%YAML 1.0
---
################################################################################
# Object detection models.
################################################################################

# YOLOv8 object detection family from ultralytics (https://github.com/ultralytics/ultralytics)
# Might be used for all YOLOv8n YOLOv8s YOLOv8m YOLOv8l and YOLOv8x
yolov8x:
  load_info:
    url: "https://huggingface.co/cabelo/yolov8/resolve/main/yolov8x.onnx?download=true"
    sha1: "462f15d668c046d38e27d3df01fe8142dd004cb4"
  model: "yolov8x.onnx"
  mean: 0.0
  scale: 0.00392
  width: 640
  height: 640
  rgb: true
  labels: "object_detection_classes_yolo.txt"
  postprocessing: "yolov8"
  sample: "object_detection"

yolov8s:
  load_info:
    url: "https://github.com/CVHub520/X-AnyLabeling/releases/download/v0.1.0/yolov8s.onnx"
    sha1: "82cd83984396fe929909ecb58212b0e86d0904b1"
  model: "yolov8s.onnx"
  mean: 0.0
  scale: 0.00392
  width: 640
  height: 640
  rgb: true
  labels: "object_detection_classes_yolo.txt"
  postprocessing: "yolov8"
  sample: "object_detection"

yolov8:
  load_info:
    url: "https://github.com/CVHub520/X-AnyLabeling/releases/download/v0.1.0/yolov8n.onnx"
    sha1: "68f864475d06e2ec4037181052739f268eeac38d"
  model: "yolov8n.onnx"
  mean: 0.0
  scale: 0.00392
  width: 640
  height: 640
  rgb: true
  labels: "object_detection_classes_yolo.txt"
  postprocessing: "yolov8"
  sample: "object_detection"

yolov8m:
  load_info:
    url: "https://github.com/CVHub520/X-AnyLabeling/releases/download/v0.1.0/yolov8m.onnx"
    sha1: "656ffeb4f3b067bc30df956728b5f9c61a4cb090"
  model: "yolov8m.onnx"
  mean: 0.0
  scale: 0.00392
  width: 640
  height: 640
  rgb: true
  labels: "object_detection_classes_yolo.txt"
  postprocessing: "yolov8"
  sample: "object_detection"

yolov8l:
  load_info:
    url: "https://github.com/CVHub520/X-AnyLabeling/releases/download/v0.1.0/yolov8l.onnx"
    sha1: "462df53ca3a85d110bf6be7fc2e2bb1277124395"
  model: "yolov8l.onnx"
  mean: 0.0
  scale: 0.00392
  width: 640
  height: 640
  rgb: true
  labels: "object_detection_classes_yolo.txt"
  postprocessing: "yolov8"
  sample: "object_detection"

# YOLOv5 object detection family from ultralytics (https://github.com/ultralytics/ultralytics)
# Might be used for all YOLOv5n YOLOv5s YOLOv5m YOLOv5l and YOLOv5x

yolov5l:
  load_info:
    url: "https://github.com/CVHub520/X-AnyLabeling/releases/download/v0.1.0/yolov5l.onnx"
    sha1: "9de7e54c524b7fe7577bbd4cdbbdaed53375c8f1"
  model: "yolov5l.onnx"
  mean: 0.0
  scale: 0.00392
  width: 640
  height: 640
  rgb: true
  classes: "object_detection_classes_yolo.txt"
  background_label_id: 0
  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
yolov4:
  load_info:
    url: "https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights"
    sha1: "0143deb6c46fcc7f74dd35bf3c14edc3784e99ee"
  model: "yolov4.weights"
  config_load_info:
    url: "https://github.com/opencv/opencv_extra/raw/refs/heads/4.x/testdata/dnn/yolov4.cfg"
    sha1: "ed0aeace88527af7524c3baf66ca44fbf049b878"
  config: "yolov4.cfg"
  mean: [0, 0, 0]
  scale: 0.00392
  width: 416
  height: 416
  rgb: true
  labels: "object_detection_classes_yolo.txt"
  background_label_id: 0
  sample: "object_detection"

