SOLOV2 training and teaching

Posted by gillypogi on Fri, 25 Feb 2022 13:06:47 +0100

Github: https://github.com/WXinlong/SOLO
My operating system is Ubuntu 18 04. This paper will be divided into the following parts:

Create dataset
Modify config
model training
Visual Mask results
Model evaluation
Inference prediction
Reasoning optimization

First, install some basic libraries. Please refer to the official installation instructions

$ git clone https://github.com/WXinlong/SOLO.git
$ cd SOLO
$ pip install -r requirements/build.txt
$ pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI"
$ pip install -v -e .

1. Create dataset

Prepare the training data set, label the data set with labelme and convert it to coco format. For labeling operations, refer to: Semantic segmentation and instance segmentation Labelme annotation Teaching
Establish a training set and a verification set, as shown below
Create custom data in mmdet/datasets. Establish my_dataset.py, write the training category. If there is only one tag, add another null category.

from .coco import CocoDataset
from .registry import DATASETS
@DATASETS.register_module
class MyDataset(CocoDataset):
    CLASSES = ['null', 'raccoon']

In mmdet/datasets/__init__.py add the data set just defined

2. Modify config

Select the model architecture to be trained, and all models are located in the configs folder. I use the lightest model of SOLOv2, and then open configurations / SOLOv2 / SOLOv2_ light_ 448_ r18_ fpn_ 8gpu_ 3x. Py modify the following:

num_classes is set to the number of categories + 1
Change dataset_type and data_root path
Change the ANN of train, val and test in data = dict()_ file,img_prefix path
optimizer, learning rate and total can be modified_ Epochs and other super parameters, where work_dir is the path where the training model is stored.

3. Model training

Next, let's start model training...

python tools/train.py configs/solov2/solov2_light_448_r18_fpn_8gpu_3x.py

If multiple GPUs are used (in case of 8 GPUs), the following instructions can be used:

./tools/dist_train.sh configs/solov2/solov2_light_448_r18_fpn_8gpu_3x.py  8

After training, the trained model will be placed in work_ Under the folder path set by dir.

4. Visual Mask results

Modify tools / test_ ins_ vis. Class in PY_ Names is a custom category

python tools/test_ins_vis.py configs/solov2/solov2_light_448_r18_fpn_8gpu_3x.py weights/solov2_light_release_r18_fpn_8gpu_3x/latest.pth --show --save_dir  work_dirs/vis_solo

Visual Mask results will be stored in work_ dirs/vis_ In the solo folder

5. Model evaluation

Evaluation model AP
--The eval parameter allows you to select which AP to display

python tools/test_ins.py configs/solov2/solov2_light_448_r18_fpn_8gpu_3x.py weights/solov2_light_release_r18_fpn_8gpu_3x/latest.pth --show --out results_solo.pkl --eval segm

If you want to display AP of each category, you can set mmdet / core / evaluation / coco_ utils. After classwise in py is changed to True, execute the command again

python tools/test_ins.py configs/solov2/solov2_light_448_r18_fpn_8gpu_3x.py weights/solov2_light_release_r18_fpn_8gpu_3x/latest.pth --show --out results_solo.pkl --eval segm