train your custom dataset

[songyangsun]

you can follow :
https://yuanwenjie.blog.csdn.net/article/details/123592452
and
https://blog.csdn.net/qq_37134388/article/details/115625744

I wanna share two things:
1.in train.py, for example, solov2_r50_fpn_8gpu_3x.py
model->pretrained and load_from are different, model->pretrained is the pretrained weights of backbone, for example, resnet.
url path: torchvision://resnet50.
load_from is pretrained weights of SOLO, for example, SOLOv2_R50_3x.pth which can be downloaded at SOLO readme.

2.Label error. mistake is :
if your class is ('A', 'B', 'C'), when you use test_ins_vis.py to get the result, you find you can get 'B' instead of 'A'(but the real label is 'A').
get 'C' instead of 'B'(but the real label is 'B'), but 'C' can't be predicted.

I use

and

as labels. then i get the good performance.(bbox_head->num_class=5 in train.py)
if you meet this problem, you can try this.

[hiiro0420]

if you get this label error, your train data may not have 'background'.
you may use CLASSES = ('A','B','C') or you add 'background' class in train data.