https://github.com/kentaroy47/ssd.objectdetection.pytorch
Library for training and testing object detection for Pytorch (ssd, retinanet)
https://github.com/kentaroy47/ssd.objectdetection.pytorch
fpn inference objectdetection pytorch retinanet ssd train-ssd yolo
Last synced: 8 months ago
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Library for training and testing object detection for Pytorch (ssd, retinanet)
- Host: GitHub
- URL: https://github.com/kentaroy47/ssd.objectdetection.pytorch
- Owner: kentaroy47
- License: mit
- Created: 2019-09-10T02:28:38.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-12-02T23:53:23.000Z (almost 6 years ago)
- Last Synced: 2025-01-20T23:52:38.650Z (9 months ago)
- Topics: fpn, inference, objectdetection, pytorch, retinanet, ssd, train-ssd, yolo
- Language: Jupyter Notebook
- Homepage:
- Size: 95.3 MB
- Stars: 8
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# ObjectDetection.Pytorch
This repo is an object detection library for pytorch (single stage detectors).
**RetinaNet Architecture**
VOCmAP: train VOC07+12 test VOC07
|*backbone*|*resolution*|*VOCmAP* |*COCOmAP*|*Inference[ms]*|*model*|
|:------:|:------------:|:----------:|:-------:|:-------------:|:-----:|
|VGG16 |300 |79.5 | | |[here](https://github.com/kentaroy47/ObjectDetection.Pytorch/releases/download/ssdvgg200/ssd300_200.pth)|
|resnet18|300 |76.5 | | | |
|resnet50|300 |80.5 | | | |
|resnet101|300 | | | | |
|resnet18|600 | | | | |
|resnet50|600 | | | | |
|resnet101|600 | | | | |
## To start off
requirements: cv2, pandas. plz install.
clone the repo.
```
git clone https://github.com/kentaroy47/ObjectDetection.Pytorch.git
```
Download PASCALVOC2007 dataset and extract.
```
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
```
Download reduced FC vgg weights and place in weights folder.
```
mkdir weights
cd weights
wget https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
```
## Train SSD Models
run train_ssd.ipynb
to run inference, try inference.ipynb.
the trained SSD model is here (still underfitting..)
https://github.com/kentaroy47/ObjectDetection.Pytorch/releases/download/ssdvgg200/ssd300_200.pth
## Train YOLO Models
run yolo.ipynb (TBD)
## Train Faster RCNN Models
run frcnn.ipynb (TBD)
## Test models
run `eval.ipynb`
# Test results for ssd
Pascal VOC 2007 test set.
### SSD-300
Model:
https://github.com/kentaroy47/ObjectDetection.Pytorch/releases/download/ssdvgg200/ssd300_200.pth
Mean AP = 0.7959
~~~~~~~~
Results:
0.842
0.850
0.784
0.736
0.518
0.891
0.888
0.902
0.634
0.832
0.793
0.873
0.899
0.862
0.815
0.521
0.798
0.815
0.885
0.780
0.796
~~~~~~~~
### RetinaNet-300 Resnet18
Mean AP = 0.7279
~~~~~~~~
Results:
0.759
0.814
0.725
0.661
0.373
0.807
0.836
0.847
0.508
0.759
0.741
0.816
0.848
0.813
0.743
0.420
0.695
0.803
0.859
0.731
0.728
~~~~~~~~