This folder contains an example of loading a custom image dataset with OpenCV and training a model to label images, using the PyTorch C++ frontend.
The dataset used here is Caltech 101 dataset.
The entire training code is contained in custom-data.cpp.
You can find instructions on how to install OpenCV here.
To build the code, run the following commands from your terminal:
$ cd custom-dataset
$ mkdir build
$ cd build
$ cmake -DCMAKE_PREFIX_PATH=/path/to/libtorch ..
$ makewhere /path/to/libtorch should be the path to the unzipped LibTorch distribution, which you can get from the PyTorch homepage.
if you see an error like undefined reference to cv::imread(std::string const&, int) when running the make command, you should build LibTorch from source using the instructions here, and then set CMAKE_PREFIX_PATH to that PyTorch source directory.
The build directory should look like this:
.
├── custom-dataset
├── dataset
│ ├── accordion
│ │ ├── image_0001.jpg
│ │ ├── ...
│ ├── airplanes
│ │ ├── ...
│ ├── ...
├── info.txt
└── Makefile
└── ...
info.txt file gets copied from source directory during build.
Execute the compiled binary to train the model:
./custom-dataset
Running on: CUDA
Train Epoch: 1 16/7281 Loss: 0.314655 Acc: 0
Train Epoch: 1 176/7281 Loss: 0.532111 Acc: 0.0681818
Train Epoch: 1 336/7281 Loss: 0.538482 Acc: 0.0714286
Train Epoch: 1 496/7281 Loss: 0.535302 Acc: 0.0705645
Train Epoch: 1 656/7281 Loss: 0.536113 Acc: 0.0716463
Train Epoch: 1 816/7281 Loss: 0.537626 Acc: 0.0784314
Train Epoch: 1 976/7281 Loss: 0.537055 Acc: 0.079918
...