Project Details

Pipeline based on EfficientDet project - https://github.com/yhenon/pytorch-retinanet

Supported Models

• Resnet18
• Resnet34
• Resnet50
• Resnet101
• Resnet152

Installation

Supports

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

cd installation

cat requirements.txt | xargs -n 1 -L 1 pip install

Functional Documentation

Link

Pipeline

• Load Dataset

gtf.Train_Dataset(root_dir="../sample_dataset", coco_dir="kangaroo", img_dir="images", set_dir="Train", batch_size=8, image_size=512, use_gpu=True)

• Load Model

gtf.Model(model_name="resnet18");

• Set Hyper Parameters

gtf.Set_Hyperparams(lr=0.0001, val_interval=1, es_min_delta=0.0, es_patience=0)

• Train

gtf.Train(num_epochs=2, output_model_name="trained.pt");

TODO

• 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

External Contributors list

• https://github.com/THEFASHIONGEEK: Multi GPU feature