%YAML 1.0 --- ################################################################################ # Object detection models. ################################################################################ # OpenCV's face detection network opencv_fd: load_info: url: "https://github.com/opencv/opencv_3rdparty/raw/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel" sha1: "15aa726b4d46d9f023526d85537db81cbc8dd566" 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" # YOLO4 object detection family from Darknet (https://github.com/AlexeyAB/darknet) # YOLO object detection family from Darknet (https://pjreddie.com/darknet/yolo/) # Might be used for all YOLOv2, TinyYolov2, YOLOv3, YOLOv4 and TinyYolov4 yolo: load_info: url: "https://pjreddie.com/media/files/yolov3.weights" sha1: "520878f12e97cf820529daea502acca380f1cb8e" 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: load_info: url: "https://pjreddie.com/media/files/yolov2-tiny-voc.weights" sha1: "24b4bd049fc4fa5f5e95f684a8967e65c625dff9" 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: load_info: url: "https://drive.google.com/uc?export=download&id=0B3gersZ2cHIxRm5PMWRoTkdHdHc" sha1: "994d30a8afaa9e754d17d2373b2d62a7dfbaaf7a" 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: load_info: url: "http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2017_11_17.tar.gz" sha1: "9e4bcdd98f4c6572747679e4ce570de4f03a70e2" download_sha: "6157ddb6da55db2da89dd561eceb7f944928e317" download_name: "ssd_mobilenet_v1_coco_2017_11_17.tar.gz" member: "ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb" 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: load_info: url: "http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_v2_coco_2018_01_28.tar.gz" sha1: "f2e4bf386b9bb3e25ddfcbbd382c20f417e444f3" download_sha: "c710f25e5c6a3ce85fe793d5bf266d581ab1c230" download_name: "faster_rcnn_inception_v2_coco_2018_01_28.tar.gz" member: "faster_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb" 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: load_info: url: "https://raw.githubusercontent.com/DeepScale/SqueezeNet/b5c3f1a23713c8b3fd7b801d229f6b04c64374a5/SqueezeNet_v1.1/squeezenet_v1.1.caffemodel" sha1: "3397f026368a45ae236403ccc81cfcbe8ebe1bd0" 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: load_info: url: "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel" sha1: "405fc5acd08a3bb12de8ee5e23a96bec22f08204" 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: load_info: url: "https://www.dropbox.com/s/tdde0mawbi5dugq/Enet-model-best.net?dl=1" sha1: "b4123a73bf464b9ebe9cfc4ab9c2d5c72b161315" 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: load_info: url: "http://dl.caffe.berkeleyvision.org/fcn8s-heavy-pascal.caffemodel" sha1: "c449ea74dd7d83751d1357d6a8c323fcf4038962" 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"