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VOC는 ANNOTATION이 XML형태
MMDetection 설치
!pip install mmcv-full
!git clone https://github.com/open-mmlab/mmdetection.git
!cd mmdetection; python setup.py install
# 아래를 수행하기 전에 kernel을 restart 해야 함.
from mmdet.apis import init_detector, inference_detector
import mmcv
PASCAL VOC형태의 BCCD Dataset를 Download 후 MS-COCO 형태로 변경
- BCCD Dataset은 백혈구(WBC), 적혈구(RBC), 혈소판(Platelets) 세가지 유형의 Object Class를 가짐.
- 다운로드 받은 Dataset은 Pascal VOC 형태이므로 이를 별도의 유틸리티를 이용하여 MS-COCO 형태로 변환
!git clone https://github.com/Shenggan/BCCD_Dataset.git
VOC를 COCO로 변환하는 package적용하기
!git clone https://github.com/yukkyo/voc2coco.git
import os
# colab 버전은 아래 명령어로 ballnfish_classes.txt 를 수정합니다.
with open('/content/BCCD_Dataset/BCCD/labels.txt', "w") as f:
f.write("WBC\n")
f.write("RBC\n")
f.write("Platelets\n")
!cat /content/BCCD_Dataset/BCCD/labels.txt
# WBC
# RBC
# Platelets
# VOC를 COCO로 변환 수행. 학습/검증/테스트 용 json annotation을 생성.
# voc2coco.py를 수행하여, annotation, jpgimage의 metafile를 매칭시켜서 json파일로 만들겠다.
# train, test, val 별로
%cd voc2coco
!python voc2coco.py --ann_dir /content/BCCD_Dataset/BCCD/Annotations \
--ann_ids /content/BCCD_Dataset/BCCD/ImageSets/Main/train.txt \
--labels /content/BCCD_Dataset/BCCD/labels.txt \
--output /content/BCCD_Dataset/BCCD/train.json \
--ext xml
!python voc2coco.py --ann_dir /content/BCCD_Dataset/BCCD/Annotations \
--ann_ids /content/BCCD_Dataset/BCCD/ImageSets/Main/val.txt \
--labels /content/BCCD_Dataset/BCCD/labels.txt \
--output /content/BCCD_Dataset/BCCD/val.json \
--ext xml
!python voc2coco.py --ann_dir /content/BCCD_Dataset/BCCD/Annotations \
--ann_ids /content/BCCD_Dataset/BCCD/ImageSets/Main/test.txt \
--labels /content/BCCD_Dataset/BCCD/labels.txt \
--output /content/BCCD_Dataset/BCCD/test.json \
--ext xml
# /content/voc2coco
# Start converting !
# 100% 205/205 [00:00<00:00, 3822.02it/s]
# Start converting !
# 100% 87/87 [00:00<00:00, 3999.13it/s]
# Start converting !
# 100% 72/72 [00:00<00:00, 3397.08it/s]
!cat /content/BCCD_Dataset/BCCD/train.json
# {"images": [{"file_name": "BloodImage_00001.jpg", "height": 480, "width": 640, "id": "BloodImage_00001"}, {"file_name": "BloodImage_00003.jpg", "height": 480, "width": 640, "id": "BloodImage_00003"}, {"file_name": "BloodImage_00004.jpg", "height": 480, "width": 640, "id": "BloodImage_00004"}, {"file_name": "BloodImage_00005.jpg", "height": 480, "width": 640, "id": "BloodImage_00005"}, {"file_name": "BloodImage_00006.jpg", "height": 480, "width": 640, "id": "BloodImage_00006"}, {"file_name": "BloodImage_00008.jpg", "height": 480, "width": 640, "id": "BloodImage_00008"}, {"file_name": "BloodImage_00009.jpg", "height": 480, "width": 640, "id": "BloodImage_00009"}, {"file_name": "BloodImage_00010.jpg", "height": 480, "width": 640, "id": "BloodImage_00010"}, {"file_name": "BloodImage_00012.jpg", "height": 480, "width": 640, "id": "BloodImage_00012"}, {"file_name": "BloodImage_00013.jpg", "height": 480, "width": 640, "id": "BloodImage_00013"}, {"file_name": "BloodImage_00020.jpg", "height": 480, "width": 640, "id": "BloodImage_00020"}, {"file_name": "BloodImage_00022.jpg", "height": 480, "width": 640, "id": "BloodImage_00022"}, {"file_name": "BloodImage_00023.jpg", "height": 480, "width": 640, "id": "BloodImage_00023"}, {"file_name": "BloodImage_00024.jpg", "height": 480, "width": 640, "id": "BloodImage_00024"}, {"file_name": "BloodImage_00026.jpg", "height": 480, "width": 640, "id": "BloodImage_00026"}, {"file_name": "BloodImage_00032.jpg", "height": 480, "width": 640, "id": "BloodImage_00032"}, {"file_name": "BloodImage_00034.jpg", "height": 480, "width": 640, "id": "BloodImage_00034"}, {"file_name": "BloodImage_00036.jpg", "height": 480, "width": 640, "id": "BloodImage_00036"}, {"file_name": "BloodImage_00038.jpg", "height": 480, "width": 640, "id": "BloodImage_00038"}, {"file_name": "BloodImage_00039.jpg", "height": 480, "width": 640, "id": "BloodImage_00039"}, {"file_name": "BloodImage_00040.jpg", "height": 480, "width": 640, "id": "BloodImage_00040"}, {"file_name": "BloodImage_00042.jpg", "height": 480, "width": 640, "id": "BloodImage_00042"}, {"file_name": "BloodImage_00043.jpg", "height": 480, "width": 640, "id": "BloodImage_00043"}, {"file_name": "BloodImage_00044.jpg", "height": 480, "width": 640, "id": "BloodImage_00044"}, {"file_name": "BloodImage_00045.jpg", "height": 480, "width": 640, "id": "BloodImage_00045"}, {"file_name": "BloodImage_00046.jpg", "height": 480, "width": 640, "id": "BloodImage_00046"}, {"file_name": "BloodImage_00047.jpg", "height": 480, "width": 640, "id": "BloodImage_00047"}, {"file_name": "BloodImage_00048.jpg", "height": 480, "width": 640, "id": "BloodImage_00048"}, {"file_name": "BloodImage_00049.jpg", "height": 480, "width": 640, "id": "BloodImage_00049"}, {"file_name": "BloodImage_00050.jpg", "height": 480, "width": 640, "id": "BloodImage_00050"}, {"file_name": "BloodImage_00052.jpg", "height": 480, "width": 640, "id": "BloodImage_00052"}, {"file_name": "BloodImage_00054.jpg", "height": 480, "width": 640, "id": "BloodImage_00054"}, {"file_name": "BloodImage_00056.jpg", "height": 480, "width": 640, "id": "BloodImage_00056"}, {"file_name": "BloodImage_00059.jpg", "height": 480, "width": 640, "id": "BloodImage_00059"}, {"file_name": "BloodImage_00069.jpg", "height": 480, "width": 640, "id": "BloodImage_00069"}, {"file_name": "BloodImage_00070.jpg", "height": 480, "width": 640, "id": "BloodImage_00070"}, {"file_name": "BloodImage_00071.jpg", "height": 480, "width": 640, "id": "BloodImage_00071"}, {"file_name": "BloodImage_00076.jpg", "height": 480, "width": 640, "id": "BloodImage_00076"}, {"file_name": "BloodImage_00078.jpg", "height": 480, "width": 640, "id": "BloodImage_00078"}, {"file_name": "BloodImage_00079.jpg", "height": 480, "width": 640, "id": "BloodImage_00079"}, {"file_name": "BloodImage_00081.jpg", "height": 480, "width": 640, "id": "BloodImage_00081"}, {"file_name": "BloodImage_00082.jpg", "height": 480, "width": 640, "id": "BloodImage_00082"}, {"file_name": "BloodImage_00083.jpg", "height": 480, "width": 640, "id": "BloodImage_00083"}, {"file_name": "BloodImage_00086.jpg", "height": 480, "width": 640, "id": "BloodImage_00086"}, {"file_name": "BloodImage_00087.jpg", "height": 480, "width": 640, "id": "BloodImage_00087"}, {"file_name": "BloodImage_00090.jpg", "height": 480, "width": 640, "id": "BloodImage_00090"}, {"file_name": "BloodImage_00091.jpg", "height": 480, "width": 640, "id": "BloodImage_00091"}, {"file_name": "BloodImage_00092.jpg", "height": 480, "width": 640, "id": "BloodImage_00092"}, {"file_name": "BloodImage_00094.jpg", "height": 480, "width": 640, "id": "BloodImage_00094"}, {"file_name": "BloodImage_00095.jpg", "height": 480, "width": 640, "id": "BloodImage_00095"}, {"file_name": "BloodImage_00097.jpg", "height": 480, "width": 640, "id": "BloodImage_00097"}, {"file_name": "BloodImage_00100.jpg", "height": 480, "width": 640, "id": "BloodImage_00100"}, {"file_name": "BloodImage_00101.jpg", "height": 480, "width": 640, "id": "BloodImage_00101"}, {"file_name": "BloodImage_00106.jpg", "height": 480, "width": 640, "id": "BloodImage_00106"}, {"file_name": "BloodImage_00107.jpg", "height": 480, "width": 640, "id": "BloodImage_00107"}, {"file_name": "BloodImage_00108.jpg", "height": 480, "width": 640, "id": "BloodImage_00108"}, {"file_name": "BloodImage_00110.jpg", "height": 480, "width": 640, "id": "BloodImage_00110"}, {"file_name": "BloodImage_00112.jpg", "height": 480, "width": 640, "id": "BloodImage_00112"}, {"file_name": "BloodImage_00113.jpg", "height": 480, "width": 640, "id": "BloodImage_00113"}, {"file_name": "BloodImage_00114.jpg", "height": 480, "width": 640, "id": "BloodImage_00114"}, {"file_name": "BloodImage_00117.jpg", "height": 480, "width": 640, "id": "BloodImage_00117"}, {"file_name": "BloodImage_00120.jpg", "height": 480, "width": 640, "id": "BloodImage_00120"}, {"file_name": "BloodImage_00123.jpg", "height": 480, "width": 640, "id": "BloodImage_00123"}, {"file_name": "BloodImage_00124.jpg", "height": 480, "width": 640, "id": "BloodImage_00124"}, {"file_name": "BloodImage_00126.jpg", "height": 480, "width": 640, "id": "BloodImage_00126"}, {"file_name": "BloodImage_00127.jpg", "height": 480, "width": 640, "id": "BloodImage_00127"}, {"file_name": "BloodImage_00133.jpg", "height": 480, "width": 640, "id": "BloodImage_00133"}, {"file_name": "BloodImage_00134.jpg", "height": 480, "width": 640, "id": "BloodImage_00134"}, {"file_name": "BloodImage_00139.jpg", "height": 480, "width": 640, "id": "BloodImage_00139"}, {"file_name": "BloodImage_00141.jpg", "height": 480, "width": 640, "id": "BloodImage_00141"}, {"file_name": "BloodImage_00142.jpg", "height": 480, "width": 640, "id": "BloodImage_00142"}, {"file_name": "BloodImage_00144.jpg", "height": 480, "width": 640, "id": "BloodImage_00144"}, {"file_name": "BloodImage_00150.jpg", "height": 480, "width": 640, "id": "BloodImage_00150"}, {"file_name": "BloodImage_00154.jpg", "height": 480, "width": 640, "id": "BloodImage_00154"}, {"file_name": "BloodImage_00156.jpg", "height": 480, "width": 640, "id": "BloodImage_00156"}, {"file_name": "BloodImage_00158.jpg", "height": 480, "width": 640, "id": "BloodImage_00158"}, {"file_name": "BloodImage_00159.jpg", "height": 480, "width": 640, "id": "BloodImage_00159"}, {"file_name": "BloodImage_00160.jpg", "height": 480, "width": 640, "id": "BloodImage_00160"}, {"file_name": "BloodImage_00163.jpg", "height": 480, "width": 640, "id": "BloodImage_00163"}, {"file_name": "BloodImage_00164.jpg", "height": 480, "width": 640, "id": "BloodImage_00164"}, {"file_name": "BloodImage_00165.jpg", "height": 480, "width": 640, "id": "BloodImage_00165"}, {"file_name": "BloodImage_00166.jpg", "height": 480, "width": 640, "id": "BloodImage_00166"}, {"file_name": "BloodImage_00168.jpg", "height": 480, "width": 640, "id": "BloodImage_00168"}, {"file_name": "BloodImage_00170.jpg", "height": 480, "width": 640, "id": "BloodImage_00170"}, {"file_name": "BloodImage_00174.jpg", "height": 480, "width": 640, "id": "BloodImage_00174"}, {"file_name": "BloodImage_00175.jpg", "height": 480, "width": 640, "id": "BloodImage_00175"}, {"file_name": "BloodImage_00176.jpg", "height": 480, "width": 640, "id": "BloodImage_00176"}, {"file_name": "BloodImage_00177.jpg", "height": 480, "width": 640, "id": "BloodImage_00177"}, {"file_name": "BloodImage_00179.jpg", "height": 480, "width": 640, "id": "BloodImage_00179"}, {"file_name": "BloodImage_00182.jpg", "height": 480, "width": 640, "id": "BloodImage_00182"}, {"file_name": "BloodImage_00187.jpg", "height": 480, "width": 640, "id": "BloodImage_00187"}, {"file_name": "BloodImage_00189.jpg", "height": 480, "width": 640, "id": "BloodImage_00189"}, {"file_name": "BloodImage_00190.jpg", "height": 480, "width": 640, "id": "BloodImage_00190"}, {"file_name": "BloodImage_00191.jpg", "height": 480, "width": 640, "id": "BloodImage_00191"}, {"file_name": "BloodImage_00195.jpg", "height": 480, "width": 640, "id": "BloodImage_00195"}, {"file_name": "BloodImage_00196.jpg", "height": 480, "width": 640, "id": "BloodImage_00196"}, {"file_name": "BloodImage_00197.jpg", "height": 480, "width": 640, "id": "BloodImage_00197"}, {"file_name": "BloodImage_00198.jpg", "height": 480, "width": 640, "id": "BloodImage_00198"}, {"file_name": "BloodImage_00199.jpg", "height": 480, "width": 640, "id": "BloodImage_00199"}, {"file_name": "BloodImage_00200.jpg", "height": 480, "width": 640, "id": "BloodImage_00200"}, {"file_name": "BloodImage_00201.jpg", "height": 480, "width": 640, "id": "BloodImage_00201"}, {"file_name": "BloodImage_00202.jpg", "height": 480, "width": 640, "id": "BloodImage_00202"}, {"file_name": "BloodImage_00203.jpg", "height": 480, "width": 640, "id": "BloodImage_00203"}, {"file_name": "BloodImage_00204.jpg", "height": 480, "width": 640, "id": "BloodImage_00204"}, {"file_name": "BloodImage_00206.jpg", "height": 480, "width": 640, "id": "BloodImage_00206"}, {"file_name": "BloodImage_00210.jpg", "height": 480, "width": 640, "id": "BloodImage_00210"}, {"file_name": "BloodImage_00211.jpg", "height": 480, "width": 640, "id": "BloodImage_00211"}, {"file_name": "BloodImage_00212.jpg", "height": 480, "width": 640, "id": "BloodImage_00212"}, {"file_name": "BloodImage_00214.jpg", "height": 480, "width": 640, "id": "BloodImage_00214"}, {"file_name": "BloodImage_00215.jpg", "height": 480, "width": 640, "id": "BloodImage_00215"}, {"file_name": "BloodImage_00224.jpg", "height": 480, "width": 640, "id": "BloodImage_00224"}, {"file_name": "BloodImage_00231.jpg", "height": 480, "width": 640, "id": "BloodImage_00231"}, {"file_name": "BloodImage_00232.jpg", "height": 480, "width": 640, "id": "BloodImage_00232"}, {"file_name": "BloodImage_00235.jpg", "height": 480, "width": 640, "id": "BloodImage_00235"}, {"file_name": "BloodImage_00237.jpg", "height": 480, "width": 640, "id": "BloodImage_00237"}, {"file_name": "BloodImage_00239.jpg", "height": 480, "width": 640, "id": "BloodImage_00239"}, {"file_name": "BloodImage_00240.jpg", "height": 480, "width": 640, "id": "BloodImage_00240"}, {"file_name": "BloodImage_00242.jpg", "height": 480, "width": 640, "id": "BloodImage_00242"}, {"file_name": "BloodImage_00243.jpg", "height": 480, "width": 640, "id": "BloodImage_00243"}, {"file_name": "BloodImage_00247.jpg", "height": 480, "width": 640, "id": "BloodImage_00247"}, {"file_name": "BloodImage_00248.jpg", "height": 480, "width": 640, "id": "BloodImage_00248"}, {"file_name": "BloodImage_00250.jpg", "height": 480, "width": 640, "id": "BloodImage_00250"}, {"file_name": "BloodImage_00251.jpg", "height": 480, "width": 640, "id": "BloodImage_00251"}, {"file_name": "BloodImage_00252.jpg", "height": 480, "width": 640, "id": "BloodImage_00252"}, {"file_name": "BloodImage_00254.jpg", "height": 480, "width": 640, "id": "BloodImage_00254"}, {"file_name": "BloodImage_00256.jpg", "height": 480, "width": 640, "id": "BloodImage_00256"}, {"file_name": "BloodImage_00257.jpg", "height": 480, "width": 640, "id": "BloodImage_00257"}, {"file_name": "BloodImage_00258.jpg", "height": 480, "width": 640, "id": "BloodImage_00258"}, {"file_name": "BloodImage_00259.jpg", "height": 480, "width": 640, "id": "BloodImage_00259"}, {"file_name": "BloodImage_00263.jpg", "height": 480, "width": 640, "id": "BloodImage_00263"}, {"file_name": "BloodImage_00264.jpg", "height": 480, "width": 640, "id": "BloodImage_00264"}, {"file_name": "BloodImage_00265.jpg", "height": 480, "width": 640, "id": "BloodImage_00265"}, {"file_name": "BloodImage_00266.jpg", "height": 480, "width": 640, "id": "BloodImage_00266"}, {"file_name": "BloodImage_00267.jpg", "height": 480, "width": 640, "id": "BloodImage_00267"}, {"file_name": "BloodImage_00268.jpg", "height": 480, "width": 