A colab notebook for training Stylegan2-ada on colab, transfer learning onto your own dataset.

Last update: Dec 16, 2022

Overview

Stylegan2-Ada-Google-Colab-Starter-Notebook

A no thrills colab notebook for training Stylegan2-ada on colab.

transfer learning onto your own dataset has never been easier :)

If you appreciate my work send all crypto donations in Eth, Bnb, Matic, Avax etc on any chain to : 0xe0a09b49721FBD8B23c67a3a9fdE44be4412B8fD

Owner

Harnick Khera

Code created with the power of coffee

GitHub Repository

💊 A 3D Generative Model for Structure-Based Drug Design (NeurIPS 2021)

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FCOS: Fully Convolutional One-Stage Object Detection (ICCV'19)

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Implementation of SETR model, Original paper: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.

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Official Codes for Graph Modularity:Towards Understanding the Cross-Layer Transition of Feature Representations in Deep Neural Networks.

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A colab notebook for training Stylegan2-ada on colab, transfer learning onto your own dataset.

Related tags

Overview

Stylegan2-Ada-Google-Colab-Starter-Notebook

Owner

Harnick Khera

💊 A 3D Generative Model for Structure-Based Drug Design (NeurIPS 2021)

FCOS: Fully Convolutional One-Stage Object Detection (ICCV'19)

MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks.

Dynamic Bottleneck for Robust Self-Supervised Exploration

Adaptive Dropblock Enhanced GenerativeAdversarial Networks for Hyperspectral Image Classification

Stacked Hourglass Network with a Multi-level Attention Mechanism: Where to Look for Intervertebral Disc Labeling

Implementation of the final project of the course DDA6309 Probabilistic Graphical Model

A simple but complete full-attention transformer with a set of promising experimental features from various papers

PyTorch/TorchScript compiler for NVIDIA GPUs using TensorRT

Explaining Hyperparameter Optimization via PDPs

Implementation of SETR model, Original paper: Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.

Official Codes for Graph Modularity:Towards Understanding the Cross-Layer Transition of Feature Representations in Deep Neural Networks.

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