mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
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117 lines
3.2 KiB
117 lines
3.2 KiB
%YAML:1.0 |
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################################################################################ |
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# Object detection models. |
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################################################################################ |
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# OpenCV's face detection network |
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opencv_fd: |
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model: "opencv_face_detector.caffemodel" |
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config: "opencv_face_detector.prototxt" |
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mean: [104, 177, 123] |
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scale: 1.0 |
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width: 300 |
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height: 300 |
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rgb: false |
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sample: "object_detection" |
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# YOLO object detection family from Darknet (https://pjreddie.com/darknet/yolo/) |
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# Might be used for all YOLOv2, TinyYolov2 and YOLOv3 |
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yolo: |
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model: "yolov3.weights" |
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config: "yolov3.cfg" |
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mean: [0, 0, 0] |
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scale: 0.00392 |
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width: 416 |
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height: 416 |
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rgb: true |
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classes: "object_detection_classes_yolov3.txt" |
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sample: "object_detection" |
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tiny-yolo-voc: |
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model: "tiny-yolo-voc.weights" |
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config: "tiny-yolo-voc.cfg" |
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mean: [0, 0, 0] |
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scale: 0.00392 |
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width: 416 |
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height: 416 |
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rgb: true |
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classes: "object_detection_classes_pascal_voc.txt" |
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sample: "object_detection" |
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# Caffe implementation of SSD model from https://github.com/chuanqi305/MobileNet-SSD |
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ssd_caffe: |
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model: "MobileNetSSD_deploy.caffemodel" |
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config: "MobileNetSSD_deploy.prototxt" |
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mean: [127.5, 127.5, 127.5] |
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scale: 0.007843 |
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width: 300 |
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height: 300 |
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rgb: false |
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classes: "object_detection_classes_pascal_voc.txt" |
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sample: "object_detection" |
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# TensorFlow implementation of SSD model from https://github.com/tensorflow/models/tree/master/research/object_detection |
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ssd_tf: |
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model: "ssd_mobilenet_v1_coco_2017_11_17.pb" |
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config: "ssd_mobilenet_v1_coco_2017_11_17.pbtxt" |
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mean: [0, 0, 0] |
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scale: 1.0 |
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width: 300 |
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height: 300 |
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rgb: true |
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classes: "object_detection_classes_coco.txt" |
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sample: "object_detection" |
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# TensorFlow implementation of Faster-RCNN model from https://github.com/tensorflow/models/tree/master/research/object_detection |
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faster_rcnn_tf: |
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model: "faster_rcnn_inception_v2_coco_2018_01_28.pb" |
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config: "faster_rcnn_inception_v2_coco_2018_01_28.pbtxt" |
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mean: [0, 0, 0] |
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scale: 1.0 |
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width: 800 |
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height: 600 |
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rgb: true |
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sample: "object_detection" |
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################################################################################ |
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# Image classification models. |
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################################################################################ |
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# SqueezeNet v1.1 from https://github.com/DeepScale/SqueezeNet |
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squeezenet: |
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model: "squeezenet_v1.1.caffemodel" |
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config: "squeezenet_v1.1.prototxt" |
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mean: [0, 0, 0] |
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scale: 1.0 |
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width: 227 |
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height: 227 |
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rgb: false |
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classes: "classification_classes_ILSVRC2012.txt" |
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sample: "classification" |
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################################################################################ |
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# Semantic segmentation models. |
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################################################################################ |
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# ENet road scene segmentation network from https://github.com/e-lab/ENet-training |
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# Works fine for different input sizes. |
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enet: |
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model: "Enet-model-best.net" |
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mean: [0, 0, 0] |
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scale: 0.00392 |
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width: 512 |
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height: 256 |
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rgb: true |
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classes: "enet-classes.txt" |
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sample: "segmentation" |
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fcn8s: |
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model: "fcn8s-heavy-pascal.caffemodel" |
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config: "fcn8s-heavy-pascal.prototxt" |
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mean: [0, 0, 0] |
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scale: 1.0 |
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width: 500 |
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height: 500 |
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rgb: false |
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sample: "segmentation"
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