640, "id": "BloodImage_00268"}, {"file_name": "BloodImage_00270.jpg", "height": 480, "width": 640, "id": "BloodImage_00270"}, {"file_name": "BloodImage_00273.jpg", "height": 480, "width": 640, "id": "BloodImage_00273"}, {"file_name": "BloodImage_00275.jpg", "height": 480, "width": 640, "id": "BloodImage_00275"}, {"file_name": "BloodImage_00276.jpg", "height": 480, "width": 640, "id": "BloodImage_00276"}, {"file_name": "BloodImage_00278.jpg", "height": 480, "width": 640, "id": "BloodImage_00278"}, {"file_name": "BloodImage_00279.jpg", "height": 480, "width": 640, "id": "BloodImage_00279"}, {"file_name": "BloodImage_00281.jpg", "height": 480, "width": 640, "id": "BloodImage_00281"}, {"file_name": "BloodImage_00283.jpg", "height": 480, "width": 640, "id": "BloodImage_00283"}, {"file_name": "BloodImage_00288.jpg", "height": 480, "width": 640, "id": "BloodImage_00288"}, {"file_name": "BloodImage_00289.jpg", "height": 480, "width": 640, "id": "BloodImage_00289"}, {"file_name": "BloodImage_00291.jpg", "height": 480, "width": 640, "id": "BloodImage_00291"}, {"file_name": "BloodImage_00292.jpg", "height": 480, "width": 640, "id": "BloodImage_00292"}, {"file_name": "BloodImage_00294.jpg", "height": 480, "width": 640, "id": "BloodImage_00294"}, {"file_name": "BloodImage_00295.jpg", "height": 480, "width": 640, "id": "BloodImage_00295"}, {"file_name": "BloodImage_00297.jpg", "height": 480, "width": 640, "id": "BloodImage_00297"}, {"file_name": "BloodImage_00299.jpg", "height": 480, "width": 640, "id": "BloodImage_00299"}, {"file_name": "BloodImage_00300.jpg", "height": 480, "width": 640, "id": "BloodImage_00300"}, {"file_name": "BloodImage_00301.jpg", "height": 480, "width": 640, "id": "BloodImage_00301"}, {"file_name": "BloodImage_00302.jpg", "height": 480, "width": 640, "id": "BloodImage_00302"}, {"file_name": "BloodImage_00305.jpg", "height": 480, "width": 640, "id": "BloodImage_00305"}, {"file_name": "BloodImage_00307.jpg", "height": 480, "width": 640, "id": "BloodImage_00307"}, {"file_name": "BloodImage_00308.jpg", "height": 480, "width": 640, "id": "BloodImage_00308"}, {"file_name": "BloodImage_00309.jpg", "height": 480, "width": 640, "id": "BloodImage_00309"}, {"file_name": "BloodImage_00311.jpg", "height": 480, "width": 640, "id": "BloodImage_00311"}, {"file_name": "BloodImage_00313.jpg", "height": 480, "width": 640, "id": "BloodImage_00313"}, {"file_name": "BloodImage_00314.jpg", "height": 480, "width": 640, "id": "BloodImage_00314"}, {"file_name": "BloodImage_00317.jpg", "height": 480, "width": 640, "id": "BloodImage_00317"}, {"file_name": "BloodImage_00318.jpg", "height": 480, "width": 640, "id": "BloodImage_00318"}, {"file_name": "BloodImage_00322.jpg", "height": 480, "width": 640, "id": "BloodImage_00322"}, {"file_name": "BloodImage_00324.jpg", "height": 480, "width": 640, "id": "BloodImage_00324"}, {"file_name": "BloodImage_00325.jpg", "height": 480, "width": 640, "id": "BloodImage_00325"}, {"file_name": "BloodImage_00332.jpg", "height": 480, "width": 640, "id": "BloodImage_00332"}, {"file_name": "BloodImage_00335.jpg", "height": 480, "width": 640, "id": "BloodImage_00335"}, {"file_name": "BloodImage_00336.jpg", "height": 480, "width": 640, "id": "BloodImage_00336"}, {"file_name": "BloodImage_00339.jpg", "height": 480, "width": 640, "id": "BloodImage_00339"}, {"file_name": "BloodImage_00343.jpg", "height": 480, "width": 640, "id": "BloodImage_00343"}, {"file_name": "BloodImage_00344.jpg", "height": 480, "width": 640, "id": "BloodImage_00344"}, {"file_name": "BloodImage_00345.jpg", "height": 480, "width": 640, "id": "BloodImage_00345"}, {"file_name": "BloodImage_00347.jpg", "height": 480, "width": 640, "id": "BloodImage_00347"}, {"file_name": "BloodImage_00348.jpg", "height": 480, "width": 640, "id": "BloodImage_00348"}, {"file_name": "BloodImage_00351.jpg", "height": 480, "width": 640, "id": "BloodImage_00351"}, {"file_name": "BloodImage_00353.jpg", "height": 480, "width": 640, "id": "BloodImage_00353"}, {"file_name": "BloodImage_00354.jpg", "height": 480, "width": 640, "id": "BloodImage_00354"}, {"file_name": "BloodImage_00355.jpg", "height": 480, "width": 640, "id": "BloodImage_00355"}, {"file_name": "BloodImage_00356.jpg", "height": 480, "width": 640, "id": "BloodImage_00356"}, 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0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2797}, {"area": 11092, "iscrowd": 0, "bbox": [386, 135, 94, 118], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2798}, {"area": 7905, "iscrowd": 0, "bbox": [541, 270, 85, 93], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2799}, {"area": 10476, "iscrowd": 0, "bbox": [8, 27, 97, 108], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2800}, {"area": 9405, "iscrowd": 0, "bbox": [102, 0, 99, 95], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2801}, {"area": 7128, "iscrowd": 0, "bbox": [197, 0, 88, 81], "category_id": 2, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2802}, {"area": 756, "iscrowd": 0, "bbox": [0, 280, 21, 36], "category_id": 3, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2803}, {"area": 1302, "iscrowd": 0, "bbox": [382, 253, 42, 31], "category_id": 3, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2804}, {"area": 42120, "iscrowd": 0, "bbox": [309, 318, 260, 162], "category_id": 1, "ignore": 0, "segmentation": [], "image_id": "BloodImage_00408", "id": 2805}], "categories": [{"supercategory": "none", "id": 1, "name": "WBC"}, {"supercategory": "none", "id": 2, "name": "RBC"}, {"supercategory": "none", "id": 3, "name": "Platelets"}]}
# annotation json 파일을 잘 볼수 있는 jq 유틸리티 셋업.
!sudo apt-get install jq
# Reading package lists... Done
# Building dependency tree
# Reading state information... Done
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# /sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link
!jq . /content/BCCD_Dataset/BCCD/train.json > output.json
!tail -100 output.json
"category_id": 2,
"ignore": 0,
"segmentation": [],
"image_id": "BloodImage_00408",
"id": 2800
},
{
"area": 9405,
"iscrowd": 0,
"bbox": [
102,
0,
99,
95
],
"category_id": 2,
"ignore": 0,
"segmentation": [],
"image_id": "BloodImage_00408",
"id": 2801
},
{
"area": 7128,
"iscrowd": 0,
"bbox": [
197,
0,
88,
81
],
"category_id": 2,
"ignore": 0,
"segmentation": [],
"image_id": "BloodImage_00408",
"id": 2802
},
{
"area": 756,
"iscrowd": 0,
"bbox": [
0,
280,
21,
36
],
"category_id": 3,
"ignore": 0,
"segmentation": [],
"image_id": "BloodImage_00408",
"id": 2803
},
{
"area": 1302,
"iscrowd": 0,
"bbox": [
382,
253,
42,
31
],
"category_id": 3,
"ignore": 0,
"segmentation": [],
"image_id": "BloodImage_00408",
"id": 2804
},
{
"area": 42120,
"iscrowd": 0,
"bbox": [
309,
318,
260,
162
],
"category_id": 1,
"ignore": 0,
"segmentation": [],
"image_id": "BloodImage_00408",
"id": 2805
}
],
"categories": [
{
"supercategory": "none",
"id": 1,
"name": "WBC"
},
{
"supercategory": "none",
"id": 2,
"name": "RBC"
},
{
"supercategory": "none",
"id": 3,
"name": "Platelets"
}
]
}
!jq . /content/BCCD_Dataset/BCCD/test.json > output.json
!cat output.json
CocoDataset 클래스를 활용하여 BCCD Dataset을 로딩하기
- MS-COCO Dataset의 경우 별다른 Custom Code없이 Object들의 Class만 지정해 주면 됨
%cd /content
# /content
from mmdet.datasets.builder import DATASETS
from mmdet.datasets.coco import CocoDataset
@DATASETS.register_module(force=True)
class BCCDDataset(CocoDataset):
CLASSES = ('WBC', 'RBC', 'Platelets')
