Ohh, It makes sense now. Instead of setting a different environment for YOLOv5 I used the same env as of YOLOv8 to train YOLOv5 nano model.

But the following website shows the similar code for YOLOv5 to train as of YOLOv8 (Reason for confusion)
https://docs.ultralytics.com/models/yolov5/#usage-examples

Following is the code from website I referred. But environment was same as we use to train latest models

from ultralytics import YOLO

# Load a COCO-pretrained YOLOv5n model
model = YOLO("yolov5n.pt")

# Display model information (optional)
model.info()

# Train the model on the COCO8 example dataset for 100 epochs
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)

# Run inference with the YOLOv5n model on the 'bus.jpg' image
results = model("path/to/bus.jpg")

I will update the environment and test the model again, thanks.

This is the latest model I trained using following reference, still it didn't convert to blob model. Still getting same error

https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/

Hi @jakob, I was able to export the blob model and it gave me onnx model and config json.

from onnx I was able to convert to blob using (https://blobconverter.luxonis.com/)
but the model is rarely detecting anything (Maybe I need to increase the dataset. currently its 250 images).
I have attached the .pt , onnx and converted blob model for reference.

Also when I referred this link (luxonis/depthai-pythonblob/main/examples/Yolo/tiny_yolo.py
) for testing the yolov5 converted blob model the oak-1 crashes with following log

'
import cv2
import depthai as dai
import numpy as np

Configuration

MODEL_PATH = 'blobs/correct.blob'
ANCHORS = [
10.0, 13.0, 16.0, 30.0, 33.0, 23.0,
30.0, 61.0, 62.0, 45.0, 59.0, 119.0,
116.0, 90.0, 156.0, 198.0, 373.0, 326.0
]
ANCHOR_MASKS = {
"side52": [0, 1, 2],
"side26": [3, 4, 5],
"side13": [6, 7, 8]
}
CONF_THRESHOLD = 0.3
IOU_THRESHOLD = 0.2
NUM_CLASSES = 1
VIDEO_SIZE = (416, 416)
SENSOR_RES = dai.ColorCameraProperties.SensorResolution.THE_4_K
ISP_SCALE = (1, 1)
FPS = 30
DEVICE_MAC = "1844301091F11CF500"

def frame_norm(frame, bbox):
"""Normalize YOLO bbox coords to image pixels."""
h, w = frame.shape[:2]
return (
int(bbox.xmin * w), int(bbox.ymin * h),
int(bbox.xmax * w), int(bbox.ymax * h)
)

def create_pipeline():
pipeline = dai.Pipeline()

# Color camera
cam = pipeline.create(dai.node.ColorCamera)
cam.setResolution(SENSOR_RES)
cam.setIspScale(*ISP_SCALE)
cam.setVideoSize(*VIDEO_SIZE)
cam.setInterleaved(False)
cam.setPreviewSize(416, 416)
cam.setPreviewKeepAspectRatio(False)
cam.setFps(FPS)

# YOLO detection network
nn = pipeline.create(dai.node.YoloDetectionNetwork)
nn.setBlobPath(MODEL_PATH)
nn.setConfidenceThreshold(CONF_THRESHOLD)
nn.setNumClasses(NUM_CLASSES)
nn.setIouThreshold(IOU_THRESHOLD)
nn.setAnchors(ANCHORS)
nn.setAnchorMasks(ANCHOR_MASKS)
nn.setCoordinateSize(4)
nn.setNumInferenceThreads(2)
nn.input.setBlocking(False)

# Outputs
xout_cam = pipeline.create(dai.node.XLinkOut)
xout_nn  = pipeline.create(dai.node.XLinkOut)
xout_pass = pipeline.create(dai.node.XLinkOut)

xout_cam.setStreamName("rgb")
xout_nn.setStreamName("detections")
xout_pass.setStreamName("pass")

# Link nodes
cam.preview.link(nn.input)
nn.out.link(xout_nn.input)
nn.passthrough.link(xout_pass.input)
if True:  # sync NN + frames
    nn.passthrough.link(xout_cam.input)
else:
    cam.preview.link(xout_cam.input)

return pipeline

def main():
pipeline = create_pipeline()

with dai.Device(pipeline, dai.DeviceInfo(DEVICE_MAC)) as device:
    q_rgb = device.getOutputQueue("rgb", maxSize=4, blocking=False)
    q_det = device.getOutputQueue("detections", maxSize=4, blocking=False)

    while True:
        in_rgb = q_rgb.get()  # blocking
        in_det = q_det.tryGet()

        frame = in_rgb.getCvFrame()

