Hi Hailo community,
I’m currently optimizing a model on the Hailo platform and noticed that some layers are showing poor Signal-to-Noise Ratio (SNR) during evaluation. I’m looking for guidance or best practices on how to improve SNR in these layers.
Here are the layers with the worst SNR values:
ew_sub_softmax1: -31.38
ne_activation_ew_sub_softmax1: 0.0
reduce_sum_softmax1: -1.72
ew_mult_softmax1: 0.76
dw1: 9.65
conv_feature_splitter7_2: 5.93
conv41: 8.09
conv49: 7.95
conv50: 4.95
conv51: 9.09
conv_feature_splitter9_2: 9.80
conv56: 8.28
conv53: -0.01
conv54: 6.93
conv58: 8.60
conv60: 8.71
conv61: 4.43
conv62: 7.02
conv64: 6.83
conv65: 7.04
conv76: 9.09
conv78: 3.95
conv79: 1.27
conv80: 0.0
My setup
normalization1 = normalization([0.0, 0.0, 0.0], [255.0, 255.0, 255.0])
change_output_activation(conv54, sigmoid)
change_output_activation(conv65, sigmoid)
change_output_activation(conv80, sigmoid)
nms_postprocess(“/content/drive/MyDrive/orangePI/Hailo/yolov11/yolov11_nms_layer_config.json”, meta_arch=yolov8, engine=cpu)
model_optimization_config(calibration, batch_size=16, calibset_size=1024)
pre_quantization_optimization(activation_clipping, layers={*}, mode=percentile, clipping_values=[0.01, 99.99])
pre_quantization_optimization(weights_clipping, layers={*}, mode=percentile, clipping_values=[0.01, 99.99])
pre_quantization_optimization(equalization, policy=enabled)
model_optimization_flavor(optimization_level=3, compression_level=0)
post_quantization_optimization(adaround, policy=enabled, epochs=320, dataset_size=1024, batch_size=128)
I’m particularly concerned about the very low or negative SNR in layers like ew_sub_softmax1
, reduce_sum_softmax1
, conv53
, and conv80
.
Any insights, experiences, or tools from the community would be greatly appreciated!
Thanks in advance!