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@@ -51,7 +51,7 @@ This creates a `model.onnx` file, in the directory of your `weights` (e.g. `runs | |
DeepSparse’s performance can be pushed even further by optimizing the model for inference. DeepSparse is built to take advantage of models that have been optimized with weight pruning | ||
and quantization—techniques that dramatically shrink the required compute without dropping accuracy. Through our One-Shot optimization methods, which will be made available in an upcoming | ||
product called Sparsify, we have produced YOLOv8s and YOLOv8n ONNX models that have been quantized to INT8 while maintaining at least 99% of the original FP32 [email protected]. | ||
This was achieved with just 1024 samples and no back-propagation. You can download the quantized models [here](https://drive.google.com/drive/folders/1vf4Es-8bxhx348TzzfhvljMQUo62XhQ4?usp=sharing). | ||
This was achieved with just 1024 samples and no back-propagation. You can download the quantized models [here](https://sparsezoo.neuralmagic.com/?searchModels=yolov8). | ||
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## Deployment Example | ||
The following example uses pipelines to run a pruned and quantized YOLOv8 model for inference. As input, the pipeline ingests a list of images and returns for each image the detection boxes in numeric form. | ||
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