  Spiking neural network (SNN) has been attached to great importance due to the
properties of high biological plausibility and low energy consumption on
neuromorphic hardware. As an efficient method to obtain deep SNN, the
conversion method has exhibited high performance on various large-scale
datasets. However, it typically suffers from severe performance degradation and
high time delays. In particular, most of the previous work focuses on simple
classification tasks while ignoring the precise approximation to ANN output. In
this paper, we first theoretically analyze the conversion errors and derive the
harmful effects of time-varying extremes on synaptic currents. We propose the
Spike Calibration (SpiCalib) to eliminate the damage of discrete spikes to the
output distribution and modify the LIPooling to allow conversion of the
arbitrary MaxPooling layer losslessly. Moreover, Bayesian optimization for
optimal normalization parameters is proposed to avoid empirical settings. The
experimental results demonstrate the state-of-the-art performance on
classification, object detection, and segmentation tasks. To the best of our
knowledge, this is the first time to obtain SNN comparable to ANN on these
tasks simultaneously. Moreover, we only need 1/50 inference time of the
previous work on the detection task and can achieve the same performance under
0.492$\times$ energy consumption of ANN on the segmentation task.
