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
I'm getting this error
AttributeError: 'Upsample' object has no attribute 'recompute_scale_factor'
when running the first inference example code
python annotate.py yolov5-deepsparse/yolov5s-sgd/weights/best.pt \ --source data/pexels-cottonbro-8717592.mp4 \ --engine torch \ --image-shape 416 416 \ --device cpu \ --conf-thres 0.7
seems related to https://github.com/ultralytics/yolov5/issues/6948#issuecomment-1065213047