# 혹시 단일 class이더라도 ( 'wbc', ) 이렇게 , 을 남겨야함
Config 설정하고 Checkpoint 파일 다운로드 받기 ( pretrained file
config_file = '/content/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
checkpoint_file = '/content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
!pwd
# /content
!cd /content/mmdetection; mkdir checkpoints
!wget -O /content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
!ls -lia /content/mmdetection/checkpoints
# total 163376
# 4983200 drwxr-xr-x 2 root root 4096 Oct 18 14:47 .
# 4456450 drwxr-xr-x 19 root root 4096 Oct 18 14:47 ..
# 4983201 -rw-r--r-- 1 root root 167287506 Aug 28 2020 faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
from mmcv import Config
cfg = Config.fromfile(config_file)
print(cfg.pretty_text)
model = dict(
type='FasterRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='CocoDataset',
ann_file='data/coco/annotations/instances_train2017.json',
img_prefix='data/coco/train2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]),
val=dict(
type='CocoDataset',
ann_file='data/coco/annotations/instances_val2017.json',
img_prefix='data/coco/val2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]),
test=dict(
type='CocoDataset',
ann_file='data/coco/annotations/instances_val2017.json',
img_prefix='data/coco/val2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]))
evaluation = dict(interval=1, metric='bbox')
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
from mmdet.apis import set_random_seed
# dataset에 대한 환경 파라미터 수정.
cfg.dataset_type = 'BCCDDataset'
cfg.data_root = '/content/BCCD_Dataset/BCCD/'
# train, val, test dataset에 대한 type, data_root, ann_file, img_prefix 환경 파라미터 수정.
cfg.data.train.type = 'BCCDDataset'
cfg.data.train.data_root = '/content/BCCD_Dataset/BCCD/'
cfg.data.train.ann_file = 'train.json'
cfg.data.train.img_prefix = 'JPEGImages'
cfg.data.val.type = 'BCCDDataset'
cfg.data.val.data_root = '/content/BCCD_Dataset/BCCD/'
cfg.data.val.ann_file = 'val.json'
cfg.data.val.img_prefix = 'JPEGImages'
cfg.data.test.type = 'BCCDDataset'
cfg.data.test.data_root = '/content/BCCD_Dataset/BCCD/'
cfg.data.test.ann_file = 'test.json'
cfg.data.test.img_prefix = 'JPEGImages'
# class의 갯수 수정.
cfg.model.roi_head.bbox_head.num_classes = 3
# pretrained 모델
cfg.load_from = '/content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
# 학습 weight 파일로 로그를 저장하기 위한 디렉토리 설정.
cfg.work_dir = './tutorial_exps'
# 학습율 변경 환경 파라미터 설정.
cfg.optimizer.lr = 0.02 / 8
cfg.lr_config.warmup = None
cfg.log_config.interval = 10
# CocoDataset의 경우 metric을 bbox로 설정해야 함.(mAP아님. bbox로 설정하면 mAP를 iou threshold를 0.5 ~ 0.95까지 변경하면서 측정)
cfg.evaluation.metric = 'bbox'
cfg.evaluation.interval = 12
cfg.checkpoint_config.interval = 12
# 두번 config를 로드하면 lr_config의 policy가 사라지는 오류로 인하여 설정.
cfg.lr_config.policy='step'
# Set seed thus the results are more reproducible
cfg.seed = 0
set_random_seed(0, deterministic=False)
cfg.gpu_ids = range(1)
print(cfg.pretty_text)
model = dict(
type='FasterRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='StandardRoIHead',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=3,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=False,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)))
dataset_type = 'BCCDDataset'
data_root = '/content/BCCD_Dataset/BCCD/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='BCCDDataset',
ann_file='train.json',
img_prefix='JPEGImages',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
],
data_root='/content/BCCD_Dataset/BCCD/'),
val=dict(
type='BCCDDataset',
ann_file='val.json',
img_prefix='JPEGImages',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
],
data_root='/content/BCCD_Dataset/BCCD/'),
test=dict(
type='BCCDDataset',
ann_file='test.json',
img_prefix='JPEGImages',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
],
data_root='/content/BCCD_Dataset/BCCD/'))
evaluation = dict(interval=12, metric='bbox')
optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup=None,
warmup_iters=500,
warmup_ratio=0.001,
step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
checkpoint_config = dict(interval=12)
log_config = dict(interval=10, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = '/content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
resume_from = None
workflow = [('train', 1)]
work_dir = './tutorial_exps'
seed = 0
gpu_ids = range(0, 1)
Dataset을 만들고, 모델 학습 및 Inference 적용
from mmdet.datasets import build_dataset
from mmdet.models import build_detector
from mmdet.apis import train_detector
# train용 Dataset 생성.
datasets = [build_dataset(cfg.data.train)]
# loading annotations into memory...
# Done (t=0.02s)
# creating index...
# index created!
print(datasets[0])
# datasets[0].__dict__ 로 모든 self variables의 key와 value값을 볼 수 있음.
datasets[0].__dict__.keys()
# BCCDDataset Train dataset with number of images 205, and instance counts:
# +----------+-------+----------+-------+---------------+-------+----------+-------+----------+-------+
# | category | count | category | count | category | count | category | count | category | count |
# +----------+-------+----------+-------+---------------+-------+----------+-------+----------+-------+
# | | | | | | | | | | |
# | 0 [WBC] | 214 | 1 [RBC] | 2382 | 2 [Platelets] | 209 | | | | |
# +----------+-------+----------+-------+---------------+-------+----------+-------+----------+-------+
# dict_keys(['ann_file', 'data_root', 'img_prefix', 'seg_prefix', 'proposal_file', 'test_mode', 'filter_empty_gt', 'CLASSES', 'coco', 'cat_ids', 'cat2label', 'img_ids', 'data_infos', 'proposals', 'flag', 'pipeline'])
datasets[0].data_infos
# [{'file_name': 'BloodImage_00001.jpg',
# 'filename': 'BloodImage_00001.jpg',
# 'height': 480,
# 'id': 'BloodImage_00001',
# 'width': 640},
# ...
# {'file_name': 'BloodImage_00408.jpg',
# 'filename': 'BloodImage_00408.jpg',
# 'height': 480,
# 'id': 'BloodImage_00408',
# 'width': 640}]
datasets[0].pipeline
# Compose(
# LoadImageFromFile(to_float32=False, color_type='color', file_client_args={'backend': 'disk'})
# LoadAnnotations(with_bbox=True, with_label=True, with_mask=False, with_seg=False, poly2mask=True, poly2mask={'backend': 'disk'})
# Resize(img_scale=[(1333, 800)], multiscale_mode=range, ratio_range=None, keep_ratio=True, bbox_clip_border=True)
# RandomFlip(flip_ratio=0.5)
# Normalize(mean=[123.675 116.28 103.53 ], std=[58.395 57.12 57.375], to_rgb=True)
# Pad(size=None, size_divisor=32, pad_to_square=False, pad_val={'img': 0, 'masks': 0, 'seg': 255})
# DefaultFormatBundle
# Collect(keys=['img', 'gt_bboxes', 'gt_labels'], meta_keys=('filename', 'ori_filename', 'ori_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'img_norm_cfg'))
# )
model = build_detector(cfg.model, train_cfg=cfg.get('train_cfg'), test_cfg=cfg.get('test_cfg'))
model.CLASSES = datasets[0].CLASSES
print(model.CLASSES)
# ('WBC', 'RBC', 'Platelets')
# /usr/local/lib/python3.7/dist-packages/mmdet-2.17.0-py3.7.egg/mmdet/core/anchor/builder.py:17: UserWarning: ``build_anchor_generator`` would be deprecated soon, please use ``build_prior_generator``
# '``build_anchor_generator`` would be deprecated soon, please use '
import os.path as osp
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# epochs는 config의 runner 파라미터로 지정됨. 기본 12회
train_detector(model, datasets, cfg, distributed=False, validate=True) # val 불러와서 모델평가
2021-10-18 14:47:52,688 - mmdet - INFO - load checkpoint from /content/mmdetection/checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
2021-10-18 14:47:52,690 - mmdet - INFO - Use load_from_local loader
2021-10-18 14:47:52,824 - mmdet - WARNING - The model and loaded state dict do not match exactly
size mismatch for roi_head.bbox_head.fc_cls.weight: copying a param with shape torch.Size([81, 1024]) from checkpoint, the shape in current model is torch.Size([4, 1024]).
size mismatch for roi_head.bbox_head.fc_cls.bias: copying a param with shape torch.Size([81]) from checkpoint, the shape in current model is torch.Size([4]).
size mismatch for roi_head.bbox_head.fc_reg.weight: copying a param with shape torch.Size([320, 1024]) from checkpoint, the shape in current model is torch.Size([12, 1024]).
size mismatch for roi_head.bbox_head.fc_reg.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([12]).
