MSCOCO_ObjDet_Detail

MSCOCO object detection dataset의 format과 pycocotools의 간략한 사용법을 정리한 repository 입니다.

MSCOCO Dataset Format

MSCOCO dataset은 실제 data인 image와 data를 설명하는 json 파일로 구성되어있음. 아래 내용은 json의 format을 설명함

세부 내용은 https://cocodataset.org/#format-data 참조

영상 설명은 https://www.youtube.com/watch?v=h6s61a_pqfM 참조

common data format

{
    "info": info, 
    "images": [image], 
    "annotations": [annotation], 
    "licenses": [license],
}

info{
    "year": int, 
    "version": str, 
    "description": str, 
    "contributor": str, 
    "url": str, 
    "date_created": datetime,
}

image{
    "id": int, 
    "width": int, 
    "height": int, 
    "file_name": str, 
    "license": int, 
    "flickr_url": str, 
    "coco_url": str, 
    "date_captured": datetime,
}

license{
    "id": int, 
    "name": str, 
    "url": str,
}

Object Detection

categories field가 추가되고 annotations field가 정의됨

annotation의 id는 annotation field의 각 item에 할당된 고유한 id이며 category_id가 object의 class(i.e. label)의 id 이다.

annotation의 iscrowd는 segmentation과 관련있음, single object인지 group/cluster of objects(object들이 이미지에서 overlap 수준으로 뭉쳐있는경우, 혹은 굉장히 많은경우)인지 표시

For object detection annotations, the format is "bbox" : [x,y,width,height]

Where:

x, y: the upper-left coordinates of the bounding box

width, height: the dimensions of your bounding box

annotation{
    "id": int,
    "image_id": int,
    "category_id": int,
    "segmentation": RLE or [polygon],
    "area": float,
    "bbox": [x,y,width,height],
    "iscrowd": 0 or 1,
}

categories[{
    "id": int,
    "name": str,
    "supercategory": str,
}]

pycocotools examples

cocoapi github : https://github.com/cocodataset/cocoapi

cocoapi는 coco dataset을 visualize하고 id에 따라 image를 관리할수 있으며 object detection evaluation을 지원함

instances_val2017.json은 coco 2017 dataset에서 다운받음

yolov3_predictions.json은 yolov3 모델 output을 저장하였음, 참조링크 : https://github.com/ultralytics/yolov3/blob/master/tutorial.ipynb

minimal_2017.json는 object detection evaluation에 필요한 최소한의 field만 모은 json, make_minimal.py 참조

# yolov3_prediction.json
[{
    "image_id": int,
    "category_id": int,
    "bbox": [x,y,width,height],
    "score": float,
}]

example

예제는 pycocoEvalDemo.ipynb 참조

Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=70.58s).
Accumulating evaluation results...
DONE (t=8.79s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.433
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.630
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.470
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.284
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.485
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.538
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.346
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.581
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.634
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.474
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.686
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.766