SSD.objectdetection.pytorch

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SSD.objectdetection.pytorch

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Library for training and testing object detection for Pytorch (ssd, retinanet)

SSD.objectdetection.pytorch 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# ObjectDetection.Pytorch
![teaser](https://github.com/kentaroy47/ObjectDetection.Pytorch/blob/master/imgs/1.png)

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
~~~~~~~~

SSD.objectdetection.pytorch

SSD.objectdetection.pytorch 최신버젼 다운로드

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