000 labels. So far, the largest dataset with semantic segmentation provides 80 categories, more than 330,000 pictures, 200,000 of which are labeled, and the number of individuals in the entire dataset exceeds 1.5 million.

Official website address: http://cocodataset.org

0. 80 categories of the COCO data set— the data set used by the YoloV3 algorithm

person(person)
bicycle(bicycle) car(automobile) motorbike(motorcycle) aeroplane(aircraft) bus(bus) train(train) truck(truck) boat(ship) traffic light(signal light) fire hydrant(fire hydrant)
stop sign (stop sign) parking meter (parking meter) bench (bench)
bird (bird) cat (cat) dog (dog) horse (horse) sheep (sheep) cow (cattle) elephant (elephant) bear (bear) ) zebra (zebra) giraffe (giraffe)
backpack (backpack) umbrella (umbrella) handbag (handbag) tie (tie) suitcase (suitcase)
frisbee (frisbee) skis (skis feet) snowboard (snowboard) sports ball (sports ball ) kite (kite) baseball bat (baseball bat) baseball glove (baseball glove) skateboard (skateboard) surfboard (surfboard) tennis racket (tennis racket) bottle (bottle) wine glass (goblet) cup (tea cup) fork (fork
) ) knife (knife)
spoon (spoon) bowl (bowl)
banana (banana) apple (apple) sandwich (sandwich) orange (orange) broccoli (broccoli) carrot (carrot) hot dog (hot dog) pizza (pizza) donut (sweet donut) cake (cake)
chair (chair) sofa (sofa) pottedplant (potted plant) bed (bed) dining table (table) toilet (toilet) tvmonitor (television) laptop (notebook) mouse (mouse) remote (remote control) keyboard (keyboard
) cell phone ( telephone)
microwave (microwave oven) oven (oven) toaster (toaster) sink (sink) refrigerator (refrigerator) book (book) clock (alarm clock
) vase (vase) scissors (scissors) teddy bear (teddy bear) hair drier ( hair dryer) toothbrush (toothbrush)

1. Significance of COCO dataset

The full name of MS COCO is Microsoft Common Objects in Context. It originated from the Microsoft COCO dataset funded by Microsoft in 2014. Like the ImageNet competition, it is regarded as one of the most concerned and authoritative competitions in the field of computer vision.
After the ImageNet competition was discontinued, the COCO competition has become the most authoritative and important benchmark in the fields of target recognition and detection, and it is also the only one in this field that can bring together Google, Microsoft, Facebook and many top domestic and foreign institutions. It is a competition jointly participated by schools and outstanding innovative enterprises.
This data set mainly solves three problems: target detection, contextual relationship between targets, and precise positioning of targets in 2 dimensions. The COCO data set has 91 categories. Although there are fewer categories than ImageNet and SUN, there are more images in each category, which is conducive to obtaining more capabilities in a specific scene in each category. Compared with PASCAL VOC, it has more categories. and images.

1. COCO Target Detection Challenge

The COCO dataset contains 200,000 images;
More than 500,000 target annotations in 80 categories, it is the most widely publicized target detection database;
The average number of objects per image is 7.2, and these are well-known datasets for object detection challenges.

2. Characteristics of COCO dataset

COCO is a large-scale object detection, segmentation, and captioning dataset. COCO has several features：

3. The size and version of the dataset

Size: 25 GB (compressed)
Number of records: 330K images, 80 object categories, 5 labels per image, 250,000 keypoints.
The COCO data set is released in two parts, the former part was released in 2014, the latter part was released in 2015, the 2014 version: 82,783 training, 40,504 validation, and 40,775 testing images, there are 270k segmented people and 886k segmented objects; the 2015 version : 165,482 train, 81,208 val, and 81,434 test images.
(1) For the data of the 2014 version, there are a total of about 20G of pictures and about 500M of label files. The label file marks the precise pixel position of each segmentation + the precise coordinates of the bounding box, and its precision is two digits after the decimal point.

Download of COCO dataset

Official website address : http://cocodataset.org/#download

1. Download of the 2014 dataset

train2014：http://images.cocodataset.org/zips/train2014.zip
val2014：http://images.cocodataset.org/zips/val2014.zip

http://msvocds.blob.core.windows.net/coco2014/train2014.zip

2. Download of the 2017 dataset

http://images.cocodataset.org/zips/train2017.zip
http://images.cocodataset.org/annotations/annotations_trainval2017.zip

http://images.cocodataset.org/zips/val2017.zip
http://images.cocodataset.org/annotations/stuff_annotations_trainval2017.zip

http://images.cocodataset.org/zips/test2017.zip
http://images.cocodataset.org/annotations/image_info_test2017.zip