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1. FPGA AI Suite Getting Started Guide
2. FPGA AI Suite Components
3. FPGA AI Suite Installation Overview
4. Installing the FPGA AI Suite Compiler and IP Generation Tools
5. Installing the FPGA AI Suite PCIe-Based Design Example Prerequisites
6. FPGA AI Suite Quick Start Tutorial
7. Running the Hostless DDR-Free Design Example
A. FPGA AI Suite Getting Started Guide Archives
B. FPGA AI Suite Getting Started Guide Document Revision History
4.1. Supported FPGA Families
4.2. Operating System Prerequisites
4.3. Installing the FPGA AI Suite with System Package Management Tools
4.4. Installing OpenVINO™ Toolkit
4.5. Installing Quartus® Prime Pro Edition Software
4.6. Setting Required Environment Variables
4.7. Installing Intel® Threading Building Blocks (TBB)
4.8. Finalizing Your FPGA AI Suite Installation
6.1. Creating a Working Directory
6.2. Preparing OpenVINO™ Model Zoo
6.3. Preparing a Model
6.4. Running the Graph Compiler
6.5. Preparing an Image Set
6.6. Programming the FPGA Device
6.7. Performing Inference on the PCIe-Based Example Design
6.8. Building an FPGA Bitstream for the PCIe Example Design
6.9. Building the Example FPGA Bitstreams
6.10. Preparing a ResNet50 v1 Model
6.11. Performing Inference on the Inflated 3D (I3D) Graph
6.12. Performing Inference on YOLOv3 and Calculating Accuracy Metrics
6.13. Performing Inference Without an FPGA Board
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6.10. Preparing a ResNet50 v1 Model
OpenVINO™ Model Zoo 2023.3 does not include a ResNet50 v1 model.
The following commands create graph.xml and graph.bin files for ResNet50 v1, using the mo_caffe.py command from OpenVINO™ Model Optimizer. These commands assume that you have enabled OpenVINO™ Model Optimizer as described Preparing OpenVINO Model Zoo.
wget https://www.deepdetect.com/models/resnet/ResNet-50-model.caffemodel
wget https://raw.githubusercontent.com/yihui-he/\ resnet-imagenet-caffe/master/resnet_50/ResNet-50-deploy.prototxt
source ~/build-openvino-dev/openvino_env/bin/activate mo \ --input_shape=[1,3,224,224] \ --mean_values=[103.53,116.28,123.675] \ --input=data \ --output=prob \ --input_model=ResNet-50-model.caffemodel \ --input_proto=ResNet-50-deploy.prototxt \ --model_name=graph