!!IMPORTANT!!
- This repository is changed and modified just for my experiment.
- The original code is at https://github.com/ShuangXieIrene/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.
- install pytorch
- install requirements by
pip install -r ./requirements.txt
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
| VOC2007 | YOLO_v2 | YOLO_v3 | SSD | RFB | FSSD |
|---|---|---|---|---|---|
| Darknet53 | 79.3% | 77.3% | 79.5% | 81.0% | |
| Darknet19 | 78.4% | 76.1% | 78.4% | 81.0% | |
| 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% | 21.1% | 22.8% | 25.8% | |
| Darknet19 | 21.6% | 20.6% | 22.4% | 26.4% | |
| 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 | 5.11ms | 4.32ms | 6.63ms | 4.41ms | |
| Darknet19 | 1.64ms | 2.21ms | 4.57ms | 2.29ms | |
| Resnet50 | 3.60ms | 6.04ms | 3.85ms | ||
| VGG16 | 1.75ms | 4.20ms | 1.98ms | ||
| MobilenetV1 | 2.02ms | 3.31ms | 2.80ms | 3.84ms | 2.62ms |
| MobilenetV2 | 3.35ms | 4.69ms | 4.05ms | 5.26ms | 4.00ms |
(*-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.)
-
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? )
- prepare update to pytorch 0.4.0
- 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