Deploy Fast and Accurate YOLOv8 Object Detection Models on CPUs You Already Have

[@muhammadwaleedather1726]

mam my question is that if i have trained a model on simple yolov8 from ultralytics i got the best.pt file as my trained model can i directly remove unwanted weights from that from your technique or i have to complete train the model through your technique to get that right?

[@nishant.wankhade]

Hi there, What are the hardware specifications of GPU to run yolov8 model , I have Nvidia GT 730 and while running the model it is giving me "cuda: no kernel image is available for execution on the device."

[@neuralmagic]

Hello! Our software is specific to CPU infrastructure. Our runtime, DeepSparse, is engineered to take advantage of CPU memory to deliver the performance we claimed in the video.

[@albertofernandez055]

Many thanks for this video. I have several questions: (1) After "applying to our data" using a SparseZoo recipe to apply to our data, using "sparseml.ultralytics.train", what is the format of the generated weights? (2) Moreover, have you tried to import the generated weights for the sparsified model in OpenCV using: cv2.dnn.readNetFromONNX('yolov8n_sparsified.onnx') (3) And finally, I have a question with the license. As far as I see in all the Neural Magic repositories, all of them have Apache License 2.0. Is this correct? (4) So are there any commercial restrictions of using cv2.dnn.readNetFromONNX('yolov8n_sparsified.onnx')? Many thanks!!!

[@chihebnouri5541]

Did you find a solution?