opencv/samples/dnn
2018-03-28 12:57:06 +03:00
..
face_detector Misc. ./samples typos 2018-02-08 05:52:08 -05:00
classification.cpp Semantic segmentation sample. 2018-03-08 11:02:26 +03:00
classification.py Update tutorials. A new cv::dnn::readNet function 2018-03-04 20:30:22 +03:00
CMakeLists.txt cmake: refactored scripts with samples building: 2018-02-12 18:42:36 +03:00
colorization.cpp Minor refactoring in several C++ samples: 2018-03-06 14:23:20 +03:00
colorization.py Merge pull request #10777 from berak:dnn_colorize_cpp 2018-02-05 15:07:40 +03:00
fast_neural_style.py Layers for fast-neural-style models: https://github.com/jcjohnson/fast-neural-style 2017-10-27 14:26:45 +03:00
js_face_recognition.html Test for FP16 version of OpenCV face detection network 2018-02-06 13:16:07 +03:00
mobilenet_ssd_accuracy.py Specific version of MobileNet-SSD from TensorFlow 2017-11-24 13:40:35 +03:00
object_detection.cpp Semantic segmentation sample. 2018-03-08 11:02:26 +03:00
object_detection.py Update tutorials. A new cv::dnn::readNet function 2018-03-04 20:30:22 +03:00
openpose.cpp dnn: add an openpose.cpp sample 2018-03-16 19:36:45 +01:00
openpose.py fixed samples/dnn/openpose.py 2018-03-15 05:17:57 +09:00
README.md Semantic segmentation sample. 2018-03-08 11:02:26 +03:00
segmentation.cpp Semantic segmentation sample. 2018-03-08 11:02:26 +03:00
segmentation.py Semantic segmentation sample. 2018-03-08 11:02:26 +03:00
shrink_tf_graph_weights.py Text TensorFlow graphs parsing. MobileNet-SSD for 90 classes. 2017-10-08 22:25:29 +03:00
tf_text_graph_ssd.py Fix minimal aspect ratio scale for SSDs from TensorFlow 2018-03-28 12:57:06 +03:00

OpenCV deep learning module samples

Model Zoo

Object detection

Model Scale Size WxH Mean subtraction Channels order
MobileNet-SSD, Caffe 0.00784 (2/255) 300x300 127.5 127.5 127.5 BGR
OpenCV face detector 1.0 300x300 104 177 123 BGR
SSDs from TensorFlow 0.00784 (2/255) 300x300 127.5 127.5 127.5 RGB
YOLO 0.00392 (1/255) 416x416 0 0 0 RGB
VGG16-SSD 1.0 300x300 104 117 123 BGR
Faster-RCNN 1.0 800x600 102.9801, 115.9465, 122.7717 BGR
R-FCN 1.0 800x600 102.9801 115.9465 122.7717 BGR

Classification

Model Scale Size WxH Mean subtraction Channels order
GoogLeNet 1.0 224x224 104 117 123 BGR
SqueezeNet 1.0 227x227 0 0 0 BGR

Semantic segmentation

Model Scale Size WxH Mean subtraction Channels order
ENet 0.00392 (1/255) 1024x512 0 0 0 RGB
FCN8s 1.0 500x500 0 0 0 BGR

References