Start training with SOTA computer vision models that span across various tasks including image classification, semantic segmentation, and object detection.
YOLO-NAS Pose offers a superior latency-accuracy balance compared to YOLOv8 Pose. Specifically, the medium-sized version, YOLO-NAS Pose M, outperforms the large YOLOv8 variant with a 38.85% reduction in latency on an Intel Xeon 4th gen CPU, all while achieving a 0.27 boost in [email protected] score.
Simplify deep learning development with SuperGradients, an open-source, production-ready library for training PyTorch-based computer vision models.
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")
model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")