Reporting by Machine Heart, Machine Heart Editorial Team For some inference tasks of large models, the bottleneck is not computational power (FLOPS). Recently, many people in the open-source community have been exploring optimization methods for large models. A project called llama.cpp has rewritten the inference code of LLaMa in pure C++, achieving excellent results and … Read more
In the process of developing machine learning, once the model has been trained, the next step is to perform model inference. Depending on the deployment environment, it can be divided into three types of scenarios: Edge Computing: Generally refers to mobile phones and embedded devices, performing inference directly on the device where the data is … Read more
Produced by | Smart Things Open Class Instructor | Wang Haitao Co-founder of OPEN AI LAB and Chief Architect of Tengine Reminder | Click the blue text above to follow us, and reply with the keyword “AI Framework” to obtain the course materials. Introduction: On the evening of April 8, Smart Things Open Class launched … Read more
MEGAWAY TECHNOLOGY RK3588 YOLOv5s Model Deployment and Evaluation 01/ Model Overview Model Name: YOLOv5s Model Type: Classification Model Official Repository: GitHub – ultralytics/yolov5: YOLOv5 in PyTorch> ONNX > CoreML > TFLite V7.0 Model Parameters (PARAMS): 7225885 Model Computation (FLOPS): 16.4 GFLOPs Deployment Device: RK3588 Deployment Environment: Ubuntu20.04/rknn_toolkit2 V1.6.0/OpenCV4.5.1 02/ Model Analysis YOLOV5 model outputs three … Read more
In the previous introduction, we discussed how to develop AI inference applications based on Orange Pi AIpro, and learned that before inference, the original network model (which could be PyTorch/TensorFlow/Caffe, etc.) needs to be converted into an .om model. Only then can we call the Ascend aclmdlExecute and other model execution interfaces for model inference … Read more