(1)yolov5_app.py for ploting moldel inference results
cd kneron_hw_models
python -m applications.yolov5_app

(2)mAP of yolov5s(input640x640) @ coco val2017
/mnt/models/Object_models/YOLOv5/101620_yolov5/yolov5s_v2_op9_sig_batch1_input05_640x640_nearest_convert.onnx 
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.356
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.549
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.383
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.208
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.403
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.458
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.299
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.503
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.559
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.367
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.621
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.702

(3)mAP of yolov5m(input640x640) @ coco val2017
/mnt/models/Object_models/YOLOv5/122220_yolov5/yolov5m_v2_op9_sig_batch1_input05_640x640_nearest_convert.onnx
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.426
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.619
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.462
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.258
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.478
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.548
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.338
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.563
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.617
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.426
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.676
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.763



(5)the parameters setting
(5.1)In order to get high mAP in coco val2017, please use 
{
    "model_path": "/mnt/models/Object_models/YOLOv5/122220_yolov5/yolov5m_v2_op9_sig_batch1_input05_640x640_nearest_convert.onnx",
    "grid20_path": "/mnt/models/Object_models/YOLOv5/122220_yolov5/20_640x640.npy",
    "grid40_path": "/mnt/models/Object_models/YOLOv5/122220_yolov5/40_640x640.npy",
    "grid80_path": "/mnt/models/Object_models/YOLOv5/122220_yolov5/80_640x640.npy",
    "num_classes": 80,
    "imgsz_h": 640,
	"imgsz_w": 640,
	"conf_thres": 0.001,
	"iou_thres": 0.65,
    "top_k_num": 3000
}


(5.2)For video usage scenarios, please use
{
    "model_path": "/mnt/models/Object_models/YOLOv5/122220_yolov5/yolov5m_v2_op9_sig_batch1_input05_640x640_nearest_convert.onnx",
    "grid20_path": "/mnt/models/Object_models/YOLOv5/122220_yolov5/20_640x640.npy",
    "grid40_path": "/mnt/models/Object_models/YOLOv5/122220_yolov5/40_640x640.npy",
    "grid80_path": "/mnt/models/Object_models/YOLOv5/122220_yolov5/80_640x640.npy",
    "num_classes": 80,
    "imgsz_h": 640,
	"imgsz_w": 640,
	"conf_thres": 0.3,
	"iou_thres": 0.5,
    "top_k_num": 3000
}

(5.3)The differences of above setting are
(5.3.1) Video uses input (640w*352h) to run faster.
Coco has high or flat wide images, so it is better to use input (640w*640h) 
 
(5.3.2) Using the yolov5 official website setting test coco val2017, the confidence setting is low "conf_thres": 0.001, and the iou setting of NMS is high "iou_thres": 0.65, which gets a better mAP.
But running video needs to be set to "conf_thres": 0.3, so that there are not too many false positives, and the iou setting of NMS "iou_thres": 0.5 is more friendly to close objects


(6) To generate the npy:
python yolov5_generate_npy