# CondInst

**Repository Path**: SearchSource/CondInst

## Basic Information

- **Project Name**: CondInst
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-04-09
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# CondInst
This repository is an unofficial pytorch implementation of [Conditional Convolutions for Instance Segmentation](https://arxiv.org/abs/2003.05664). The model with ResNet-101 backbone achieves 37.1 mAP on COCO val2017 set.

## Install
The code is based on [detectron2](https://github.com/facebookresearch/detectron2). Please check [Install.md](https://github.com/facebookresearch/detectron2/blob/master/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](https://github.com/Epiphqny/CondInst/issues/1), 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](https://github.com/Epiphqny/CondInst/blob/master/postprocessing.py) 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 

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

<img src="AP.jpg">

## Visualization

<img src="condinst.png" width="2000">