Webinar: How to Achieve FP32 Accuracy with INT8 Inference Speed

Watch Deci’s experts, Ofer Baratz and Borys Tymchenko, PhD, in this hands-on technical session about INT8 quantization.

✅ Learn the different quantization techniques and best practices for accelerating speed without degrading your models’ accuracy.

✅ Check out code examples and tools that you can easily leverage to achieve your inference performance targets.

Do you want to accelerate the inference of your deep learning use case? Book a demo here.

You May Also Like

Webinar: How to Optimize Latency for Edge AI Deployments

Gen AI Models: Open Source vs Closed Source—Pros, Cons & Everything in Between

Webinar: The Making of YOLO-NAS, a Foundation Model, with NAS

Add Your Heading Text Here
					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")