Object Detection with YOLOX (PyTorch) - training with python in Colab, inference with C++

SAMPLE DATA =========== Sample images are in the attached zip [login to view URL] under CreatedImages The annotations are given in the text files [login to view URL] [login to view URL] Suppose data is in Google Drive under '/content/gdrive/MyDrive/SampleObjectDetection02/od01/' So start with an Colab notebook like: from [login to view URL] import drive [login to view URL]("/content/gdrive", force_remount=True) dir = '/content/gdrive/MyDrive/SampleObjectDetection02/od01/' %cd {dir} TASKS ===== I) Training in Colab -------------------- Remark: This all has to be done in Colab. (For development you nust ahve a Colab environment on your own!) 1) Prepare data for use in training using a pretrained YOLOX model 2) Train model and show how to set the following training features: - selection of pretrained model (YOLOX-s, YOLOX-x,...) - settings for data augmentation, at least : * rotation with limits for angle * flipping vertical and horizontal * brightness and/or contrast with limits for brightness/contrat * zoom with limits for zoom factor - how to freeze layers - use of tensorboard 3) Evaluate model, calculate measures for goodness of model abritray test data 4) Inference: Use model with a specific image, show results (rectangles around objects found), save results as tab-delimited text file -> Send notebook which I can run in Colab II) Inference with C++ ---------------------- Remark: It is fine to use OpenCV (C++) via vcpkg for this part. 1) Setup a concole C++ Visual Studio 2021 project (or Embarcadero C++ Builder 11 project) 2) Use trained model from above 3) Calculate specific image, show results (rectangles around objects found), save results as tab-delimited text file -> Send source code for C++ project Both tasks have to be done sucessfully on my machine before payment is released. Of course, I have tp be able to perform the training in my Colab and build and run the C++ project for inference.