History

Alexander Alekhin fcec053d59 Merge remote-tracking branch 'upstream/3.4' into merge-3.4		2019-02-05 19:12:41 +03:00
..
face_detector
classification.cpp	Add a file with preprocessing parameters for deep learning networks	2018-09-25 18:28:37 +03:00
classification.py	Add a file with preprocessing parameters for deep learning networks	2018-09-25 18:28:37 +03:00
CMakeLists.txt
colorization.cpp	samples: use findFile() in dnn	2018-11-16 18:08:22 +00:00
colorization.py
common.hpp	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2018-10-26 17:56:55 +03:00
common.py	samples: use findFile() in dnn	2018-11-16 18:08:22 +00:00
custom_layers.hpp	Merge pull request #12264 from dkurt:dnn_remove_forward_method	2018-09-06 13:26:47 +03:00
edge_detection.py	Fix edge_detection.py sample for Python 3	2019-01-09 15:28:10 +03:00
fast_neural_style.py	samples: use findFile() in dnn	2018-11-16 18:08:22 +00:00
js_face_recognition.html
mask_rcnn.py	samples: use findFile() in dnn	2018-11-16 18:08:22 +00:00
mobilenet_ssd_accuracy.py	samples: use findFile() in dnn	2018-11-16 18:08:22 +00:00
models.yml	dnn/samples: add googlenet to model zoo	2019-01-17 12:14:35 +01:00
object_detection.cpp	Update obj. detect sample	2019-01-30 16:44:42 -01:00
object_detection.py	Update obj. detect sample	2019-01-30 16:44:42 -01:00
openpose.cpp	Fix openpose samples	2018-12-25 14:12:44 -01:00
openpose.py	Fix openpose samples	2018-12-25 14:12:44 -01:00
README.md
segmentation.cpp	Merge remote-tracking branch 'upstream/3.4' into merge-3.4	2018-10-13 16:19:05 +00:00
segmentation.py	Add a file with preprocessing parameters for deep learning networks	2018-09-25 18:28:37 +03:00
shrink_tf_graph_weights.py
text_detection.cpp
text_detection.py	Merge pull request #13432 from vishwesh5:patch-1	2018-12-18 13:40:04 +03:00
tf_text_graph_common.py	Align TensorFlow and OpenCV paths to create a text graph	2019-01-31 13:40:54 +03:00
tf_text_graph_faster_rcnn.py	Create text graphs for Faster-RCNN from TensorFlow with dilated convolutions	2019-01-18 18:46:52 +03:00
tf_text_graph_mask_rcnn.py	SSD with FPN proposals from TensorFlow	2019-01-11 12:20:03 +03:00
tf_text_graph_ssd.py	SSD with FPN proposals from TensorFlow	2019-01-11 12:20:03 +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