2021-10-18 14:47:52,828 - mmdet - INFO - Start running, host: root@f8acc20ddcac, work_dir: /content/tutorial_exps
2021-10-18 14:47:52,834 - mmdet - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) CheckpointHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook
--------------------
before_train_epoch:
(VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) NumClassCheckHook
(LOW ) IterTimerHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook
--------------------
before_train_iter:
(VERY_HIGH ) StepLrUpdaterHook
(LOW ) IterTimerHook
(LOW ) EvalHook
--------------------
after_train_iter:
(ABOVE_NORMAL) OptimizerHook
(NORMAL ) CheckpointHook
(LOW ) IterTimerHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook
--------------------
after_train_epoch:
(NORMAL ) CheckpointHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook
--------------------
before_val_epoch:
(NORMAL ) NumClassCheckHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook
--------------------
before_val_iter:
(LOW ) IterTimerHook
--------------------
after_val_iter:
(LOW ) IterTimerHook
--------------------
after_val_epoch:
(VERY_LOW ) TextLoggerHook
--------------------
2021-10-18 14:47:52,835 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)
return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
/usr/local/lib/python3.7/dist-packages/mmdet-2.17.0-py3.7.egg/mmdet/core/anchor/anchor_generator.py:324: UserWarning: ``grid_anchors`` would be deprecated soon. Please use ``grid_priors``
warnings.warn('``grid_anchors`` would be deprecated soon. '
/usr/local/lib/python3.7/dist-packages/mmdet-2.17.0-py3.7.egg/mmdet/core/anchor/anchor_generator.py:361: UserWarning: ``single_level_grid_anchors`` would be deprecated soon. Please use ``single_level_grid_priors``
'``single_level_grid_anchors`` would be deprecated soon. '
2021-10-18 14:48:11,147 - mmdet - INFO - Epoch [1][10/103] lr: 2.500e-03, eta: 0:36:37, time: 1.792, data_time: 0.238, memory: 3163, loss_rpn_cls: 0.1477, loss_rpn_bbox: 0.0886, loss_cls: 0.8380, acc: 66.0742, loss_bbox: 0.7945, loss: 1.8688
2021-10-18 14:48:26,850 - mmdet - INFO - Epoch [1][20/103] lr: 2.500e-03, eta: 0:34:05, time: 1.572, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0410, loss_rpn_bbox: 0.0682, loss_cls: 0.4894, acc: 84.2676, loss_bbox: 0.6111, loss: 1.2097
2021-10-18 14:48:42,486 - mmdet - INFO - Epoch [1][30/103] lr: 2.500e-03, eta: 0:33:00, time: 1.564, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0200, loss_rpn_bbox: 0.0711, loss_cls: 0.3802, acc: 86.5137, loss_bbox: 0.4905, loss: 0.9619
2021-10-18 14:48:58,107 - mmdet - INFO - Epoch [1][40/103] lr: 2.500e-03, eta: 0:32:20, time: 1.561, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0248, loss_rpn_bbox: 0.0792, loss_cls: 0.3563, acc: 85.7715, loss_bbox: 0.4395, loss: 0.8997
2021-10-18 14:49:13,799 - mmdet - INFO - Epoch [1][50/103] lr: 2.500e-03, eta: 0:31:51, time: 1.569, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0216, loss_rpn_bbox: 0.0775, loss_cls: 0.3678, acc: 84.5312, loss_bbox: 0.4920, loss: 0.9588
2021-10-18 14:49:29,424 - mmdet - INFO - Epoch [1][60/103] lr: 2.500e-03, eta: 0:31:25, time: 1.563, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0652, loss_cls: 0.3114, acc: 87.1484, loss_bbox: 0.4320, loss: 0.8256
2021-10-18 14:49:44,975 - mmdet - INFO - Epoch [1][70/103] lr: 2.500e-03, eta: 0:31:01, time: 1.556, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0583, loss_cls: 0.3246, acc: 85.8203, loss_bbox: 0.4269, loss: 0.8280
2021-10-18 14:50:00,647 - mmdet - INFO - Epoch [1][80/103] lr: 2.500e-03, eta: 0:30:41, time: 1.567, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0233, loss_rpn_bbox: 0.0650, loss_cls: 0.2901, acc: 86.6016, loss_bbox: 0.3662, loss: 0.7446
2021-10-18 14:50:16,314 - mmdet - INFO - Epoch [1][90/103] lr: 2.500e-03, eta: 0:30:22, time: 1.567, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0117, loss_rpn_bbox: 0.0606, loss_cls: 0.2966, acc: 86.9824, loss_bbox: 0.3820, loss: 0.7508
2021-10-18 14:50:31,911 - mmdet - INFO - Epoch [1][100/103] lr: 2.500e-03, eta: 0:30:02, time: 1.560, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0240, loss_rpn_bbox: 0.0609, loss_cls: 0.2727, acc: 88.0469, loss_bbox: 0.3575, loss: 0.7150
2021-10-18 14:50:54,057 - mmdet - INFO - Epoch [2][10/103] lr: 2.500e-03, eta: 0:29:10, time: 1.742, data_time: 0.233, memory: 3163, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0590, loss_cls: 0.2809, acc: 87.5391, loss_bbox: 0.3818, loss: 0.7377
2021-10-18 14:51:09,722 - mmdet - INFO - Epoch [2][20/103] lr: 2.500e-03, eta: 0:28:55, time: 1.566, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0605, loss_cls: 0.2494, acc: 89.4141, loss_bbox: 0.3764, loss: 0.6997
2021-10-18 14:51:25,435 - mmdet - INFO - Epoch [2][30/103] lr: 2.500e-03, eta: 0:28:40, time: 1.570, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0557, loss_cls: 0.2251, acc: 90.4688, loss_bbox: 0.3036, loss: 0.6002
2021-10-18 14:51:41,089 - mmdet - INFO - Epoch [2][40/103] lr: 2.500e-03, eta: 0:28:25, time: 1.566, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0608, loss_cls: 0.2599, acc: 88.5352, loss_bbox: 0.3750, loss: 0.7120
2021-10-18 14:51:56,764 - mmdet - INFO - Epoch [2][50/103] lr: 2.500e-03, eta: 0:28:10, time: 1.568, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0562, loss_cls: 0.2804, acc: 87.6074, loss_bbox: 0.3292, loss: 0.6856
2021-10-18 14:52:12,388 - mmdet - INFO - Epoch [2][60/103] lr: 2.500e-03, eta: 0:27:55, time: 1.563, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0455, loss_cls: 0.2401, acc: 89.0723, loss_bbox: 0.2994, loss: 0.5979
2021-10-18 14:52:28,030 - mmdet - INFO - Epoch [2][70/103] lr: 2.500e-03, eta: 0:27:39, time: 1.564, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0524, loss_cls: 0.2657, acc: 88.3008, loss_bbox: 0.3692, loss: 0.7033
2021-10-18 14:52:43,643 - mmdet - INFO - Epoch [2][80/103] lr: 2.500e-03, eta: 0:27:23, time: 1.560, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0513, loss_cls: 0.2644, acc: 88.4668, loss_bbox: 0.3327, loss: 0.6608
2021-10-18 14:52:59,301 - mmdet - INFO - Epoch [2][90/103] lr: 2.500e-03, eta: 0:27:08, time: 1.567, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0097, loss_rpn_bbox: 0.0493, loss_cls: 0.2427, acc: 89.4336, loss_bbox: 0.3167, loss: 0.6184
2021-10-18 14:53:14,918 - mmdet - INFO - Epoch [2][100/103] lr: 2.500e-03, eta: 0:26:52, time: 1.561, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0551, loss_cls: 0.2767, acc: 87.0215, loss_bbox: 0.3340, loss: 0.6810
2021-10-18 14:53:37,139 - mmdet - INFO - Epoch [3][10/103] lr: 2.500e-03, eta: 0:26:19, time: 1.750, data_time: 0.234, memory: 3163, loss_rpn_cls: 0.0064, loss_rpn_bbox: 0.0491, loss_cls: 0.2134, acc: 90.7812, loss_bbox: 0.3132, loss: 0.5822
2021-10-18 14:53:52,789 - mmdet - INFO - Epoch [3][20/103] lr: 2.500e-03, eta: 0:26:04, time: 1.565, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0580, loss_cls: 0.2401, acc: 89.4336, loss_bbox: 0.3260, loss: 0.6377
2021-10-18 14:54:08,426 - mmdet - INFO - Epoch [3][30/103] lr: 2.500e-03, eta: 0:25:49, time: 1.563, data_time: 0.046, memory: 3163, loss_rpn_cls: 0.0095, loss_rpn_bbox: 0.0522, loss_cls: 0.2173, acc: 90.1660, loss_bbox: 0.3065, loss: 0.5856
2021-10-18 14:54:24,065 - mmdet - INFO - Epoch [3][40/103] lr: 2.500e-03, eta: 0:25:34, time: 1.564, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0107, loss_rpn_bbox: 0.0587, loss_cls: 0.2301, acc: 89.9121, loss_bbox: 0.3299, loss: 0.6293
2021-10-18 14:54:39,614 - mmdet - INFO - Epoch [3][50/103] lr: 2.500e-03, eta: 0:25:19, time: 1.555, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0530, loss_cls: 0.2393, acc: 89.2285, loss_bbox: 0.3204, loss: 0.6281
2021-10-18 14:54:55,205 - mmdet - INFO - Epoch [3][60/103] lr: 2.500e-03, eta: 0:25:04, time: 1.559, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0108, loss_rpn_bbox: 0.0466, loss_cls: 0.2780, acc: 86.8262, loss_bbox: 0.2879, loss: 0.6234
2021-10-18 14:55:10,793 - mmdet - INFO - Epoch [3][70/103] lr: 2.500e-03, eta: 0:24:49, time: 1.559, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0498, loss_cls: 0.2277, acc: 90.0195, loss_bbox: 0.3022, loss: 0.5930
