This is the document on how to test the YOLOv8s detection map with the COCO 2017.
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1. Prerequest
1.1. Provided Files
Unzip Download C3V_Validation_Resources_v1.0.zip and unzip the provided zipped file. We use 'provided_files' to represent the folder where you unzip the file to. The folder structure is as follows:
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The results will be printed in the terminal as follows:
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3.3. Inference by C code
3.3.1. Prepare the environment
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(C3V Board)
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sudo apt update sudo apt install python3-pip libopencv-dev libjsoncpp-dev pip install pycocotools |
3.3.2. Prepare the project (Ubuntu PC)
Download the mapTestTools, the .zip and unzip the provided zipped file. The project code tree like this：
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#include "common.h"
#include "vnn_quantized_extracted_yolov8s_inspiren.h"
vsi_nn_graph_t *
vnn_CreateGraph(const char *data_file_name, vsi_nn_context_t in_ctx,
const vsi_nn_preprocess_map_element_t *pre_process_map,
uint32_t pre_process_map_count,
const vsi_nn_postprocess_map_element_t *post_process_map,
uint32_t post_process_map_count)
{
return vnn_Createquantized_extracted_yolov8s_inspiren(data_file_name, in_ctx, pre_process_map, pre_process_map_count, post_process_map, post_process_map_count);
}
void vnn_ReleaseGraph(vsi_nn_graph_t *graph, vsi_bool release_ctx)
{
vnn_Releasequantized_extracted_yolov8s_inspiren(graph, release_ctx);
} |
3.3.3. Build and Evaluation
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(C3V Board)
Copy the project files to the C3V, and build it on C3V Ubuntu with follow command.
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