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# OpenCV deep learning module samples
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## Model Zoo
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### Object detection
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| Model | Scale | Size WxH| Mean subtraction | Channels order |
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|---------------|-------|-----------|--------------------|-------|
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| [MobileNet-SSD, Caffe](https://github.com/chuanqi305/MobileNet-SSD/) | `0.00784 (2/255)` | `300x300` | `127.5 127.5 127.5` | BGR |
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| [OpenCV face detector](https://github.com/opencv/opencv/tree/master/samples/dnn/face_detector) | `1.0` | `300x300` | `104 177 123` | BGR |
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| [SSDs from TensorFlow](https://github.com/tensorflow/models/tree/master/research/object_detection/) | `0.00784 (2/255)` | `300x300` | `127.5 127.5 127.5` | RGB |
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| [YOLO](https://pjreddie.com/darknet/yolo/) | `0.00392 (1/255)` | `416x416` | `0 0 0` | RGB |
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| [VGG16-SSD](https://github.com/weiliu89/caffe/tree/ssd) | `1.0` | `300x300` | `104 117 123` | BGR |
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| [Faster-RCNN](https://github.com/rbgirshick/py-faster-rcnn) | `1.0` | `800x600` | `102.9801 115.9465 122.7717` | BGR |
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| [R-FCN](https://github.com/YuwenXiong/py-R-FCN) | `1.0` | `800x600` | `102.9801 115.9465 122.7717` | BGR |
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| [Faster-RCNN, ResNet backbone](https://github.com/tensorflow/models/tree/master/research/object_detection/) | `1.0` | `300x300` | `103.939 116.779 123.68` | RGB |
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| [Faster-RCNN, InceptionV2 backbone](https://github.com/tensorflow/models/tree/master/research/object_detection/) | `0.00784 (2/255)` | `300x300` | `127.5 127.5 127.5` | RGB |
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#### Face detection
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[An origin model](https://github.com/opencv/opencv/tree/master/samples/dnn/face_detector)
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with single precision floating point weights has been quantized using [TensorFlow framework](https://www.tensorflow.org/).
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To achieve the best accuracy run the model on BGR images resized to `300x300` applying mean subtraction
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of values `(104, 177, 123)` for each blue, green and red channels correspondingly.
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The following are accuracy metrics obtained using [COCO object detection evaluation
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tool](http://cocodataset.org/#detections-eval) on [FDDB dataset](http://vis-www.cs.umass.edu/fddb/)
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(see [script](https://github.com/opencv/opencv/blob/master/modules/dnn/misc/face_detector_accuracy.py))
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applying resize to `300x300` and keeping an origin images' sizes.
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```
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AP - Average Precision | FP32/FP16 | UINT8 | FP32/FP16 | UINT8 |
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AR - Average Recall | 300x300 | 300x300 | any size | any size |
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--------------------------------------------------|-----------|----------------|-----------|----------------|
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AP @[ IoU=0.50:0.95 | area= all | maxDets=100 ] | 0.408 | 0.408 | 0.378 | 0.328 (-0.050) |
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AP @[ IoU=0.50 | area= all | maxDets=100 ] | 0.849 | 0.849 | 0.797 | 0.790 (-0.007) |
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AP @[ IoU=0.75 | area= all | maxDets=100 ] | 0.251 | 0.251 | 0.208 | 0.140 (-0.068) |
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AP @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.050 | 0.051 (+0.001) | 0.107 | 0.070 (-0.037) |
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AP @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.381 | 0.379 (-0.002) | 0.380 | 0.368 (-0.012) |
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AP @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.455 | 0.455 | 0.412 | 0.337 (-0.075) |
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AR @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] | 0.299 | 0.299 | 0.279 | 0.246 (-0.033) |
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AR @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] | 0.482 | 0.482 | 0.476 | 0.436 (-0.040) |
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AR @[ IoU=0.50:0.95 | area= all | maxDets=100 ] | 0.496 | 0.496 | 0.491 | 0.451 (-0.040) |
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AR @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.189 | 0.193 (+0.004) | 0.284 | 0.232 (-0.052) |
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AR @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.481 | 0.480 (-0.001) | 0.470 | 0.458 (-0.012) |
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AR @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.528 | 0.528 | 0.520 | 0.462 (-0.058) |
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```
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### Classification
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| Model | Scale | Size WxH| Mean subtraction | Channels order |
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|---------------|-------|-----------|--------------------|-------|
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| GoogLeNet | `1.0` | `224x224` | `104 117 123` | BGR |
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| [SqueezeNet](https://github.com/DeepScale/SqueezeNet) | `1.0` | `227x227` | `0 0 0` | BGR |
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### Semantic segmentation
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| Model | Scale | Size WxH| Mean subtraction | Channels order |
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|---------------|-------|-----------|--------------------|-------|
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| [ENet](https://github.com/e-lab/ENet-training) | `0.00392 (1/255)` | `1024x512` | `0 0 0` | RGB |
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| FCN8s | `1.0` | `500x500` | `0 0 0` | BGR |
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## References
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* [Models downloading script](https://github.com/opencv/opencv_extra/blob/master/testdata/dnn/download_models.py)
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* [Configuration files adopted for OpenCV](https://github.com/opencv/opencv_extra/tree/master/testdata/dnn)
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* [How to import models from TensorFlow Object Detection API](https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API)
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* [Names of classes from different datasets](https://github.com/opencv/opencv/tree/master/samples/data/dnn)
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