A small example package
Project description
Project Details
Pipeline based on GluonCV Fintuning project - https://gluon-cv.mxnet.io/build/examples_detection/index.html
Installation
Supports
- Python 3.6
- Python 3.7
cd installation
Check the cuda version using the command
nvcc -V
Select the right requirements file and run
cat <selected requirements file> | xargs -n 1 -L 1 pip install
For example for cuda 9.0
cat requirements_cuda9.0.txt | xargs -n 1 -L 1 pip install
Functional Documentation
Pipeline
- Load Dataset
gtf.Dataset(root_dir, img_dir, anno_file, batch_size=batch_size);
- Load Model
gtf.Model(model_name, use_pretrained=pretrained, use_gpu=gpu);
- Set Hyper-parameter
gtf.Set_Learning_Rate(0.001);
- Train
gtf.Train(epochs, params_file);
TODO
- Add SSD support
- Add YoloV3 support
- Add support for Coco-Type Annotated Datasets
- Add support for VOC-Type Annotated Dataset
- Add Faster-RCNN support
- 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
External Contributors list
- https://github.com/THEFASHIONGEEK: Multi GPU feature
Project details
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