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Remove preprocessing parameters from README

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Dmitry Kurtaev 2019-04-16 13:10:31 +03:00
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samples/dnn/README.md
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@ -2,19 +2,22 @@
## Model Zoo ## Model Zoo
### Object detection Check [a wiki](https://github.com/opencv/opencv/wiki/Deep-Learning-in-OpenCV) for a list of tested models.
| Model | Scale | Size WxH| Mean subtraction | Channels order | If OpenCV is built with [Intel's Inference Engine support](https://github.com/opencv/opencv/wiki/Intel%27s-Deep-Learning-Inference-Engine-backend) you can use [Intel's pre-trained](https://github.com/opencv/open_model_zoo) models.
|---------------|-------|-----------|--------------------|-------|
| [MobileNet-SSD, Caffe](https://github.com/chuanqi305/MobileNet-SSD/) | `0.00784 (2/255)` | `300x300` | `127.5 127.5 127.5` | BGR | There are different preprocessing parameters such mean subtraction or scale factors for different models.
| [OpenCV face detector](https://github.com/opencv/opencv/tree/3.4/samples/dnn/face_detector) | `1.0` | `300x300` | `104 177 123` | BGR | You may check the most popular models and their parameters at [models.yml](https://github.com/opencv/opencv/blob/master/samples/dnn/models.yml) configuration file. It might be also used for aliasing samples parameters. In example,
| [SSDs from TensorFlow](https://github.com/tensorflow/models/tree/master/research/object_detection/) | `0.00784 (2/255)` | `300x300` | `127.5 127.5 127.5` | RGB |
| [YOLO](https://pjreddie.com/darknet/yolo/) | `0.00392 (1/255)` | `416x416` | `0 0 0` | RGB | ```bash
| [VGG16-SSD](https://github.com/weiliu89/caffe/tree/ssd) | `1.0` | `300x300` | `104 117 123` | BGR | python object_detection.py opencv_fd --model /path/to/caffemodel --config /path/to/prototxt
| [Faster-RCNN](https://github.com/rbgirshick/py-faster-rcnn) | `1.0` | `800x600` | `102.9801 115.9465 122.7717` | BGR | ```
| [R-FCN](https://github.com/YuwenXiong/py-R-FCN) | `1.0` | `800x600` | `102.9801 115.9465 122.7717` | BGR |
| [Faster-RCNN, ResNet backbone](https://github.com/tensorflow/models/tree/master/research/object_detection/) | `1.0` | `300x300` | `103.939 116.779 123.68` | RGB | Check `-h` option to know which values are used by default:
| [Faster-RCNN, InceptionV2 backbone](https://github.com/tensorflow/models/tree/master/research/object_detection/) | `0.00784 (2/255)` | `300x300` | `127.5 127.5 127.5` | RGB |
```bash
python object_detection.py opencv_fd -h
```
#### Face detection #### Face detection
[An origin model](https://github.com/opencv/opencv/tree/3.4/samples/dnn/face_detector) [An origin model](https://github.com/opencv/opencv/tree/3.4/samples/dnn/face_detector)
@ -44,18 +47,6 @@ AR @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.481 | 0.480 (-0.001) |
AR @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.528 | 0.528 | 0.520 | 0.462 (-0.058) | AR @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.528 | 0.528 | 0.520 | 0.462 (-0.058) |
``` ```
### Classification
| Model | Scale | Size WxH| Mean subtraction | Channels order |
|---------------|-------|-----------|--------------------|-------|
| GoogLeNet | `1.0` | `224x224` | `104 117 123` | BGR |
| [SqueezeNet](https://github.com/DeepScale/SqueezeNet) | `1.0` | `227x227` | `0 0 0` | BGR |
### Semantic segmentation
| Model | Scale | Size WxH| Mean subtraction | Channels order |
|---------------|-------|-----------|--------------------|-------|
| [ENet](https://github.com/e-lab/ENet-training) | `0.00392 (1/255)` | `1024x512` | `0 0 0` | RGB |
| FCN8s | `1.0` | `500x500` | `0 0 0` | BGR |
## References ## References
* [Models downloading script](https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/download_models.py) * [Models downloading script](https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/download_models.py)
* [Configuration files adopted for OpenCV](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn) * [Configuration files adopted for OpenCV](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn)