UPop VS sparseml

SparseML is opensource https://github.com/neuralmagic/sparseml

Utilizing the oBERT research we published at Neural Magic and some further iteration, we’ve enabled an increase in NLP performance of 175X while retaining 99% accuracy on the question-answering task in MLPerf. A combination of distillation, layer dropping, quantization, and unstructured pruning with oBERT enabled these large performance gains through the DeepSparse Engine. All of our contributions and research are open-sourced or free to use. Read through the oBERT paper on arxiv, try out the research in SparseML, and dive into the writeup to learn more about how we achieved these impressive results and utilize them for your own use cases!

Open-source projects leveraged by nebullvm include OpenVINO, TensorRT, Intel Neural Compressor, SparseML and DeepSparse, Apache TVM, ONNX Runtime, TFlite and XLA. A huge thank you to the open-source community for developing and maintaining these amazing projects.

BERT-Large (345 million parameters) is now faster than the much smaller DistilBERT (66 million parameters) all while retaining the accuracy of the much larger BERT-Large model! We made this possible with Intel Labs by applying cutting-edge sparsification and quantization research from their Prune Once For All paper and utilizing it in the DeepSparse engine. It makes BERT-Large 12x smaller while delivering 8x latency speedup on commodity CPUs. We open-sourced the research in SparseML; run through the overview here and give it a try!

All models and code are open-sourced, try it out with the walk-through in SparseML.

Hi u/_Arsenie_Boca_, definitely. Our recipes and sparse models along with the SparseZoo Python API to download them are open-sourced and the SparseZoo UI that can be used to explore them is free to use. The SparseML codebase to apply recipes enabling the creation of the sparse models is open sourced. The Sparsify codebase to create recipes through a UI is as well. And finally, the DeepSparse Engine's backend is closed sourced but free to use.

Hi mikedotonline, we haven't focused on any datasets specifically for natural/forest environments. If you have any in mind, we could do some quick transfer learning runs to see how these models perform on them! Also if you wanted to try them out, we have a tutorial pushed up that walks through transfer learning the sparse architectures to new data: https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov5/tutorials/yolov5_sparse_transfer_learning.md

Apply the sparse-quantized results to your dataset by following the YOLOv3 tutorial. All software is open source or freely available.

We’ve noticed YOLOv3 runs pretty slowly on CPUs restricting its use for real-time requests. Given that, we looked into combining pruning and quantization using the Ultralytics YOLOv3 model, and the results turned out well, over 5X faster over a dense FP32 baseline! We open sourced the integration and models on GitHub for anyone to play around with; if you’re interested, please check it out and give us feedback.