Got low accuracy results for Scannet per-point prediction
sylyt62 opened this issue · comments
I followed ScanNet Segmentation instructions, but got bad prediction results.
Firstly, I downloaded scene0000_00 from training set and scene0707_00 from test set, ran python tools/scannet.py --run process_scannet --path_in <scannet_folder>
to prepare the data, and ceated my own filelist.txt
Secondly, changed config file (based on seg_scannet_eval.yaml
, only changed in and out dir) and ran python segmentation.py --config configs/seg_custom_eval.yaml
, using weights 00600.model.pth
Finally, converted the predictions to segmentation labels.
No errors jumped out, and got results in logs folder. But after I attach the predicted labels to ply file, seems like it can only distinguish walls and floor.
Did I miss something?
Could you forward the network on the validation dataset and get a mIoU around 74.0?