MMDetection-annotations have been update to latest version 1.0. I'll continue updating but may not chase after upgrades for latest version.
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.
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 scriptdemo.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 addingipdb.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.pycan be a provision served as a reference for your work.
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) -
notes
-
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 ).
Test on Mask RCNN model:
- 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.
Remember to set lr in config file according to your own GPU_NUM !!!!(eg.1/8 of default lr for 1 GPU)
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