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This is implementation of the paper Yolact:《YOLACT: Real-time Instance Segmentation》.
The vision community has rapidly improved object detection and semantic segmentation results over a short period of time. In large part, these advances have been driven by powerful baseline systems, such as the Fast/Faster RCNN and Fully Convolutional Network (FCN) frameworks for object detection and semantic segmentation, respectively. For Instance Segmentation , State-ofthe-art approaches like Mask RCNN and FCIS directly build off of advances in object detection like Faster R-CNN and R-FCN . Yet, these methods focus primarily on performance over speed, leaving the scene devoid of instance segmentation parallels to real-time object detectors like SSD and YOLO. YOLACT fill that gap with a fast, one-stage instance segmentation model in the same way that SSD and YOLO fill that gap for object detection.
The original model used Resnet-101 as backbone and was trained on MS-COCO Dataset of 80 classes. But due to GPU limitations, I have trained my model using PASCAL VOC dataset which contains 3000 images of 20 classes. As my model was predicting 20 classes instead of 80, I used Resnet-50 as backbone to compensate. After training for 111 epochs on Tesla K80 GPU it managed to achieve fairly good results.
You can download the Pascal VOC dataset here or using this link
Feel free to download my trained weights using this drive link.
