https://github.com/dnth/yolov5-deepsparse-blogpost
By the end of this post, you will learn how to: Train a SOTA YOLOv5 model on your own data. Sparsify the model using SparseML quantization aware training, sparse transfer learning, and one-shot quantization. Export the sparsified model and run it using the DeepSparse engine at insane speeds. P/S: The end result - YOLOv5 on CPU at 180+ FPS using on
https://github.com/dnth/yolov5-deepsparse-blogpost
hacktoberfest
Last synced: about 1 year ago
JSON representation
By the end of this post, you will learn how to: Train a SOTA YOLOv5 model on your own data. Sparsify the model using SparseML quantization aware training, sparse transfer learning, and one-shot quantization. Export the sparsified model and run it using the DeepSparse engine at insane speeds. P/S: The end result - YOLOv5 on CPU at 180+ FPS using on
- Host: GitHub
- URL: https://github.com/dnth/yolov5-deepsparse-blogpost
- Owner: dnth
- Created: 2022-05-24T12:26:58.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2023-04-20T23:44:41.000Z (about 3 years ago)
- Last Synced: 2025-04-14T14:14:23.200Z (about 1 year ago)
- Topics: hacktoberfest
- Language: Jupyter Notebook
- Homepage: https://dicksonneoh.com/portfolio/supercharging_yolov5_180_fps_cpu/
- Size: 1.08 GB
- Stars: 54
- Watchers: 2
- Forks: 13
- Open Issues: 17
-
Metadata Files:
- Readme: README.md