Here is a list of all files with brief descriptions:

[detail level 1234]

▼ darknet
doc
► src-cli
darknet_cli.cpp
► src-examples
darknet_01_inference_images.cpp	This application will print to `STDOUT` details on each object detected in an image
darknet_02_display_annotated_images.cpp	This application will display processed images in a GUI window
darknet_03_display_videos.cpp	This application will display one or more videos at the frame rate stored in the video file
darknet_04_process_videos.cpp	This application will process one or more videos as fast as possible on a single thread and save a new output video to disk
darknet_05_process_videos_multithreaded.cpp	This application will process one or more videos as fast as possible using multiple threads and save a new output video to disk
darknet_06_images_to_json.cpp	This application will call predict() on an image or images and store the results in a JSON file
darknet_07_display_rtsp_stream.cpp	This application will read from a RTP stream, run the video through Darknet/YOLO, and display the results
darknet_08_display_webcam.cpp	This application will read from a webcam, run the video through Darknet/YOLO, and display the results
darknet_09_keypoints_images.cpp	This application uses a specific neural network that follows the MSCOCO-style keypoints to show "skeletons" over people detected in images
darknet_10_keypoints_videos.cpp	This application combines the functionality of darknet_03_display_videos and darknet_09_keypoints_images
darknet_11_images_to_yolo.cpp	This application will call predict() on images and create YOLO-format annotations
► src-lib
► apple
apple_mps.hpp	Apple MPS/Metal inference entry points (optional at runtime)
apple_mps.mm
metal_backend.hpp	Minimal Metal compute wrapper used by the Apple MPS path
metal_backend.mm
► windows
getopt.c
getopt.h
activation_kernels.cu
activations.cpp
activations.hpp
amd_rocm.cpp
amd_rocm.hpp
avgpool_layer.cpp
avgpool_layer.hpp
avgpool_layer_kernels.cu
batchnorm_layer.cpp
batchnorm_layer.hpp
blas.cpp
blas.hpp	BLAS: "Basic Linear Algebra Subprograms"
blas_kernels.cu
box.cpp
box.hpp
Chart.cpp
Chart.hpp	This file contains C++ classes and functions for working with chart.png
col2im.cpp
col2im.hpp
col2im_kernels.cu
connected_layer.cpp
connected_layer.hpp
convolutional_kernels.cu
convolutional_layer.cpp
convolutional_layer.hpp
cost_layer.cpp
cost_layer.hpp
crnn_layer.cpp
crnn_layer.hpp
crop_layer_kernels.cu
dark_cuda.cpp
dark_cuda.hpp
darknet.cpp
darknet.h	Include this file to get access to the Darknet/YOLO C API
darknet.hpp	Include this file to get access to the new Darknet V3 C++ API
darknet_args_and_parms.cpp
darknet_args_and_parms.hpp
darknet_cfg.cpp
darknet_cfg.hpp	The classes in this header file are used to parse Darknet/YOLO .cfg files
darknet_cfg_and_state.cpp
darknet_cfg_and_state.hpp
darknet_enums.cpp
darknet_enums.hpp	Some of the common Darknet/YOLO enums, and methods to convert between them and `std::string`
darknet_format_and_colour.cpp
darknet_format_and_colour.hpp
darknet_gpu.hpp	Some GPU-specific includes and definitions
darknet_image.cpp
darknet_image.hpp	This file defines Darknet::Image as well as many functions which modify or create images
darknet_internal.hpp
darknet_keypoints.cpp
darknet_keypoints.hpp	This file defines Darknet::Keypoints and related functions
darknet_layers.hpp	Defines the layer structure and includes all of the different layer include files
darknet_network.cpp
darknet_network.hpp
darknet_utils.cpp
darknet_utils.hpp	Collection of helper and utility functions for Darknet
darknet_version.h	The input file darknet_version.h.in is processed by CMake which then creates the header file darknet_version.h
darkunistd.hpp
data.cpp
data.hpp
detector.cpp
detector_map.cpp
dropout_layer.cpp
dropout_layer.hpp
dropout_layer_kernels.cu
dump.cpp
dump.hpp
gaussian_yolo_layer.cpp
gaussian_yolo_layer.hpp
gemm.cpp	General matrix multiplication (GEMM)
gemm.hpp	General matrix multiplication (GEMM)
im2col.cpp
im2col.hpp
im2col_kernels.cu
image_opencv.cpp
image_opencv.hpp
layer.cpp
list.cpp
list.hpp
lstm_layer.cpp	Long short-term memory
lstm_layer.hpp	Long short-term memory
matrix.cpp
matrix.hpp
maxpool_layer.cpp
maxpool_layer.hpp
maxpool_layer_kernels.cu
network_kernels.cu
nightmare.cpp
option_list.cpp
option_list.hpp
region_layer.cpp
region_layer.hpp
reorg_layer.cpp
reorg_layer.hpp
rnn_layer.cpp
rnn_layer.hpp
route_layer.cpp
route_layer.hpp
sam_layer.cpp
sam_layer.hpp
scale_channels_layer.cpp
scale_channels_layer.hpp
shortcut_layer.cpp
shortcut_layer.hpp
softmax_layer.cpp
softmax_layer.hpp
Timing.cpp
Timing.hpp	This file contains C++ classes and functions for working with timing and tracking results
tree.cpp
tree.hpp
upsample_layer.cpp
upsample_layer.hpp
utils.cpp
utils.hpp
weights.cpp
weights.hpp
yolo_layer.cpp
yolo_layer.hpp
► src-onnx
darknet_node.cpp
darknet_node.hpp	Class used to create and manipulate ONNX nodes
darknet_onnx.cpp	Convert Darknet/YOLO .cfg and .weights files to .onnx files
darknet_onnx.hpp	Class used to convert the Darknet .cfg and .weights file to a .onnx file
darknet_onnx_main.cpp	Convert Darknet/YOLO .cfg and .weights files to .onnx files
onnx.proto3.pb.cc
onnx.proto3.pb.h
onnx_check_model.py
onnx_display_model.py
onnx_export_and_verify.py
onnx_test_model.py
► src-python
darknet.py
darknet_images.py
darknet_video.py
example.py