Wolfram Research

YOLOR Trained on MS-COCO Data

Detect and localize objects in an image

YOLO (You Only Learn One Representation) Version R is a family of object detection models published in May 2021. It is characterized by a unified network that can accomplish various tasks, integrating implicit and explicit knowledge by leveraging techniques such as kernel space alignment, prediction refinement and a convolutional neural network with multitask learning. These models achieve comparable object detection accuracy as the Scaled-YOLO Version 4 models while having an inference speed faster by 88%.

Training Set Information

Model Information

Examples

Resource retrieval

Get the pre-trained net:

In[1]:=
NetModel["YOLOR Trained on MS-COCO Data"]
Out[1]=

NetModel parameters

This model consists of a family of individual nets, each identified by a specific parameter combination. Inspect the available parameters:

In[2]:=
NetModel["YOLOR Trained on MS-COCO Data", "ParametersInformation"]
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Pick a non-default net by specifying the parameters:

In[3]:=
NetModel[{"YOLOR Trained on MS-COCO Data", "Architecture" -> "S2D"}]
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Pick a non-default uninitialized net:

In[4]:=
NetModel[{"YOLOR Trained on MS-COCO Data", "Architecture" -> "W6"}, "UninitializedEvaluationNet"]
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Evaluation function

Write an evaluation function to scale the result to the input image size and suppress the least probable detections:

In[5]:=
nonMaximumSuppression = ResourceFunction["NonMaximumSuppression"];
In[6]:=
labels = {"person", "bicycle", "car", "motorcycle", "airplane", "bus",
    "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"};
In[7]:=
netevaluate[model_, img_, detectionThreshold_ : .5, overlapThreshold_ : .5] := Module[{imgSize, classes, coords, obj, scores, bestClass, probable, probableClasses, probableScores, probableBoxes, h, w, max, scale, padding, nms, finals},
   imgSize = Last@NetExtract[model, {"Input", "Output"}];
   {classes, coords, obj} = Values@model[img];
   (*each class probability is rescaled with the box objectness*) scores = classes*obj;
   bestClass = Last@*Ordering /@ scores;
   (*filter by probability*)
   (*very small probability are thresholded*) probable = UnitStep[obj - detectionThreshold]; {probableClasses, probableBoxes, probableScores} = Map[Pick[#, probable, 1] &, {labels[[bestClass]], coords, obj}];
   If[Length[probableBoxes] == 0, Return[{}]];
   (*transform coordinates into rectangular boxes*)
   {w, h} = ImageDimensions[img];
   max = Max[{w, h}];
   scale = max/imgSize ;
   padding = imgSize*(1 - {w, h}/max)/2;
   probableBoxes = Apply[
     Rectangle[
       scale*({#1 - #3/2, imgSize - #2 - #4/2} - padding),
       scale*({#1 + #3/2, imgSize - #2 + #4/2} - padding)
       ] &, probableBoxes, 1];
   (*gather the boxes of the same class and perform non-
   max suppression*) nms = nonMaximumSuppression[probableBoxes -> probableScores, "Index"];
   finals = Transpose[{probableBoxes, probableClasses, probableScores}];
   Part[finals, nms]
   ];

Basic usage

Obtain the detected bounding boxes with their corresponding classes and confidences for a given image:

In[8]:=
net = NetModel["YOLOR Trained on MS-COCO Data"]
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In[9]:=
(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/0309ff3f-1bfe-4013-9df4-e1654085540e"]
In[10]:=
detection = netevaluate[net, testImage];

Inspect which classes are detected:

In[11]:=
classes = DeleteDuplicates@Flatten@detection[[All, 2]]
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Visualize the detection:

In[12]:=
HighlightImage[testImage, GroupBy[detection[[All, ;; 2]], Last -> First]]
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Network result

The network computes 102,200 bounding boxes and the probability that the objects in each box are of any given class:

In[13]:=
res = NetModel["YOLOR Trained on MS-COCO Data"][testImage];
In[14]:=
Dimensions /@ res
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Rescale the bounding boxes to the coordinates of the input image and visualize them scaled by their "objectness" measures:

In[15]:=
rectangles = Block[
   {w, h, max, imgSize, scale, padding},
   {w, h} = ImageDimensions[testImage];
   max = Max[{w, h}];
   imgSize = 1280;
   scale = max/imgSize ;
   padding = imgSize*(1 - {w, h}/max)/2;
   Apply[
    Rectangle[
      scale*({#1 - #3/2, imgSize - #2 - #4/2} - padding),
      scale*({#1 + #3/2, imgSize - #2 + #4/2} - padding)
      ] &,
    res["Boxes"],
    1
    ]
   ];
In[16]:=
Graphics[MapThread[{EdgeForm[Opacity[Total[#1] + .01]], #2} &, {res[
    "Objectness"], rectangles}], BaseStyle -> {FaceForm[], EdgeForm[{Thin, Black}]}]
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Visualize all the boxes scaled by the probability that they contain a cat:

In[17]:=
idx = Position[labels, "cat"][[1, 1]]
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In[18]:=
Graphics[
 MapThread[{EdgeForm[Opacity[#1 + .01]], #2} &, {res["Objectness"]*
    Extract[res["ClassProb"], {All, idx}], rectangles}],
 BaseStyle -> {FaceForm[], EdgeForm[{Thin, Black}]}
 ]
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Superimpose the cat prediction on top of the input received by the net:

In[19]:=
HighlightImage[testImage, Graphics[MapThread[{EdgeForm[{Thickness[#1/100], Opacity[(#1 + .01)/3]}], #2} &, {res["Objectness"]*
     Extract[res["ClassProb"], {All, idx}], rectangles}]], BaseStyle -> {FaceForm[], EdgeForm[{Thin, Red}]}]
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Net information

Inspect the number of parameters of all arrays in the net:

In[20]:=
Information[
 NetModel["YOLOR Trained on MS-COCO Data"], "ArraysElementCounts"]
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Obtain the total number of parameters:

In[21]:=
Information[
 NetModel["YOLOR Trained on MS-COCO Data"], "ArraysTotalElementCount"]
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Obtain the layer type counts:

In[22]:=
Information[
 NetModel["YOLOR Trained on MS-COCO Data"], "LayerTypeCounts"]
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Display the summary graphic:

In[23]:=
Information[
 NetModel["YOLOR Trained on MS-COCO Data"], "SummaryGraphic"]
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Resource History

Reference