%YAML:1.0 ################################################################################ # Object detection models. from opencv.4.1.1 ################################################################################ # OpenCV's face detection network opencv_fd: model: "opencv_face_detector.caffemodel" config: "opencv_face_detector.prototxt" mean: [104, 177, 123] scale: 1.0 width: 300 height: 300 rgb: false sample: "object_detection" # YOLO object detection family from Darknet (https://pjreddie.com/darknet/yolo/) # Might be used for all YOLOv2, TinyYolov2 and YOLOv3 yolo: model: "yolov3.weights" config: "yolov3.cfg" mean: [0, 0, 0] scale: 0.00392 width: 416 height: 416 rgb: true classes: "object_detection_classes_yolov3.txt" sample: "object_detection" tiny-yolo-voc: model: "tiny-yolo-voc.weights" config: "tiny-yolo-voc.cfg" mean: [0, 0, 0] scale: 0.00392 width: 416 height: 416 rgb: true classes: "object_detection_classes_pascal_voc.txt" sample: "object_detection" # Caffe implementation of SSD model from https://github.com/chuanqi305/MobileNet-SSD ssd_caffe: model: "MobileNetSSD_deploy.caffemodel" config: "MobileNetSSD_deploy.prototxt" mean: [127.5, 127.5, 127.5] scale: 0.007843 width: 300 height: 300 rgb: false classes: "object_detection_classes_pascal_voc.txt" sample: "object_detection" # TensorFlow implementation of SSD model from https://github.com/tensorflow/models/tree/master/research/object_detection ssd_tf: model: "ssd_mobilenet_v1_coco_2017_11_17.pb" config: "ssd_mobilenet_v1_coco_2017_11_17.pbtxt" mean: [0, 0, 0] scale: 1.0 width: 300 height: 300 rgb: true classes: "object_detection_classes_coco.txt" sample: "object_detection" # TensorFlow implementation of Faster-RCNN model from https://github.com/tensorflow/models/tree/master/research/object_detection faster_rcnn_tf: model: "faster_rcnn_inception_v2_coco_2018_01_28.pb" config: "faster_rcnn_inception_v2_coco_2018_01_28.pbtxt" mean: [0, 0, 0] scale: 1.0 width: 800 height: 600 rgb: true sample: "object_detection" ################################################################################ # Image classification models. ################################################################################ # SqueezeNet v1.1 from https://github.com/DeepScale/SqueezeNet squeezenet: model: "squeezenet_v1.1.caffemodel" config: "squeezenet_v1.1.prototxt" mean: [0, 0, 0] scale: 1.0 width: 227 height: 227 rgb: false classes: "classification_classes_ILSVRC2012.txt" sample: "classification" # Googlenet from https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet googlenet: model: "bvlc_googlenet.caffemodel" config: "bvlc_googlenet.prototxt" mean: [104, 117, 123] scale: 1.0 width: 224 height: 224 rgb: false classes: "classification_classes_ILSVRC2012.txt" sample: "classification" ################################################################################ # Semantic segmentation models. ################################################################################ # ENet road scene segmentation network from https://github.com/e-lab/ENet-training # Works fine for different input sizes. enet: model: "Enet-model-best.net" mean: [0, 0, 0] scale: 0.00392 width: 512 height: 256 rgb: true classes: "enet-classes.txt" sample: "segmentation" fcn8s: model: "fcn8s-heavy-pascal.caffemodel" config: "fcn8s-heavy-pascal.prototxt" mean: [0, 0, 0] scale: 1.0 width: 500 height: 500 rgb: false sample: "segmentation"