yolov4-tiny:
  load_info:
    url: "https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights"
    sha1: "451caaab22fb9831aa1a5ee9b5ba74a35ffa5dcb"
  model: "yolov4-tiny.weights"
  config_load_info:
    url: "https://github.com/opencv/opencv_extra/raw/refs/heads/4.x/testdata/dnn/yolov4-tiny-2020-12.cfg"
    sha1: "b161c2b0984b0c3b466c04b0d6cb3e52f06d93dd"
  config: "yolov4-tiny-2020-12.cfg"
  mean: [0, 0, 0]
  scale: 0.00392
  width: 416
  height: 416
  rgb: true
  labels: "object_detection_classes_yolo.txt"
  background_label_id: 0
  sample: "object_detection"

yolov3:
  load_info:
    url: "https://pjreddie.com/media/files/yolov3.weights"
    sha1: "520878f12e97cf820529daea502acca380f1cb8e"
  model: "yolov3.weights"
  config_load_info:
    url: "https://github.com/opencv/opencv_extra/raw/refs/heads/4.x/testdata/dnn/yolov3.cfg"
    sha1: "caaf16a895b7bae3cd5c042199d1df0269f3dce6"
  config: "yolov3.cfg"
  mean: [0, 0, 0]
  scale: 0.00392
  width: 416
  height: 416
  rgb: true
  labels: "object_detection_classes_yolo.txt"
  background_label_id: 0
  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_load_info:
    url: "https://github.com/opencv/opencv_extra/raw/refs/heads/4.x/testdata/dnn/tiny-yolo-voc.cfg"
    sha1: "d26e2408ce4e20136278411760ba904d744fe5b5"
  config: "tiny-yolo-voc.cfg"
  mean: [0, 0, 0]
  scale: 0.00392
  width: 416
  height: 416
  rgb: true
  labels: "object_detection_classes_pascal_voc.txt"
  background_label_id: 0
  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
  labels: "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_load_info:
    url: "https://github.com/opencv/opencv_extra/raw/refs/heads/4.x/testdata/dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt"
    sha1: "c7cf985ce0a4a8953daaa4b8cacdd3c8e31437a6"
  config: "ssd_mobilenet_v1_coco_2017_11_17.pbtxt"
  mean: [0, 0, 0]
  scale: 1.0
  width: 300
  height: 300
  rgb: true
  labels: "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_load_info:
    url: "https://github.com/opencv/opencv_extra/raw/refs/heads/4.x/testdata/dnn/faster_rcnn_inception_v2_coco_2018_01_28.pbtxt"
    sha1: "059ee437fb4d6f82a6f1d2b3c7a8dd54c107687f"
  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:
  load_info:
    url: "https://github.com/onnx/models/raw/main/validated/vision/classification/squeezenet/model/squeezenet1.1-7.onnx?download="
    sha1: "ec31942d17715941bb9b81f3a91dc59def9236be"
  model: "squeezenet1.1-7.onnx"
  mean: [0.485, 0.456, 0.406]
  std: [0.229, 0.224, 0.225]
  scale: 0.003921
  width: 224
  height: 224
  rgb: true
  labels: "classification_classes_ILSVRC2012.txt"
  sample: "classification"

googlenet:
  load_info:
    url: "https://github.com/onnx/models/raw/69c5d3751dda5349fd3fc53f525395d180420c07/vision/classification/inception_and_googlenet/googlenet/model/googlenet-8.onnx"
    sha1: "da39a3ee5e6b4b0d3255bfef95601890afd80709"
  model: "googlenet-8.onnx"
  mean: [103.939, 116.779, 123.675]
  std: [1, 1, 1]
  scale: 1.0
  width: 224
  height: 224
  rgb: false
  labels: "classification_classes_ILSVRC2012.txt"
  sample: "classification"

resnet:
  load_info:
    url: "https://github.com/onnx/models/raw/main/validated/vision/classification/resnet/model/resnet50-v2-7.onnx"
    sha1: "c3a67b3cb2f0a61a7eb75eb8bd9139c89557cbe0"
  model: "resnet50-v2-7.onnx"
  mean: [123.675, 116.28, 103.53]
  std: [58.395, 57.12, 57.375]
  scale: 1.0
  width: 224
  height: 224
  rgb: true
  labels: "classification_classes_ILSVRC2012.txt"
  sample: "classification"

