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detectors: increase yolov8 postprocessing score trershold to 0.5

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This commit is contained in:
Indrek Mandre 2024-02-09 10:46:06 +02:00
parent 79b88c979e
commit 4318b5aee1
1 changed files with 1 additions and 1 deletions
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frigate/detectors/yolo_utils.py
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@ -22,7 +22,7 @@ def preprocess(tensor_input, model_input_shape, model_input_element_type):
# cv2.dnn.blobFromImage is faster than numpying it # cv2.dnn.blobFromImage is faster than numpying it
return cv2.dnn.blobFromImage(tensor_input[0], 1.0 / 255, (model_input_shape[3], model_input_shape[2]), None, swapRB=False) return cv2.dnn.blobFromImage(tensor_input[0], 1.0 / 255, (model_input_shape[3], model_input_shape[2]), None, swapRB=False)
def yolov8_postprocess(model_input_shape, tensor_output, box_count = 20, score_threshold = 0.3, nms_threshold = 0.5): def yolov8_postprocess(model_input_shape, tensor_output, box_count = 20, score_threshold = 0.5, nms_threshold = 0.5):
model_box_count = tensor_output.shape[2] model_box_count = tensor_output.shape[2]
probs = tensor_output[0, 4:, :] probs = tensor_output[0, 4:, :]
all_ids = np.argmax(probs, axis=0) all_ids = np.argmax(probs, axis=0)