🚀 Feature Implement numpy.random.choice equivalent. Motivation In some cases, it is useful to get random samples from a torch Tensor efficiently. For instance, during the training of some Region-Based Detectors, it is necessary to contro...
A1: Because AFAIK none of them fully recovers the true algorithm of the official efficientdet, that's why their communities could not achieve or having a hard time to achieve the same score as the official efficientdet by training from scratch. ...
The procedure is depicted in Algorithm 1 and works as follows: • Given a set of input-output examples and additional outputs , we first determine which additional outputs are useful for improving the synthesized transformation rule (lines 1 - 2); • For each useful additional output, we ...
A1: Because AFAIK none of them fully recovers the true algorithm of the official efficientdet, that's why their communities could not achieve or having a hard time to achieve the same score as the official efficientdet by training from scratch. ...
A1: Because AFAIK none of them fully recovers the true algorithm of the official efficientdet, that's why their communities could not achieve or having a hard time to achieve the same score as the official efficientdet by training from scratch. ...
A1: Because AFAIK none of them fully recovers the true algorithm of the official efficientdet, that's why their communities could not achieve or having a hard time to achieve the same score as the official efficientdet by training from scratch. ...
A1: Because AFAIK none of them fully recovers the true algorithm of the official efficientdet, that's why their communities could not achieve or having a hard time to achieve the same score as the official efficientdet by training from scratch. ...
A1: Because AFAIK none of them fully recovers the true algorithm of the official efficientdet, that's why their communities could not achieve or having a hard time to achieve the same score as the official efficientdet by training from scratch. ...