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Open Source Computer Vision Library https://opencv.org/
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opencv/samples/dnn
Dmitry Kurtaev dc9e6d3af8
Update a script to generate text graphs for Faster-RCNN networks from TensorFlow 		6 years ago
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face_detector Update face detection network in samples 6 years ago
CMakeLists.txt Install data for samples to correct directories, do not download face_detector models in cmake 6 years ago
README.md
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classification.cpp Add a file with preprocessing parameters for deep learning networks 6 years ago
classification.py Add a file with preprocessing parameters for deep learning networks 6 years ago
colorization.cpp
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colorization.py
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common.hpp dnn/samples: handle not set env vars gracefully 6 years ago
common.py Add a file with preprocessing parameters for deep learning networks 6 years ago
custom_layers.hpp Merge pull request #12264 from dkurt:dnn_remove_forward_method 6 years ago
edge_detection.py
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fast_neural_style.py
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mobilenet_ssd_accuracy.py
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models.yml Add a file with preprocessing parameters for deep learning networks 6 years ago
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openpose.py
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segmentation.cpp Add a file with preprocessing parameters for deep learning networks 6 years ago
segmentation.py Add a file with preprocessing parameters for deep learning networks 6 years ago
shrink_tf_graph_weights.py
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text_detection.cpp core: repair CV_Assert() messages 6 years ago
tf_text_graph_common.py Add a file with preprocessing parameters for deep learning networks 6 years ago
tf_text_graph_faster_rcnn.py Update a script to generate text graphs for Faster-RCNN networks from TensorFlow 6 years ago
tf_text_graph_mask_rcnn.py Import tensorflow to create text graphs if import cv is failed 6 years ago
tf_text_graph_ssd.py Import tensorflow to create text graphs if import cv is failed 6 years ago

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

References