################################################################################
# Semantic segmentation models.
################################################################################

fcnresnet50:
  load_info:
    url: "https://github.com/onnx/models/raw/491ce05590abb7551d7fae43c067c060eeb575a6/validated/vision/object_detection_segmentation/fcn/model/fcn-resnet50-12.onnx"
    sha1: "1bb0c7e0034038969aecc6251166f1612a139230"
  model: "fcn-resnet50-12.onnx"
  mean: [103.5, 116.2, 123.6]
  scale: 0.019
  width: 500
  height: 500
  rgb: false
  sample: "segmentation"

fcnresnet101:
  load_info:
    url: "https://github.com/onnx/models/raw/fb8271d5d5d9b90dbb1eb5e8e40f8f580fb248b3/vision/object_detection_segmentation/fcn/model/fcn-resnet101-11.onnx"
    sha1: "e7e76474bf6b73334ab32c4be1374c9e605f5aed"
  model: "fcn-resnet101-11.onnx"
  mean: [103.5, 116.2, 123.6]
  scale: 0.019
  width: 500
  height: 500
  rgb: false
  sample: "segmentation"

u2netp:
  load_info:
    url: "https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2netp.onnx"
    sha1: "0a99236f0d5c1916a99a8c401b23e5ef32038606"
  model: "u2netp.onnx"
  mean: [123.6, 116.2, 103.5]
  scale: 0.019
  width: 320
  height: 320
  rgb: true
  sample: "segmentation"

################################################################################
# Text detection models.
################################################################################

DB:
  load_info:
    url: "https://drive.google.com/uc?export=dowload&id=17_ABp79PlFt9yPCxSaarVc_DKTmrSGGf"
    sha1: "bef233c28947ef6ec8c663d20a2b326302421fa3"
  model: "DB_IC15_resnet50.onnx"
  ocr_load_info:
    ocr_url: "https://drive.google.com/uc?export=dowload&id=159VavnbvfBQkLIPSAu2SP5Yij1Fy4azw"
    ocr_sha1: "c4ab1fb3f13c1c8ffc04f016e72ec85311de4ebe"
  ocr_model: "VGG_CTC.onnx"
  mean: [122.67891434, 116.66876762, 104.00698793]
  scale: 0.00392
  width: 736
  height: 736
  rgb: false
  sample: "text_detection"

East:
  load_info:
    url: "https://www.dropbox.com/s/r2ingd0l3zt8hxs/frozen_east_text_detection.tar.gz?dl=1"
    sha1: "fffabf5ac36f37bddf68e34e84b45f5c4247ed06"
    download_name: "frozen_east_text_detection.tar.gz"
    download_sha: "3ca8233d6edd748f7ed23246c8ca24cbf696bb94"
  model: "frozen_east_text_detection.pb"
  ocr_load_info:
    ocr_url: "https://drive.google.com/uc?export=dowload&id=159VavnbvfBQkLIPSAu2SP5Yij1Fy4azw"
    ocr_sha1: "c4ab1fb3f13c1c8ffc04f016e72ec85311de4ebe"
  ocr_model: "VGG_CTC.onnx"
  mean: [123.68, 116.78, 103.94]
  scale: 1.0
  width: 736
  height: 736
  rgb: false
  sample: "text_detection"

OCR:
  load_info:
    url: "https://drive.google.com/uc?export=dowload&id=159VavnbvfBQkLIPSAu2SP5Yij1Fy4azw"
    sha1: "c4ab1fb3f13c1c8ffc04f016e72ec85311de4ebe"
  model: "VGG_CTC.onnx"
  sample: "text_recognition"

# Edge Detection models.
################################################################################

dexined:
  load_info:
    url: "https://github.com/gursimarsingh/opencv_zoo/raw/dexined_model/models/edge_detection_dexined/dexined.onnx"
    sha1: "f86f2d32c3cf892771f76b5e6b629b16a66510e9"
  model: "dexined.onnx"
  mean: [103.5, 116.2, 123.6]
  scale: 1.0
  width: 512
  height: 512
  rgb: false
  sample: "edge_detection"