Notes!!

MMDetection-annotations have been update to latest version 1.0. I'll continue finish the remaining part and may not chase after upgrades in the future (present version is good enough)

mmdetection-annotated

Introduction

Refer to the execllent implemention here:https://github.com/open-mmlab/mmdetection ,and thanks to author Kai Chen.
Open-mmlab project , which contains various models and implementions of latest papers , achieves great results in detection/segmentataion tasks , and is kind enough for rookies in CV field.

Getting started

More information about installation or pre-train model downloads , pls refer to officia mmdetection or blog here

• Test on images
  You can test on Faster RCNN demo by running the script demo.py. I have just rewritten the demo file to detect on single image or a folder as follow:

import os
from mmdet.apis import init_detector, inference_detector, show_result

if __name__ == '__main__':
	config_file = 'configs/faster_rcnn_r50_fpn_1x.py'
	checkpoint_file = 'weights/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth'
	# checkpoint_file = 'tools/work_dirs/mask_rcnn_r101_fpn_1x/epoch_1200.pth'
	img_path = '/home/bit/下载/n07753592'
	model = init_detector(config_file, checkpoint_file, device='cuda:0')
	# print(model)
	# 输入可以为文件夹或者图片
	if os.path.isdir(img_path):
		imgs= os.listdir(img_path)
		for i in range(len(imgs)):
			imgs[i]=os.path.join(img_path,imgs[i])
		for i, result in enumerate(inference_detector(model, imgs)):	# 支持可迭代输入imgs
			print(i, imgs[i])
			show_result(imgs[i], result, model.CLASSES, out_file='output/result_{}.jpg'.format(i))

	elif os.path.isfile(img_path):
		result = inference_detector(model, img_path)
		show_result(img_path, result, model.CLASSES)

• Debug
  You can debug by setting breakpoint with method of adding ipdb.set_trace().Before that , make sure of the success installment and import of ipdb package.
• Hook
  If you want to inspect on intermediate variables , hook.py can be a provision served as a reference for your work.

Annotations

Annotations are attached everywhere in the code(surely only the part I have read , and the not finished part will be completed as soon as possible). Beside , annotation folder contains some interpreting documents as well.

• Dataset Example
  Provide a simple small sample data set for testing (segmentation && detection) .More details referrd to instruction here

• CUDA related code
  I've delete files in folder mmdet/ops cause no annotations attached inside.However it's a good news that specific notes are made about RoIAlign here .

• Model visualization
  Take Mask-RCNN for example , the model can be visualized as follow:(more details refere to model-structure-png)

• Configuration
  Explicit describtion on config file , take Mask RCNN for example , refer to mask_rcnn_r101_fpn_1x.py

• MMCV&MMDET
  Specification of mmcv lib and a partial of mmdet(more details about various models will be updated later ).
  

Detection Results

Test on Mask RCNN model:

Training

dataset

• You can just use COCO dataset , refer here.
  
• If you want to train on your customed dataset labeled by labelme , you need first convert json files to COCO style , this toolbox may help you ;
  
• If you want to train on your customed dataset labeled by labelImg , you need first convert xml files to COCO style , this toolbox may also help you .
  
• I have tested on these tools recently to make sure them still work well, if questiones still arised , desrcibe on issue please or contact me , thanks.
  

learning rate

Remember to set lr in config file according to your own GPU_NUM !!!!(eg.1/8 of default lr for 1 GPU)

Future work

Mmdetection performs better than many classical implementions , it's really a excellent work , can be called as ‘Chinese Detectron’ :p . I will update this project with annotations for more details in the future, letting more people make a good use of this great work.You can continue to foucus on this repo.
BTW , this repo is just used for better comprehension , if you ask for better performance or latest paper implementions ,please keep eyes on mmdetection