When I run the example code with a custom model in local model zoo folder it gives me an error. I have been trying to solve it for many hours and still no luck.
raise DegirumException(
degirum.exception.DegirumException: Model 'yolov8n--640x640_quant_hailort_hailo8_1'
is not found in model zoo 'home/tennibot/hailo_examples/models'
Main code:
import degirum as dg, degirum_tools
inference_host_address = "@local"
zoo_url = '/home/tennibot/hailo_examples/models'
token=''
device_type='HAILORT/HAILO8'
# set model name, and image source
model_name = "yolov8n--640x640_quant_hailort_hailo8_1"
video_source=0
class_list = ["tennisball"]
# load AI model
model = dg.load_model(
model_name=model_name,
inference_host_address=inference_host_address,
zoo_url = zoo_url,
token=token,
device_type=device_type,
)
# create object tracker
tracker = degirum_tools.ObjectTracker(
class_list=class_list,
track_thresh=0.35,
track_buffer=100,
match_thresh=0.9999,
trail_depth=20,
anchor_point=degirum_tools.AnchorPoint.BOTTOM_CENTER,
)
# attach object tracker to model
degirum_tools.attach_analyzers(model, [tracker])
# run AI inference on video stream and display the results
with degirum_tools.Display("AI Camera") as output_display:
for inference_result in degirum_tools.predict_stream(model, video_source):
output_display.show(inference_result.image_overlay)
Heres my folder structure:
yolov8n--640x640_quant_hailort_hailo8_1
|--- HailoDetectionYolo.py
|--- Labels.json
|--- yolov8n--640x640_quant_hailort_hailo8_1.hef
|--- yolov8n--640x640_quant_hailort_hailo8_1.json
HailoDetectionYolo.py
import json
# Post-processor class, must have fixed name 'PostProcessor'
class PostProcessor:
def __init__(self, json_config):
"""
Initialize the post-processor with configuration settings.
Parameters:
json_config (str): JSON string containing post-processing configuration.
"""
# Parse the JSON configuration
self._json_config = json.loads(json_config)
# Extract configuration parameters
self._num_classes = int(
self._json_config["POST_PROCESS"][0]["OutputNumClasses"]
)
self._label_json_path = self._json_config["POST_PROCESS"][0]["LabelsPath"]
self._input_height = int(self._json_config["PRE_PROCESS"][0]["InputH"])
self._input_width = int(self._json_config["PRE_PROCESS"][0]["InputW"])
# Load label dictionary from JSON file
with open(self._label_json_path, "r") as json_file:
self._label_dictionary = json.load(json_file)
# Extract confidence threshold
self._output_conf_threshold = float(
self._json_config["POST_PROCESS"][0].get("OutputConfThreshold", 0.0)
)
def forward(self, tensor_list, details_list):
"""
Process the raw output tensor to produce formatted JSON results.
Parameters:
tensor_list (list): List of tensors from the model.
details_list (list): Additional details (unused in this example).
Returns:
str: JSON-formatted string containing detection results.
"""
# Initialize results list
new_inference_results = []
# Extract and reshape the raw output tensor
output_array = tensor_list[0].reshape(-1)
# Index to parse the array
index = 0
# Iterate over classes and parse results
for class_id in range(self._num_classes):
# Number of detections for this class
num_detections = int(output_array[index])
index += 1 # Move to the next entry
# Skip if no detections for this class
if num_detections == 0:
continue
# Process each detection for this class
for _ in range(num_detections):
if index + 5 > len(output_array):
# Safeguard against unexpected array end
break
# Extract score and bounding box in x_center, y_center, width, height format
score = float(output_array[index + 4])
y_min, x_min, y_max, x_max = map(float, output_array[index : index + 4])
index += 5 # Move to the next detection
# Skip detections below the confidence threshold
if score < self._output_conf_threshold:
continue
# Convert to x_min, y_min, x_max, y_max format
x_min = x_min * self._input_width
y_min = y_min * self._input_height
x_max = x_max * self._input_width
y_max = y_max * self._input_height
# Format the detection result
result = {
"bbox": [x_min, y_min, x_max, y_max],
"score": score,
"category_id": class_id,
"label": self._label_dictionary.get(
str(class_id), f"class_{class_id}"
),
}
new_inference_results.append(result)
# Stop processing if padded zeros are reached
if index >= len(output_array) or all(v == 0 for v in output_array[index:]):
break
# Return results as JSON string
return json.dumps(new_inference_results)
Labels.json:
{
"0": "tennisball"
}
yolov8n–640x640_quant_hailort_hailo8_1.json:
{
"Checksum": "3314f4fcd9099b9085ee79d010affe0c3975ab2be811d26fca7cf252c92e6239",
"ConfigVersion": 10,
"DEVICE": [
{
"DeviceType": "HAILO8",
"RuntimeAgent": "HAILORT",
"SupportedDeviceTypes": "HAILORT/HAILO8",
}
],
"PRE_PROCESS": [
{
"InputType": "Image",
"ImageBackend": "opencv",
"InputPadMethod": "letterbox",
"InputResizeMethod": "bilinear",
"InputN": 1,
"InputH": 640,
"InputW": 640,
"InputC": 3,
"InputQuantEn": true
}
],
"MODEL_PARAMETERS": [
{
"ModelPath": "Yolov8n.hef"
}
],
"POST_PROCESS": [
{
"LabelsPath": "Labels.json",
"OutputNumClasses": 1,
"PythonFile": "HailoDetectionYolo.py",
"OutputPostprocessType": "DetectionYoloV8"
}
]
}
When when i run this code to list models in zoo directory:
import degirum as dg
zoo_1 = dg.connect(
inference_host_address='@local',
zoo_url='/home/tennibot/hailo_examples/models'
)
print(zoo_1.list_models())
I get this output:
['yolov8n_relu6_coco--640x640_quant_hailort_hailo8_3', 'yolov8n_relu6_coco--640x640_quant_hailort_hailo8l_1']