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History

Alexander Alekhin 9ba9358ecb documentation: avoid links to 'master' branch from 3.4 maintenance branch		2018-05-31 16:45:18 +03:00
..
face_detector	Merge pull request #11631 from sparkecho:3.4	2018-05-31 07:23:19 +00:00
classification.cpp	Update Intel's Inference Engine deep learning backend (#11587 )	2018-05-31 14:05:21 +03:00
classification.py	Update Intel's Inference Engine deep learning backend (#11587 )	2018-05-31 14:05:21 +03:00
CMakeLists.txt	Update links to OpenCV's face detection network	2018-04-02 13:02:56 +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
custom_layers.hpp	EAST: An Efficient and Accurate Scene Text Detector (https://arxiv.org/abs/1704.03155v2 )	2018-05-11 14:55:42 +03:00
edge_detection.py	Custom deep learning layers in Python	2018-04-26 09:25:18 +03:00
fast_neural_style.py	print() is a function in Python 3	2018-05-03 07:12:12 +02:00
js_face_recognition.html	Update links to OpenCV's face detection network	2018-04-02 13:02:56 +03:00
mobilenet_ssd_accuracy.py	print() is a function in Python 3	2018-05-03 07:12:12 +02:00
object_detection.cpp	Update Intel's Inference Engine deep learning backend (#11587 )	2018-05-31 14:05:21 +03:00
object_detection.py	Update Intel's Inference Engine deep learning backend (#11587 )	2018-05-31 14:05:21 +03:00
openpose.cpp	select the device (video capture)	2018-05-09 17:20:02 +03:00
openpose.py	fixed samples/dnn/openpose.py	2018-03-15 05:17:57 +09:00
README.md	documentation: avoid links to 'master' branch from 3.4 maintenance branch	2018-05-31 16:45:18 +03:00
segmentation.cpp	Update Intel's Inference Engine deep learning backend (#11587 )	2018-05-31 14:05:21 +03:00
segmentation.py	Update Intel's Inference Engine deep learning backend (#11587 )	2018-05-31 14:05:21 +03:00
shrink_tf_graph_weights.py	Text TensorFlow graphs parsing. MobileNet-SSD for 90 classes.	2017-10-08 22:25:29 +03:00
text_detection.cpp	Return a convex hull from rotatedRectangleIntersection	2018-05-18 14:20:17 +03:00
tf_text_graph_faster_rcnn.py	Faster-RCNN object detection models from TensorFlow	2018-05-30 17:12:36 +03:00
tf_text_graph_ssd.py	Fix batch normalization fusion from TensorFlow's SSDs	2018-05-23 16:49:31 +03:00

README.md

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
Faster-RCNN, ResNet backbone	`1.0`	`300x300`	`103.939 116.779 123.68`	RGB
Faster-RCNN, InceptionV2 backbone	`0.00784 (2/255)`	`300x300`	`127.5 127.5 127.5`	RGB

Face detection

An origin model with single precision floating point weights has been quantized using TensorFlow framework. To achieve the best accuracy run the model on BGR images resized to 300x300 applying mean subtraction of values (104, 177, 123) for each blue, green and red channels correspondingly.

The following are accuracy metrics obtained using COCO object detection evaluation tool on FDDB dataset (see script) applying resize to 300x300 and keeping an origin images' sizes.

AP - Average Precision                            | FP32/FP16 | UINT8          | FP32/FP16 | UINT8          |
AR - Average Recall                               | 300x300   | 300x300        | any size  | any size       |
--------------------------------------------------|-----------|----------------|-----------|----------------|
AP @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] | 0.408     | 0.408          | 0.378     | 0.328 (-0.050) |
AP @[ IoU=0.50      | area=   all | maxDets=100 ] | 0.849     | 0.849          | 0.797     | 0.790 (-0.007) |
AP @[ IoU=0.75      | area=   all | maxDets=100 ] | 0.251     | 0.251          | 0.208     | 0.140 (-0.068) |
AP @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.050     | 0.051 (+0.001) | 0.107     | 0.070 (-0.037) |
AP @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.381     | 0.379 (-0.002) | 0.380     | 0.368 (-0.012) |
AP @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.455     | 0.455          | 0.412     | 0.337 (-0.075) |
AR @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] | 0.299     | 0.299          | 0.279     | 0.246 (-0.033) |
AR @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] | 0.482     | 0.482          | 0.476     | 0.436 (-0.040) |
AR @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] | 0.496     | 0.496          | 0.491     | 0.451 (-0.040) |
AR @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.189     | 0.193 (+0.004) | 0.284     | 0.232 (-0.052) |
AR @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.481     | 0.480 (-0.001) | 0.470     | 0.458 (-0.012) |
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	`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