Unofficially implements https://arxiv.org/abs/2112.05682 to get Linear Memory Cost on Attention (+ some sidekick speedup on the GPU when compared to reference implementation in JAX) Usage: git clone https://github.com/CHARM-Tx/linear_mem_attention-pytorch cd linear_mem_attention_pytorch python setup.py install Usage: High Level from linear_mem_attention_torch.

This is a simple implementation of Guided Dropout for research.We try to reproduce the algorithm according to the paper published in AAA-19, but we can’t guarantee the performance reported in the paper.We will list some experiment results soon. TODO Release the reproduced code list experiment results … Setup pip install -r requirements.txt Run Run Guided Dropout on CIFAR10 Dataset (mlp 3 hidden layers with 1024 nodes) python mainpro.py –dataset CIFAR10 –arc mlp –mlp-depth 3 –hidden-dim 1024 -e    

Pytorch based library to rank predicted bounding boxes using text/image user’s prompts. Usually, object detection models trains to detect common classes of objects such as “car”, “person”, “cup”, “bottle”. But sometimes we need to detect more complex classes such as “lady in the red dress”, “bottle of whiskey”, or “where is my red cup” instead of “person”, “bottle”, “cup” respectively. One way to solve this problem is to train more complex detectors that can detect more complex classes, but we […]