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Dmitry Kurtaev f96f934426 Update Intel's Inference Engine deep learning backend (#11587) 
* Update Intel's Inference Engine deep learning backend

* Remove cpu_extension dependency

* Update Darknet accuracy tests
2018-05-31 14:05:21 +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 Update links to OpenCV's face detection network 2018-04-02 13:02:56 +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_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

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

References