Pipeline based on Pytorch RetinaFace project - https://github.com/biubug6/Pytorch_Retinaface
- mobilenet
- resnet
Supports
- Python 3.6
- Cuda 9.0, 10.0 (Other cuda version support is experimental)
cd installation
cat requirements.txt | xargs -n 1 -L 1 pip install
- Load Dataset
gtf.Train_Dataset(img_dir, anno_file);
- Dataset params
gtf.Dataset_Params(batch_size=32, num_workers=4);
- Load Model
gtf.Model_Params(model_type="mobilenet", use_gpu=True, resume_from=None);
- Set Hyper Parameters
gtf.Hyper_Parameters(lr=0.0001, momentum=0.9, weight_decay=0.0005, gamma=0.1);
gtf.Training_Params(num_epochs=20, output_dir="weights_trained");
- 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