Can we Train YOLOv3 on custom objects with no GPU?

Question

My YOLO model works fine for detecting objects such as bottle, person, cellphone, backpack et cetera. But I want to make my model detect a ring or a bracelet or a helmet (objects which are not in the present in the present yolo model). Without GPU can I make a custom object detection yolo model? What are the risks involved? (if any).

My System is Windows 10 Home single language with 8GB RAM.

Answer 1

Re-compile darknet.exe to run on CPU is terribly slow. I've tried before. It's totally unpractical. Recommend you study Intel OpenVINO toolkit.

https://software.intel.com/en-us/openvino-toolkit

and

https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_tf_specific_Convert_YOLO_From_Tensorflow.html

OpenVINO toolkit can load and run any frameworks on their CPU/integrated GPU.

You can still use regular NVIDIA cards to train your custom objects by Darknet YOLO. Then use 3rd-party converter tools (which can be easily found on the GitHub) to convert YOLO weight files you trained to the Tensorflow PB file. Then use Intel's Model Optimizer to transform the PB file and label file into their so-called "Inference Representation" files (named in *.bin, *.xml, *.labels, and *.mapping files) which later can be loaded and run on Intel's CPU or integrated GPU.

Their Model Optimizer will automatically optimize and remove some unused nodes in YOLO convolutional network file and improve the overall inference speed, which is much faster than simply using re-compiled CPU version of darknet.exe to run YOLO weight on CPU.