Epiphqny / CondInst

Conditional Convolutions for Instance Segmentation, achives 37.1mAP on coco val

CondInst

This repository is an unofficial pytorch implementation of Conditional Convolutions for Instance Segmentation. The model with ResNet-101 backbone achieves 37.1 mAP on COCO val2017 set.

Install

The code is based on detectron2. Please check Install.md for installation instructions.

Training

Follows the same way as detectron2.

Single GPU:

python train_net.py --config-file configs/CondInst/MS_R_101_3x.yaml

Multi GPU(for example 8):

python train_net.py --num-gpus 8 --config-file configs/CondInst/MS_R_101_3x.yaml

Please adjust the IMS_PER_BATCH in the config file according to the GPU memory.

Notes

I have replaced the original upsample with the aligned upsample according to the author's issue, and use the upsampled mask to calculate loss, this brings more gains but may cost more GPU memory, if you do not have much memory, use the original unupsampled version to calculate loss.

Inference

First replace the original detectron2 installed postprocessing.py with the file in this repository, as the original file only suit for ROI obatined masks. The path should be like /miniconda3/envs/py37/lib/python3.7/site-packages/detectron2/modeling/postprocessing.py

Single GPU:

python train_net.py --config-file configs/CondInst/MS_R_101_3x.yaml --eval-only MODEL.WEIGHTS /path/to/checkpoint_file

Multi GPU(for example 8):

python train_net.py --num-gpus 8 --config-file configs/CondInst/MS_R_101_3x.yaml --eval-only MODEL.WEIGHTS /path/to/checkpoint_file

Weights

Trained model can be download in Google drive

Results

After training 36 epochs on the coco dataset using the resnet-101 backbone, the mAP is 0.371 on COCO val2017 dataset:

Visualization