2021-10-18 14:55:26,348 - mmdet - INFO - Epoch [3][80/103] lr: 2.500e-03, eta: 0:24:33, time: 1.556, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0486, loss_cls: 0.2372, acc: 89.6484, loss_bbox: 0.3007, loss: 0.5996
2021-10-18 14:55:41,922 - mmdet - INFO - Epoch [3][90/103] lr: 2.500e-03, eta: 0:24:18, time: 1.557, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0096, loss_rpn_bbox: 0.0524, loss_cls: 0.2315, acc: 89.4434, loss_bbox: 0.3501, loss: 0.6436
2021-10-18 14:55:57,458 - mmdet - INFO - Epoch [3][100/103] lr: 2.500e-03, eta: 0:24:02, time: 1.553, data_time: 0.046, memory: 3163, loss_rpn_cls: 0.0110, loss_rpn_bbox: 0.0490, loss_cls: 0.2349, acc: 89.6582, loss_bbox: 0.2973, loss: 0.5922
2021-10-18 14:56:19,591 - mmdet - INFO - Epoch [4][10/103] lr: 2.500e-03, eta: 0:23:34, time: 1.739, data_time: 0.233, memory: 3163, loss_rpn_cls: 0.0175, loss_rpn_bbox: 0.0588, loss_cls: 0.2577, acc: 88.3301, loss_bbox: 0.2968, loss: 0.6307
2021-10-18 14:56:35,179 - mmdet - INFO - Epoch [4][20/103] lr: 2.500e-03, eta: 0:23:19, time: 1.559, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0088, loss_rpn_bbox: 0.0496, loss_cls: 0.2249, acc: 89.9219, loss_bbox: 0.2827, loss: 0.5660
2021-10-18 14:56:50,736 - mmdet - INFO - Epoch [4][30/103] lr: 2.500e-03, eta: 0:23:04, time: 1.556, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0524, loss_cls: 0.2309, acc: 89.2188, loss_bbox: 0.3273, loss: 0.6242
2021-10-18 14:57:06,282 - mmdet - INFO - Epoch [4][40/103] lr: 2.500e-03, eta: 0:22:49, time: 1.555, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0089, loss_rpn_bbox: 0.0451, loss_cls: 0.2151, acc: 90.6738, loss_bbox: 0.3046, loss: 0.5737
2021-10-18 14:57:21,844 - mmdet - INFO - Epoch [4][50/103] lr: 2.500e-03, eta: 0:22:34, time: 1.556, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0106, loss_rpn_bbox: 0.0526, loss_cls: 0.2454, acc: 88.8770, loss_bbox: 0.3133, loss: 0.6219
2021-10-18 14:57:37,402 - mmdet - INFO - Epoch [4][60/103] lr: 2.500e-03, eta: 0:22:19, time: 1.556, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0112, loss_rpn_bbox: 0.0513, loss_cls: 0.2401, acc: 89.4336, loss_bbox: 0.2884, loss: 0.5910
2021-10-18 14:57:52,980 - mmdet - INFO - Epoch [4][70/103] lr: 2.500e-03, eta: 0:22:04, time: 1.557, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0554, loss_cls: 0.2413, acc: 89.2578, loss_bbox: 0.2870, loss: 0.5970
2021-10-18 14:58:08,563 - mmdet - INFO - Epoch [4][80/103] lr: 2.500e-03, eta: 0:21:48, time: 1.559, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0119, loss_rpn_bbox: 0.0454, loss_cls: 0.2204, acc: 90.3125, loss_bbox: 0.2928, loss: 0.5706
2021-10-18 14:58:24,113 - mmdet - INFO - Epoch [4][90/103] lr: 2.500e-03, eta: 0:21:33, time: 1.555, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0106, loss_rpn_bbox: 0.0541, loss_cls: 0.2269, acc: 89.9609, loss_bbox: 0.3076, loss: 0.5992
2021-10-18 14:58:39,697 - mmdet - INFO - Epoch [4][100/103] lr: 2.500e-03, eta: 0:21:18, time: 1.559, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0090, loss_rpn_bbox: 0.0505, loss_cls: 0.2398, acc: 89.2383, loss_bbox: 0.3206, loss: 0.6198
2021-10-18 14:59:01,793 - mmdet - INFO - Epoch [5][10/103] lr: 2.500e-03, eta: 0:20:53, time: 1.741, data_time: 0.233, memory: 3163, loss_rpn_cls: 0.0099, loss_rpn_bbox: 0.0457, loss_cls: 0.2310, acc: 89.3457, loss_bbox: 0.2852, loss: 0.5719
2021-10-18 14:59:17,360 - mmdet - INFO - Epoch [5][20/103] lr: 2.500e-03, eta: 0:20:38, time: 1.556, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0093, loss_rpn_bbox: 0.0483, loss_cls: 0.2239, acc: 89.6484, loss_bbox: 0.2795, loss: 0.5610
2021-10-18 14:59:33,025 - mmdet - INFO - Epoch [5][30/103] lr: 2.500e-03, eta: 0:20:23, time: 1.566, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0110, loss_rpn_bbox: 0.0564, loss_cls: 0.2414, acc: 89.4238, loss_bbox: 0.3214, loss: 0.6302
2021-10-18 14:59:48,632 - mmdet - INFO - Epoch [5][40/103] lr: 2.500e-03, eta: 0:20:08, time: 1.560, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0476, loss_cls: 0.2286, acc: 89.6875, loss_bbox: 0.2757, loss: 0.5645
2021-10-18 15:00:04,209 - mmdet - INFO - Epoch [5][50/103] lr: 2.500e-03, eta: 0:19:53, time: 1.559, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0106, loss_rpn_bbox: 0.0517, loss_cls: 0.2213, acc: 90.2930, loss_bbox: 0.2719, loss: 0.5556
2021-10-18 15:00:19,786 - mmdet - INFO - Epoch [5][60/103] lr: 2.500e-03, eta: 0:19:37, time: 1.558, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0102, loss_rpn_bbox: 0.0393, loss_cls: 0.1849, acc: 92.0898, loss_bbox: 0.2542, loss: 0.4886
2021-10-18 15:00:35,359 - mmdet - INFO - Epoch [5][70/103] lr: 2.500e-03, eta: 0:19:22, time: 1.557, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0115, loss_rpn_bbox: 0.0430, loss_cls: 0.2312, acc: 89.7266, loss_bbox: 0.2958, loss: 0.5815
2021-10-18 15:00:50,928 - mmdet - INFO - Epoch [5][80/103] lr: 2.500e-03, eta: 0:19:07, time: 1.557, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0454, loss_cls: 0.2032, acc: 91.3281, loss_bbox: 0.2920, loss: 0.5536
2021-10-18 15:01:06,512 - mmdet - INFO - Epoch [5][90/103] lr: 2.500e-03, eta: 0:18:52, time: 1.558, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0101, loss_rpn_bbox: 0.0529, loss_cls: 0.2109, acc: 90.4590, loss_bbox: 0.3155, loss: 0.5895
2021-10-18 15:01:22,111 - mmdet - INFO - Epoch [5][100/103] lr: 2.500e-03, eta: 0:18:37, time: 1.560, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0537, loss_cls: 0.2242, acc: 89.6289, loss_bbox: 0.3117, loss: 0.6031
2021-10-18 15:01:44,312 - mmdet - INFO - Epoch [6][10/103] lr: 2.500e-03, eta: 0:18:13, time: 1.746, data_time: 0.233, memory: 3163, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0506, loss_cls: 0.2074, acc: 90.7520, loss_bbox: 0.2752, loss: 0.5469
2021-10-18 15:01:59,964 - mmdet - INFO - Epoch [6][20/103] lr: 2.500e-03, eta: 0:17:58, time: 1.565, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0074, loss_rpn_bbox: 0.0402, loss_cls: 0.2013, acc: 91.2305, loss_bbox: 0.2628, loss: 0.5118
2021-10-18 15:02:15,565 - mmdet - INFO - Epoch [6][30/103] lr: 2.500e-03, eta: 0:17:43, time: 1.559, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0107, loss_rpn_bbox: 0.0541, loss_cls: 0.2096, acc: 91.0547, loss_bbox: 0.3057, loss: 0.5801
2021-10-18 15:02:31,228 - mmdet - INFO - Epoch [6][40/103] lr: 2.500e-03, eta: 0:17:28, time: 1.567, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0102, loss_rpn_bbox: 0.0431, loss_cls: 0.2095, acc: 90.8008, loss_bbox: 0.3012, loss: 0.5640
2021-10-18 15:02:46,946 - mmdet - INFO - Epoch [6][50/103] lr: 2.500e-03, eta: 0:17:13, time: 1.572, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0093, loss_rpn_bbox: 0.0565, loss_cls: 0.2169, acc: 90.1953, loss_bbox: 0.3056, loss: 0.5884
2021-10-18 15:03:02,644 - mmdet - INFO - Epoch [6][60/103] lr: 2.500e-03, eta: 0:16:58, time: 1.569, data_time: 0.046, memory: 3163, loss_rpn_cls: 0.0087, loss_rpn_bbox: 0.0444, loss_cls: 0.1868, acc: 91.9824, loss_bbox: 0.2689, loss: 0.5088
2021-10-18 15:03:18,398 - mmdet - INFO - Epoch [6][70/103] lr: 2.500e-03, eta: 0:16:43, time: 1.575, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0076, loss_rpn_bbox: 0.0476, loss_cls: 0.2094, acc: 90.6543, loss_bbox: 0.3088, loss: 0.5733
2021-10-18 15:03:34,123 - mmdet - INFO - Epoch [6][80/103] lr: 2.500e-03, eta: 0:16:28, time: 1.573, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0468, loss_cls: 0.2358, acc: 89.4141, loss_bbox: 0.2845, loss: 0.5796
2021-10-18 15:03:49,854 - mmdet - INFO - Epoch [6][90/103] lr: 2.500e-03, eta: 0:16:13, time: 1.573, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0107, loss_rpn_bbox: 0.0418, loss_cls: 0.2364, acc: 89.4629, loss_bbox: 0.2580, loss: 0.5468
2021-10-18 15:04:05,618 - mmdet - INFO - Epoch [6][100/103] lr: 2.500e-03, eta: 0:15:58, time: 1.576, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0128, loss_rpn_bbox: 0.0515, loss_cls: 0.2274, acc: 90.1855, loss_bbox: 0.2945, loss: 0.5862
