Learning and Building Convolutional Neural Networks using PyTorch
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Updated
Apr 1, 2022 - Python
Learning and Building Convolutional Neural Networks using PyTorch
The repository collects many various multi-modal transformer architectures, including image transformer, video transformer, image-language transformer, video-language transformer and self-supervised learning models. Additionally, it also collects many useful tutorials and tools in these related domains.
A collection of SOTA Image Classification Models in PyTorch
[CVPR 2022 Oral] AdaMixer: A Fast-Converging Query-Based Object Detector
Implementation for paper MLP-Mixer: An all-MLP Architecture for Vision
(TPAMI2022) Salient Object Detection via Integrity Learning.
[ECCV 2022] Official pytorch implementation of the paper, "PointMixer: MLP-Mixer for Point Cloud Understanding"
Unofficial PyTorch Implementation for pNLP-Mixer: an Efficient all-MLP Architecture for Language (https://arxiv.org/abs/2202.04350)
An all MLP architecture for Computer Vision by Google (Paper Implementation)
An official implementation of CVPR 2019 paper "All You Need Is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification".
PyTorch implementation of Deep-Learning Architectures
Keras implementation of mlp-mixer, ResMLP, gmlp. imagenet/imagenet21k weights reloaded.
Tensorflow implementation of MLP-Mixer based TTS
HomebrewNLP in Mesh-TensorFlow flavour for distributed TPU training
TensorFlow implementation of "MLP-Mixer: An all-MLP Architecture for Vision"
Unofficial implementation of MLP-Mixer: An all-MLP Architecture for Vision
Implementation for paper MLP-Mixer: An all-MLP Architecture for Vision
Implementation for paper MLP-Mixer: An all-MLP Architecture for Vision. MLP-Mixer, an architecture based exclusively on multi-layer perceptrons (MLPs). MLP-Mixer contains two types of layers: one with MLPs applied independently to image patches (i.e. "mixing" the per-location features), and one with MLPs applied across patches (i.e. "mixing" spa…
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