        # Draw detections
        if in_det:
            for det in in_det.detections:
                x1, y1, x2, y2 = frame_norm(frame, det)
                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)

        cv2.imshow("YOLO", frame)
        if cv2.waitKey(1) == ord('q'):
            break

cv2.destroyAllWindows()

if name == "main":
main()
'

with dai.Device(pipeline, dai.DeviceInfo(DEVICE_MAC)) as device:
Stack trace (most recent call last):
#31 Object "", at 00007FFD3AED99B8, in PyInit_depthai
#30 Object "", at 00007FFD3AED908B, in PyInit_depthai
#29 Object "", at 00007FFD3AEDD8DD, in PyInit_depthai
#28 Object "", at 00007FFD3AED68FE, in PyInit_depthai
#27 Object "", at 00007FFD3AEA3344, in PyInit_depthai
#26 Object "", at 00007FFD3AEB9CC1, in PyInit_depthai
#25 Object "", at 00007FFD3AEB82AB, in PyInit_depthai
#24 Object "", at 00007FFE28F73C66, in RtlCaptureContext2
#23 Object "", at 00007FFD3B04F86E, in PyInit_depthai
#22 Object "", at 00007FFD3B058B40, in PyInit_depthai
#21 Object "", at 00007FFD3B0928B4, in PyInit_depthai
#20 Object "", at 00007FFD3B04CB8F, in PyInit_depthai
#19 Object "", at 00007FFE262B565C, in RaiseException
#18 Object "", at 00007FFE28F24475, in RtlRaiseException
#17 Object "", at 00007FFE28EEE466, in RtlFindCharInUnicodeString
#16 Object "", at 00007FFE28F7441F, in _chkstk
#15 Object "", at 00007FFD3B04BE25, in PyInit_depthai
#14 Object "", at 00007FFD3B04F3F1, in PyInit_depthai
#13 Object "", at 00007FFD3B04F38C, in PyInit_depthai
#12 Object "", at 00007FFD3B04E581, in PyInit_depthai
#11 Object "", at 00007FFD3B04DD09, in PyInit_depthai
#10 Object "", at 00007FFD3B04BA32, in PyInit_depthai
#9 Object "", at 00007FFE28EEFD54, in RtlUnwindEx
#8 Object "", at 00007FFE28F7449F, in _chkstk
#7 Object "", at 00007FFD3B04A84C, in PyInit_depthai
#6 Object "", at 00007FFD3B04BE25, in PyInit_depthai
#5 Object "", at 00007FFD3B04F3F1, in PyInit_depthai
#4 Object "", at 00007FFD3B04F2B9, in PyInit_depthai
#3 Object "", at 00007FFD3B04B489, in PyInit_depthai
#2 Object "", at 00007FFD3B050017, in PyInit_depthai
#1 Object "", at 00007FFD3B058BAA, in PyInit_depthai
#0 Object "", at 00007FFD3AEB72F4, in PyInit_depthai

could you give me updated or fixed version of the code to test the blob model before directly running in android device.

Also let me know if I am doing something wrong.

Note: the existing model is working fine from the android code repo. And once I had received some detections from the
custom model that I had built the way I mentioned above.

Thanks

Blob

Config json

Onnx

Pt

Everything mentioned above is working fine. The only catch was following part of the code where inference was not actually running on preview size 416

this fixed it

    camRgb->setResolution(dai::ColorCameraProperties::SensorResolution::THE_4_K);
    camRgb->setPreviewSize(416, 416); // forgot to use it
    camRgb->setIspScale(26,135);
    camRgb->setVideoSize(416, 416);        // 416 × 416 for NN & preview

Currently getting 20FPS on 4K resolution in android application connected to luxonis OAK-1.

Thanks for help

Hi @jakob, what could be the possible reason for Oak-1 Fixed Focus camera to stay blurry for far objects (Like 30-35ft)?
The fixed focus camera focuses properly for closer objects but stays blurry for objects which are further distance.