2021-10-18 15:04:27,956 - mmdet - INFO - Epoch [7][10/103] lr: 2.500e-03, eta: 0:15:35, time: 1.757, data_time: 0.234, memory: 3163, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0552, loss_cls: 0.1878, acc: 91.8750, loss_bbox: 0.2967, loss: 0.5517
2021-10-18 15:04:43,678 - mmdet - INFO - Epoch [7][20/103] lr: 2.500e-03, eta: 0:15:20, time: 1.572, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0113, loss_rpn_bbox: 0.0543, loss_cls: 0.2078, acc: 91.2988, loss_bbox: 0.2867, loss: 0.5601
2021-10-18 15:04:59,445 - mmdet - INFO - Epoch [7][30/103] lr: 2.500e-03, eta: 0:15:05, time: 1.577, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0064, loss_rpn_bbox: 0.0436, loss_cls: 0.2141, acc: 90.7910, loss_bbox: 0.2589, loss: 0.5230
2021-10-18 15:05:15,222 - mmdet - INFO - Epoch [7][40/103] lr: 2.500e-03, eta: 0:14:50, time: 1.578, data_time: 0.046, memory: 3163, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0425, loss_cls: 0.1989, acc: 91.2402, loss_bbox: 0.2928, loss: 0.5463
2021-10-18 15:05:30,968 - mmdet - INFO - Epoch [7][50/103] lr: 2.500e-03, eta: 0:14:35, time: 1.574, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0069, loss_rpn_bbox: 0.0476, loss_cls: 0.1792, acc: 92.3438, loss_bbox: 0.2747, loss: 0.5085
2021-10-18 15:05:46,673 - mmdet - INFO - Epoch [7][60/103] lr: 2.500e-03, eta: 0:14:20, time: 1.571, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0118, loss_rpn_bbox: 0.0427, loss_cls: 0.1889, acc: 91.3867, loss_bbox: 0.2528, loss: 0.4962
2021-10-18 15:06:02,363 - mmdet - INFO - Epoch [7][70/103] lr: 2.500e-03, eta: 0:14:04, time: 1.569, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0059, loss_rpn_bbox: 0.0406, loss_cls: 0.2097, acc: 90.7910, loss_bbox: 0.2512, loss: 0.5075
2021-10-18 15:06:18,129 - mmdet - INFO - Epoch [7][80/103] lr: 2.500e-03, eta: 0:13:49, time: 1.577, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0104, loss_rpn_bbox: 0.0499, loss_cls: 0.2154, acc: 90.3320, loss_bbox: 0.2776, loss: 0.5534
2021-10-18 15:06:33,890 - mmdet - INFO - Epoch [7][90/103] lr: 2.500e-03, eta: 0:13:34, time: 1.576, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0420, loss_cls: 0.2420, acc: 89.1309, loss_bbox: 0.2749, loss: 0.5739
2021-10-18 15:06:49,605 - mmdet - INFO - Epoch [7][100/103] lr: 2.500e-03, eta: 0:13:19, time: 1.571, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0089, loss_rpn_bbox: 0.0460, loss_cls: 0.2090, acc: 91.2109, loss_bbox: 0.2762, loss: 0.5402
2021-10-18 15:07:12,005 - mmdet - INFO - Epoch [8][10/103] lr: 2.500e-03, eta: 0:12:57, time: 1.764, data_time: 0.234, memory: 3163, loss_rpn_cls: 0.0058, loss_rpn_bbox: 0.0533, loss_cls: 0.2009, acc: 91.5137, loss_bbox: 0.2877, loss: 0.5477
2021-10-18 15:07:27,751 - mmdet - INFO - Epoch [8][20/103] lr: 2.500e-03, eta: 0:12:42, time: 1.575, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0080, loss_rpn_bbox: 0.0523, loss_cls: 0.2126, acc: 90.6543, loss_bbox: 0.2808, loss: 0.5536
2021-10-18 15:07:43,526 - mmdet - INFO - Epoch [8][30/103] lr: 2.500e-03, eta: 0:12:27, time: 1.578, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0421, loss_cls: 0.1987, acc: 91.4160, loss_bbox: 0.2803, loss: 0.5331
2021-10-18 15:07:59,324 - mmdet - INFO - Epoch [8][40/103] lr: 2.500e-03, eta: 0:12:12, time: 1.579, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0097, loss_rpn_bbox: 0.0436, loss_cls: 0.1987, acc: 91.4746, loss_bbox: 0.2670, loss: 0.5191
2021-10-18 15:08:15,074 - mmdet - INFO - Epoch [8][50/103] lr: 2.500e-03, eta: 0:11:56, time: 1.575, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0104, loss_rpn_bbox: 0.0430, loss_cls: 0.2119, acc: 90.8203, loss_bbox: 0.2779, loss: 0.5433
2021-10-18 15:08:30,775 - mmdet - INFO - Epoch [8][60/103] lr: 2.500e-03, eta: 0:11:41, time: 1.569, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0331, loss_cls: 0.1876, acc: 91.7578, loss_bbox: 0.2415, loss: 0.4749
2021-10-18 15:08:46,585 - mmdet - INFO - Epoch [8][70/103] lr: 2.500e-03, eta: 0:11:26, time: 1.582, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0112, loss_rpn_bbox: 0.0361, loss_cls: 0.2252, acc: 89.8438, loss_bbox: 0.2437, loss: 0.5161
2021-10-18 15:09:02,432 - mmdet - INFO - Epoch [8][80/103] lr: 2.500e-03, eta: 0:11:11, time: 1.584, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0099, loss_rpn_bbox: 0.0537, loss_cls: 0.1993, acc: 91.5234, loss_bbox: 0.2850, loss: 0.5480
2021-10-18 15:09:18,211 - mmdet - INFO - Epoch [8][90/103] lr: 2.500e-03, eta: 0:10:56, time: 1.578, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0065, loss_rpn_bbox: 0.0534, loss_cls: 0.1822, acc: 92.0117, loss_bbox: 0.2840, loss: 0.5262
2021-10-18 15:09:33,972 - mmdet - INFO - Epoch [8][100/103] lr: 2.500e-03, eta: 0:10:40, time: 1.576, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0079, loss_rpn_bbox: 0.0499, loss_cls: 0.1804, acc: 92.2559, loss_bbox: 0.2750, loss: 0.5133
2021-10-18 15:09:56,375 - mmdet - INFO - Epoch [9][10/103] lr: 2.500e-04, eta: 0:10:19, time: 1.764, data_time: 0.235, memory: 3163, loss_rpn_cls: 0.0109, loss_rpn_bbox: 0.0461, loss_cls: 0.2038, acc: 91.1133, loss_bbox: 0.2733, loss: 0.5341
2021-10-18 15:10:12,120 - mmdet - INFO - Epoch [9][20/103] lr: 2.500e-04, eta: 0:10:04, time: 1.575, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0114, loss_rpn_bbox: 0.0459, loss_cls: 0.1855, acc: 91.9824, loss_bbox: 0.2692, loss: 0.5121
2021-10-18 15:10:27,837 - mmdet - INFO - Epoch [9][30/103] lr: 2.500e-04, eta: 0:09:48, time: 1.571, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0085, loss_rpn_bbox: 0.0349, loss_cls: 0.1752, acc: 92.5488, loss_bbox: 0.2350, loss: 0.4536
2021-10-18 15:10:43,597 - mmdet - INFO - Epoch [9][40/103] lr: 2.500e-04, eta: 0:09:33, time: 1.576, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0080, loss_rpn_bbox: 0.0415, loss_cls: 0.1967, acc: 91.6895, loss_bbox: 0.2577, loss: 0.5040
2021-10-18 15:10:59,391 - mmdet - INFO - Epoch [9][50/103] lr: 2.500e-04, eta: 0:09:18, time: 1.580, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0082, loss_rpn_bbox: 0.0343, loss_cls: 0.1593, acc: 93.1445, loss_bbox: 0.2243, loss: 0.4261
2021-10-18 15:11:15,188 - mmdet - INFO - Epoch [9][60/103] lr: 2.500e-04, eta: 0:09:03, time: 1.580, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0093, loss_rpn_bbox: 0.0392, loss_cls: 0.1843, acc: 92.0215, loss_bbox: 0.2262, loss: 0.4590
2021-10-18 15:11:30,975 - mmdet - INFO - Epoch [9][70/103] lr: 2.500e-04, eta: 0:08:47, time: 1.578, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0080, loss_rpn_bbox: 0.0394, loss_cls: 0.1610, acc: 93.2422, loss_bbox: 0.2546, loss: 0.4630
2021-10-18 15:11:46,744 - mmdet - INFO - Epoch [9][80/103] lr: 2.500e-04, eta: 0:08:32, time: 1.578, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0070, loss_rpn_bbox: 0.0434, loss_cls: 0.1895, acc: 91.8262, loss_bbox: 0.2544, loss: 0.4944
2021-10-18 15:12:02,500 - mmdet - INFO - Epoch [9][90/103] lr: 2.500e-04, eta: 0:08:17, time: 1.575, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0069, loss_rpn_bbox: 0.0364, loss_cls: 0.1707, acc: 92.8613, loss_bbox: 0.2301, loss: 0.4440
2021-10-18 15:12:18,240 - mmdet - INFO - Epoch [9][100/103] lr: 2.500e-04, eta: 0:08:01, time: 1.574, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0102, loss_rpn_bbox: 0.0396, loss_cls: 0.1664, acc: 92.9297, loss_bbox: 0.2421, loss: 0.4583
2021-10-18 15:12:40,644 - mmdet - INFO - Epoch [10][10/103] lr: 2.500e-04, eta: 0:07:41, time: 1.762, data_time: 0.235, memory: 3163, loss_rpn_cls: 0.0087, loss_rpn_bbox: 0.0417, loss_cls: 0.1784, acc: 92.4121, loss_bbox: 0.2568, loss: 0.4856
2021-10-18 15:12:56,354 - mmdet - INFO - Epoch [10][20/103] lr: 2.500e-04, eta: 0:07:25, time: 1.571, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0081, loss_rpn_bbox: 0.0380, loss_cls: 0.1600, acc: 93.1641, loss_bbox: 0.2438, loss: 0.4498
2021-10-18 15:13:12,156 - mmdet - INFO - Epoch [10][30/103] lr: 2.500e-04, eta: 0:07:10, time: 1.581, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0071, loss_rpn_bbox: 0.0433, loss_cls: 0.1610, acc: 93.0371, loss_bbox: 0.2484, loss: 0.4598
2021-10-18 15:13:27,901 - mmdet - INFO - Epoch [10][40/103] lr: 2.500e-04, eta: 0:06:55, time: 1.575, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0066, loss_rpn_bbox: 0.0392, loss_cls: 0.1850, acc: 91.7676, loss_bbox: 0.2658, loss: 0.4967
