Pipeline based on Open-MMLAB MM-Detection project - https://github.com/open-mmlab/mmdetection

• mask_rcnn_r50_fpn
• mask_rcnn_r101_fpn
• mask_rcnn_x101_32x4d_fpn
• mask_rcnn_x101_32x4d_fpn
• cascade_mask_rcnn_r50_fpn
• cascade_mask_rcnn_r101_fpn
• cascade_mask_rcnn_r101_32x4d_fpn_coco
• cascade_mask_rcnn_r101_64x4d_fpn_coco

Supports

• Python 3.6
• Cuda 9.0, 10.0 (Other cuda version support is experimental)

cd installation

chmod +x install.sh && ./install.sh

• Load Dataset

gtf.Train_Dataset(img_dir, annofile, class_file);

gtf.Dataset_Params(batch_size=2, num_workers=2);

• Load Model

gtf.Model_Params(model_name="faster_rcnn_x101_64x4d_fpn");

• Set Hyper Parameters

gtf.Hyper_Params(lr=0.02, momentum=0.9, weight_decay=0.0001);

gtf.Training_Params(num_epochs=2, val_interval=1);

• Train

gtf.Train();

• Add support for Coco-Type Annotated Datasets
• Add support for VOC-Type Annotated Dataset
• Test on Kaggle and Colab
• Add validation feature & data pipeline
• Add Optimizer selection feature
• Enable Learning-Rate Scheduler Support
• Enable Layer Freezing
• Set Verbosity Levels
• Add Project management and version control support (Similar to Monk Classification)
• Add Graph Visualization Support
• Enable batch proessing at inference
• Add feature for top-k output visualization
• Add Multi-GPU training
• Auto correct missing or corrupt images - Currently skips them
• Add Experimental Data Analysis Feature