# Yolact_fcos

**Repository Path**: IOEvan/Yolact_fcos

## Basic Information

- **Project Name**: Yolact_fcos
- **Description**: YOLACT: Real-time Instance Segmentation on the FCOS detector (without bbox cropping), achives 35.2mAP on coco val
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-06-12
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Yolact_fcos
This repository implements [YOLACT: Real-time Instance Segmentation](https://arxiv.org/abs/1904.02689) on the [FCOS: Fully Convolutional One-Stage Object Detection](https://arxiv.org/abs/1904.01355)
detector. The model with ResNet-101 backbone achieves 35.2 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/Yolact/MS_R_101_3x.yaml
```
Multi GPU(for example 8):
```
python train_net.py --num-gpus 8 --config-file configs/Yolact/MS_R_101_3x.yaml
```
Please adjust the IMS_PER_BATCH in the config file according to the GPU memory.

## Notes
Different from the original YOLACT, The repository performs instance segmentation without ROI operations or any box cropping operations, it directly obtains the masks in the whole image size.

## Inference
First replace the original detectron2 installed postprocessing.py with the [file](https://github.com/Epiphqny/Yolact_fcos/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/Yolact/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/Yolact/MS_R_101_3x.yaml --eval-only MODEL.WEIGHTS /path/to/checkpoint_file
```

## Weights
Trained model can be download in https://drive.google.com/file/d/1TtkMFtZhacsWVaMQvNtYHhcVxY8T2o8A/view?usp=sharing

## Results
After training 36 epochs on the coco dataset using the resnet-101 backbone, the mAP is 0.352 on COCO val2017 dataset:

<img src="AP.jpg">

## Visualization

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