Project Details

Pipeline based on SAC_Object_Detection project Journal: Evolving Systems DOI: 10.1007/s12530-025-09749-y Title: Scale-invariant object detection by adaptive convolution with unified global-local context

Supported Models

• efficientdet-d0.pth
• efficientdet-d1.pth
• efficientdet-d2.pth
• efficientdet-d3.pth
• efficientdet-d4.pth
• efficientdet-d5.pth
• efficientdet-d6.pth
• efficientdet-d7.pth

Supported Models

• EfficientDet-d1+SAC.pth
• EfficientDet-d2+SAC.pth
• EfficientDet_d1+SAC+Feature.pth
• EfficientDet_d2+SAC+Feature.pth
• EfficientDet_d1+SAC+Global+Feature.pth
• EfficientDet_d2+SAC+Global+Feature.pth

Installation

Supports

• Python 3.9
• Cuda 9.0, 11.0 (Other cuda version support is experimental)

cd installation

cat requirements_cuda9.0.txt | xargs -n 1 -L 1 pip install

Functional Documentation

Pipeline

• Load Dataset

gtf.set_train_dataset(root_dir, coco_dir, img_dir, set_dir, classes_list=[ ], batch_size=2, num_workers=3)

• Load Model

gtf.set_model(model_name="efficientdet-d3.pth", num_gpus=1, freeze_head=False);

• Set Hyper Parameters

gtf.set_hyperparams(optimizer="adamw", lr=0.00001, es_min_delta=0.0, es_patience=0)

• Train

gtf.train(num_epochs=5, val_interval=1, save_interval=1)