Project Details

Pipeline based on SlimYoloV3 project - https://github.com/PengyiZhang/SlimYOLOv3 Base Yolov3 code from - https://github.com/erikguo/yolov3

Supported Models

• YoloV3-SPP3

Installation

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

Pipeline

• Load Dataset

gtf.set_train_dataset(img_dir, label_dir, class_list_file, batch_size=2, img_size=608)

• Set Hyper Params

gtf.set_hyperparams(optimizer="sgd", lr=0.00579, multi_scale=False, evolve=True, num_generations=2);

• Train

gtf.Train(num_epochs=2);

• Reload Model, prune and retrain

gtf.prune_weights("yolov3-spp3.cfg", "weights/last.pt", "pruned1.cfg", "pruned1.pt"1);

gtf.Train(num_epochs=2, finetune=True)

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