Dynamic anchor: A Feature-Guided Anchor Strategy for Object Detection

we achieve RetinaNet with dynamic anchor based on mmdetection

Introduction

Code details of dynamic anchor

Installation

This DA-RetinaNet implementation is based on FCOS and mmdetection and the installation is the same as mmdetection. Please check INSTALL.md for installation instructions.

Inference

Once the installation is done, you can download DA-RetinaNet_r50.pth from Google or Baidu. The following command line will inference on coco minival split and please replace root with your root directory at first (for example: my root directory is "/home/yht/Demo1/mmdetection/") :

python tools/test.py root/configs/DA-RetinaNet/DA-RetinaNet_r50_caffe_fpn_4x4_1x_coco.py root/weights/DA-RetinaNet_r50_FPN_1x.pth  --eval bbox  --show-dir root/show_results/DA-RetinaNet_r50/

Please note that:

1. Config file path and weight file path are best to use absolute paths.
2. --show-dir represents saving painted images with detection results.
3. --eval represents evaluating performance.

Training

The following command line will train DA-RetinaNet_r50_FPN_1x on 2 GPUs with Synchronous Stochastic Gradient Descent (SGD) and please replace the root with your root directory at first (for example: my root directory is "/home/yht/Demo1/mmdetection/") :

python tools/dist_train.sh  root/configs/DA-RetinaNet/DA-RetinaNet_r50_caffe_fpn_4x4_1x_coco.py  2

Results and Models

we provide the following trained models. The AP All models are trained with 8 images in a mini-batch on 2 RTX 3090 GPUs. The AP is evaluated on coco test_dev split.

Method	Backbone	Style	Lr schd	box AP	GFLPs	log	Download
DA-RetinaNet	R-50-FPN	caffe	1x	38.0	141.79	log/key:2ahy	weight/key:w787
DA-RetinaNet	R-101-FPN	caffe	1x	40.0	217.86	log/key:sp8a	weight/key:b4ay
DA-RetinaNet	R-50-FPN	caffe	2x		141.79
DA-RetinaNet	R-101-FPN	caffe	2x		217.86