anminhhung / ssd_pytorch

SSD, FSSD, RFB, etc with Pytorch

Bring code from https://github.com/ShuangXieIrene/ssds.pytorch Made some custom Changes

ssds.pytorch

Repository for Single Shot MultiBox Detector and its variants, implemented with pytorch, python3.

Currently, it contains these features:

• Multiple SSD Variants: ssd, rfb, fssd, ssd-lite, rfb-lite, fssd-lite
• Multiple Base Network: VGG, Mobilenet V1/V2
• Free Image Size
• Visualization with tensorboard-pytorch: training loss, eval loss/mAP, example archor boxs.

This repo is depended on the work of ssd.pytorch, faster-rcnn.pytorch, RFBNet, Detectron and Tensorflow Object Detection API. Thanks for there works.

Table of Contents

• Installation
• Usage
• Performance and Model Zoo
• Visualization
• Future Work
• Reference

Installation

1. install pytorch
2. install requirements by pip install -r ./requirements.txt

Usage

To train, test and demo some specific model. Please run the relative file in folder with the model configure file, like:

python train.py --cfg=./experiments/cfgs/rfb_lite_mobilenetv2_train_voc.yml

Change the configure file based on the note in config_parse.py

Performance

VOC2007	YOLO_v2	YOLO_v3	SSD	RFB	FSSD
Darknet53		79.3%
Darknet19	78.4%
Resnet50			79.7%	81.2%
VGG16			76.0%	80.5%	77.8%
MobilenetV1	74.7%	78.2%	72.7%	73.7%	78.4%
MobilenetV2	72.0%	75.8%	73.2%	73.4%	76.7%

COCO2017	YOLO_v2	YOLO_v3	SSD	RFB	FSSD
Darknet53		27.3%
Darknet19	21.6%
Resnet50			25.1%	26.5%	27.2%
VGG16			25.4%	25.5%	27.2%
MobilenetV1	21.5%	25.7%	18.8%	19.1%	24.2%
MobilenetV2	20.4%	24.0%	18.5%	18.5%	22.2%

Net InferTime* (fp32)	YOLO_v2	YOLO_v3	SSD	RFB	FSSD
Darknet53		5.6ms
Darknet19	1.9ms
Resnet50
VGG16			1.78ms	4.20ms	1.98ms
MobilenetV1		3.8ms	2.87ms	3.84ms	2.62ms
MobilenetV2		5.1ms	4.18ms	5.28ms	4.02ms

(*-only calculate the all network inference time, without pre-processing & post-processing. In fact, the speed of vgg is super impress me. Maybe it is caused by MobilenetV1 and MobilenetV2 is using -lite structure, which uses the seperate conv in the base and extra layers.)

Visualization

• visualize the network graph (terminal) -tensorboard has bugs.

• visualize the loss during the training progress and meanAP during the eval progress (terminal & tensorboard)

• visualize archor box for each feature extractor (tensorboard)

• visualize the preprocess steps for training (tensorboard)

• visualize the pr curve for different classes and anchor matching strategy for different properties during evaluation (tensorboard) (*guess the dataset in the figure, coco or voc?)

• visualize featuremap and grads (not satisfy me, does not give me any information. any suggestions? )

TODO

• add DSSDs: DSSD FPN TDM
• test the multi-resolution traning
• add rotation for prerprocess
• test focal loss
• add resnet, xception, inception
• figure out the problem of visualize graph
• speed up preprocess part (any suggestion?)
• speed up postprocess part (any suggestion?) huge bug!!!

• add network visualization based on pytorch-cnn-visualizations
• object detection

Reference