vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

modules/dnn_objdetect/README.md

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+# Object Detection using Convolutional Neural Networks
+
+This module uses Convolutional Neural Networks for detecting objects in an image
+
+## Dependencies
+- opencv dnn module
+- Google Protobuf
+
+## Building this module
+
+Run the following command to build this module:
+
+```make
+cmake -DOPENCV_EXTRA_MODULES_PATH=<opencv_contrib>/modules -Dopencv_dnn_objdetect=ON <opencv_source_dir>
+```
+
+## Models
+
+There are two models which are trained.
+
+#### SqueezeNet model trained for Image Classification.
+
+- This model was trained for 1500000 iterations with a batch size of 16
+- Size of Model: 4.9MB
+- Top-1 Accuracy on ImageNet 2012 DataSet: 56.10%
+- Top-5 Accuracy on ImageNet 2012 DataSet: 79.54%
+- Link to trained weights: [here](https://github.com/kvmanohar22/caffe/blob/obj_detect_loss/proto/SqueezeNet.caffemodel) ([copy](https://github.com/opencv/opencv_3rdparty/tree/dnn_objdetect_20170827))
+
+#### SqueezeDet model trained for Object Detection
+
+- This model was trained for 180000 iterations with a batch size of 16
+- Size of the Model: 14.2MB
+- Link to the trained weights: [here](https://github.com/kvmanohar22/caffe/blob/obj_detect_loss/proto/SqueezeDet.caffemodel) ([copy](https://github.com/opencv/opencv_3rdparty/tree/dnn_objdetect_20170827))
+
+## Usage
+
+#### With Caffe
+
+For details pertaining to the usage of the model, have a look at [this repository](https://github.com/kvmanohar22/caffe)
+
+You can infact train your own object detection models with the loss function which is implemented.
+
+#### Without Caffe, using `opencv's dnn module`
+`tutorials/core_detect.cpp` gives an example of how to use the model to predict the bounding boxes.
+`tutorials/image_classification.cpp` gives an example of how to use the model to classify an image.
+
+Here's the brief summary of examples. For detailed usage and testing, refer `tutorials` directory.
+
+## Examples:
+
+### Image Classification
+
+```c++
+// Read the net along with it's trained weights
+cv::dnn::Net net = cv::dnn::readNet(model_weights, model_defn);
+
+// Read an image
+cv::Mat image = cv::imread(image_file);
+
+// Convert the image into blob
+cv::Mat image_blob = cv::net::blobFromImage(image);
+
+// Get the output of "predictions" layer
+cv::Mat probs = net.forward("predictions");
+
+```
+`probs` is a 4-d tensor of shape `[1, 1000, 1, 1]` which is obtained after the application of `softmax` activation.
+
+### Object Detection
+
+```c++
+// Reading the network and weights, converting image to blob is same as Image Classification example.
+
+// Forward through the network and collect blob data
+cv::Mat delta_bboxs = net.forward("slice")[0];
+cv::Mat conf_scores = net.forward("softmax");
+cv::Mat class_scores = net.forward("sigmoid");
+```
+Three blobs aka `delta_bbox`, `conf_scores`, `class_scores` are post-processed in `cv::dnn_objdetect::InferBbox` class and the bounding boxes predicted.
+
+```c++
+InferBbox infer(delta_bbox, class_scores, conf_scores);
+infer.filter();
+```
+
+`infer.filter()` returns vector of `cv::dnn_objdetect::object` of predictions. Here `cv::dnn_objdetect::object` is a structure containing the following elements.
+
+```c++
+typedef struct {
+  int xmin, xmax;
+  int ymin, ymax;
+  int class_idx;
+  std::string label_name;
+  double class_prob;
+} object;
+
+```
+For further details on post-processing refer this detailed [blog-post](https://kvmanohar22.github.io/GSoC/).
+
+## Results from Object Detection
+
+Refer `tutorials` directory for results.