2021-10-18 15:13:43,628 - mmdet - INFO - Epoch [10][50/103] lr: 2.500e-04, eta: 0:06:39, time: 1.572, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0082, loss_rpn_bbox: 0.0416, loss_cls: 0.1646, acc: 93.3887, loss_bbox: 0.2515, loss: 0.4659
2021-10-18 15:13:59,433 - mmdet - INFO - Epoch [10][60/103] lr: 2.500e-04, eta: 0:06:24, time: 1.581, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0089, loss_rpn_bbox: 0.0459, loss_cls: 0.1665, acc: 92.9102, loss_bbox: 0.2580, loss: 0.4793
2021-10-18 15:14:15,196 - mmdet - INFO - Epoch [10][70/103] lr: 2.500e-04, eta: 0:06:08, time: 1.577, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0066, loss_rpn_bbox: 0.0378, loss_cls: 0.1633, acc: 93.1055, loss_bbox: 0.2305, loss: 0.4381
2021-10-18 15:14:30,971 - mmdet - INFO - Epoch [10][80/103] lr: 2.500e-04, eta: 0:05:53, time: 1.577, data_time: 0.051, memory: 3163, loss_rpn_cls: 0.0071, loss_rpn_bbox: 0.0348, loss_cls: 0.1681, acc: 92.8906, loss_bbox: 0.2326, loss: 0.4426
2021-10-18 15:14:46,770 - mmdet - INFO - Epoch [10][90/103] lr: 2.500e-04, eta: 0:05:38, time: 1.581, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0106, loss_rpn_bbox: 0.0360, loss_cls: 0.1589, acc: 93.0371, loss_bbox: 0.2178, loss: 0.4232
2021-10-18 15:15:02,550 - mmdet - INFO - Epoch [10][100/103] lr: 2.500e-04, eta: 0:05:22, time: 1.578, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0115, loss_rpn_bbox: 0.0357, loss_cls: 0.1748, acc: 92.5098, loss_bbox: 0.2288, loss: 0.4508
2021-10-18 15:15:24,986 - mmdet - INFO - Epoch [11][10/103] lr: 2.500e-04, eta: 0:05:02, time: 1.768, data_time: 0.235, memory: 3163, loss_rpn_cls: 0.0066, loss_rpn_bbox: 0.0436, loss_cls: 0.1668, acc: 92.7246, loss_bbox: 0.2477, loss: 0.4647
2021-10-18 15:15:40,764 - mmdet - INFO - Epoch [11][20/103] lr: 2.500e-04, eta: 0:04:46, time: 1.577, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0059, loss_rpn_bbox: 0.0340, loss_cls: 0.1574, acc: 93.5645, loss_bbox: 0.2274, loss: 0.4246
2021-10-18 15:15:56,568 - mmdet - INFO - Epoch [11][30/103] lr: 2.500e-04, eta: 0:04:31, time: 1.581, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0080, loss_rpn_bbox: 0.0415, loss_cls: 0.1643, acc: 93.2520, loss_bbox: 0.2272, loss: 0.4409
2021-10-18 15:16:12,338 - mmdet - INFO - Epoch [11][40/103] lr: 2.500e-04, eta: 0:04:16, time: 1.577, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0105, loss_rpn_bbox: 0.0426, loss_cls: 0.1746, acc: 92.7051, loss_bbox: 0.2327, loss: 0.4604
2021-10-18 15:16:28,127 - mmdet - INFO - Epoch [11][50/103] lr: 2.500e-04, eta: 0:04:00, time: 1.579, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0105, loss_rpn_bbox: 0.0357, loss_cls: 0.1502, acc: 93.6719, loss_bbox: 0.2220, loss: 0.4184
2021-10-18 15:16:43,887 - mmdet - INFO - Epoch [11][60/103] lr: 2.500e-04, eta: 0:03:45, time: 1.575, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0074, loss_rpn_bbox: 0.0433, loss_cls: 0.1712, acc: 92.9785, loss_bbox: 0.2520, loss: 0.4739
2021-10-18 15:16:59,694 - mmdet - INFO - Epoch [11][70/103] lr: 2.500e-04, eta: 0:03:30, time: 1.581, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0077, loss_rpn_bbox: 0.0385, loss_cls: 0.1562, acc: 93.4082, loss_bbox: 0.2385, loss: 0.4409
2021-10-18 15:17:15,410 - mmdet - INFO - Epoch [11][80/103] lr: 2.500e-04, eta: 0:03:14, time: 1.572, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0046, loss_rpn_bbox: 0.0372, loss_cls: 0.1741, acc: 92.6953, loss_bbox: 0.2438, loss: 0.4596
2021-10-18 15:17:31,165 - mmdet - INFO - Epoch [11][90/103] lr: 2.500e-04, eta: 0:02:59, time: 1.575, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0058, loss_rpn_bbox: 0.0340, loss_cls: 0.1713, acc: 92.7344, loss_bbox: 0.2241, loss: 0.4351
2021-10-18 15:17:46,930 - mmdet - INFO - Epoch [11][100/103] lr: 2.500e-04, eta: 0:02:43, time: 1.577, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0061, loss_rpn_bbox: 0.0409, loss_cls: 0.1846, acc: 92.2852, loss_bbox: 0.2562, loss: 0.4879
2021-10-18 15:18:09,435 - mmdet - INFO - Epoch [12][10/103] lr: 2.500e-05, eta: 0:02:23, time: 1.772, data_time: 0.239, memory: 3163, loss_rpn_cls: 0.0067, loss_rpn_bbox: 0.0461, loss_cls: 0.1533, acc: 93.8379, loss_bbox: 0.2564, loss: 0.4625
2021-10-18 15:18:25,199 - mmdet - INFO - Epoch [12][20/103] lr: 2.500e-05, eta: 0:02:08, time: 1.576, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0078, loss_rpn_bbox: 0.0337, loss_cls: 0.1636, acc: 92.9395, loss_bbox: 0.2393, loss: 0.4444
2021-10-18 15:18:40,980 - mmdet - INFO - Epoch [12][30/103] lr: 2.500e-05, eta: 0:01:52, time: 1.578, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0074, loss_rpn_bbox: 0.0378, loss_cls: 0.1535, acc: 93.3203, loss_bbox: 0.2322, loss: 0.4308
2021-10-18 15:18:56,750 - mmdet - INFO - Epoch [12][40/103] lr: 2.500e-05, eta: 0:01:37, time: 1.577, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0078, loss_rpn_bbox: 0.0415, loss_cls: 0.1613, acc: 93.3594, loss_bbox: 0.2299, loss: 0.4405
2021-10-18 15:19:12,512 - mmdet - INFO - Epoch [12][50/103] lr: 2.500e-05, eta: 0:01:21, time: 1.575, data_time: 0.048, memory: 3163, loss_rpn_cls: 0.0061, loss_rpn_bbox: 0.0396, loss_cls: 0.1502, acc: 93.3105, loss_bbox: 0.2375, loss: 0.4334
2021-10-18 15:19:28,253 - mmdet - INFO - Epoch [12][60/103] lr: 2.500e-05, eta: 0:01:06, time: 1.575, data_time: 0.050, memory: 3163, loss_rpn_cls: 0.0077, loss_rpn_bbox: 0.0355, loss_cls: 0.1670, acc: 92.8809, loss_bbox: 0.2007, loss: 0.4109
2021-10-18 15:19:43,993 - mmdet - INFO - Epoch [12][70/103] lr: 2.500e-05, eta: 0:00:50, time: 1.574, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0071, loss_rpn_bbox: 0.0359, loss_cls: 0.1480, acc: 93.9160, loss_bbox: 0.2361, loss: 0.4271
2021-10-18 15:19:59,777 - mmdet - INFO - Epoch [12][80/103] lr: 2.500e-05, eta: 0:00:35, time: 1.578, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0109, loss_rpn_bbox: 0.0388, loss_cls: 0.1823, acc: 92.3828, loss_bbox: 0.2493, loss: 0.4813
2021-10-18 15:20:15,526 - mmdet - INFO - Epoch [12][90/103] lr: 2.500e-05, eta: 0:00:20, time: 1.575, data_time: 0.049, memory: 3163, loss_rpn_cls: 0.0038, loss_rpn_bbox: 0.0387, loss_cls: 0.1636, acc: 93.0078, loss_bbox: 0.2283, loss: 0.4344
2021-10-18 15:20:31,262 - mmdet - INFO - Epoch [12][100/103] lr: 2.500e-05, eta: 0:00:04, time: 1.573, data_time: 0.047, memory: 3163, loss_rpn_cls: 0.0065, loss_rpn_bbox: 0.0373, loss_cls: 0.1759, acc: 92.7246, loss_bbox: 0.2317, loss: 0.4514
2021-10-18 15:20:35,682 - mmdet - INFO - Saving checkpoint at 12 epochs
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 87/87, 2.7 task/s, elapsed: 32s, ETA: 0s2021-10-18 15:21:09,102 - mmdet - INFO - Evaluating bbox...
Loading and preparing results...
DONE (t=0.00s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
2021-10-18 15:21:10,170 - mmdet - INFO - Epoch(val) [12][87] bbox_mAP: 0.6290, bbox_mAP_50: 0.8990, bbox_mAP_75: 0.7130, bbox_mAP_s: 0.1520, bbox_mAP_m: 0.4960, bbox_mAP_l: 0.7330, bbox_mAP_copypaste: 0.629 0.899 0.713 0.152 0.496 0.733
DONE (t=0.94s).
Accumulating evaluation results...
DONE (t=0.09s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.629
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.899
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.713
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.152
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.496
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.733
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.714
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.714
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.714
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.179
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.633
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.797
import cv2
from mmdet.apis import inference_detector, init_detector, show_result_pyplot
img = cv2.imread('/content/BCCD_Dataset/BCCD/JPEGImages/BloodImage_00007.jpg')
model.cfg = cfg
result = inference_detector(model, img)
show_result_pyplot(model, img, result)
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