CoCalc -- models.yml

GitHub Repository: Tetragramm/opencv
Path: blob/master/samples/dnn/models.yml
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%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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