efficientnet
stylegan2-projecting-images
efficientnet | stylegan2-projecting-images | |
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9 | 135 | |
2,058 | 282 | |
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0.0 | 10.0 | |
3 months ago | about 1 year ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | MIT License |
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efficientnet
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Getting Started with Gemma Models
Examples of lightweight models include MobileNet, a computer vision model designed for mobile and embedded vision applications, EfficientDet, an object detection model, and EfficientNet, a CNN that uses compound scaling to enable better performance. All these are lightweight models from Google.
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How did you make that?!
There was a recent paper by Facebook (2022), where they modernise a vanilla ConvNet by using the latest empirical design choices and manage to achieve state-of-the-art performance with it. This was also done before, with EffecientNet in 2019.
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Why did the original ResNet paper not use dropout?
not true at all, plenty of sota models combines batchnorm and dropout 1. efficientnet 2. resnet rs 3. timm resnet50 (appendix)
- Increasing Model Dimensionality
- [D] How does one choose a learning rate schedule for models that take days or weeks to train?
- [D] What's the intuition behind certain CNN architectures?
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[D] What are some interesting hidden stuff about CNNs?
Right - I think these days they do more of a balanced tradeoff between width and depth. One more recent CNN, Efficientnet, carefully chooses the width-to-depth ratio to have the best performance for a given compute budget.
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I made an image recognition model written in NodeJs
EfficientNet a lightweight convolutional neural network architecture achieving the state-of-the-art accuracy with an order of magnitude fewer parameters and FLOPS, on both ImageNet and five other commonly used transfer learning datasets.
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Training custom EfficientNet from scratch (greyscale)
Additionally, if you want to custom change the number of filters in the EfficientNet I would suggest using the detailed Keras implementation of the EfficientNet in this repository.
stylegan2-projecting-images
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Getting Started with Gemma Models
A Colab notebook.
- Welcome to Colaboratory
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A playground to practice differential privacy - Antigranular
To play with the dataset, we first must create a Jupyter notebook, a powerful and popular tool among data engineers. I created mine on Google Colab.
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Topic and Subtopic Extraction with the Google Gemini Pro
Please head over to the Google Colab
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How do I begin building AI tools for myself?
But regardless of what you want to do, you'll probably use Python. In this context, a good way to work with Python is using Jupyter Notebooks. So you should start with installing Python and Jupyter and go from there. If you want to get started without installing anything, Google Colab gives you a remote Jupyter Notebook which runs in the browser for free.
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教程:使用 Google Colab 安全地转发 B 站视频
访问 Google Colab 。
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Journey into Jupyter Notebooks: A Beginner's Guide
Remember school days when you'd share notes with classmates? Jupyter takes that spirit and amplifies it. Once you've crafted your Notebook, you can share it with peers, collaborators, and the world. Platforms like GitHub and Google's Colab natively render Jupyter Notebooks. It's like penning an open letter to the world but in a delightful mix of code, text, and visuals.
- This feels like an obvious question, but if I load a pickle file that is 1GB in size, is it taking up 1GB of memory?
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Leveraging Google Colab to run Postgres: A Comprehensive Guide
Open your web browser and navigate to Google Colab.
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No excuses to start working with Python
Using Google Colab you can develop Python codes, similar to Jupyter Notebooks. You will have an environment prepared with various Python libraries. In addition you have tips on small codes for development, some tutorials, gihub connection, cloud -saved notebooks and more.
What are some alternatives?
mmpretrain - OpenMMLab Pre-training Toolbox and Benchmark
fast-stable-diffusion - fast-stable-diffusion + DreamBooth
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
stable-diffusion-webui-colab - stable diffusion webui colab
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
gimp-stable-diffusion
models - Models and examples built with TensorFlow
discoart - 🪩 Create Disco Diffusion artworks in one line
PaddleClas - A treasure chest for visual classification and recognition powered by PaddlePaddle
quickstart-android - Firebase Quickstart Samples for Android
models - A collection of pre-trained, state-of-the-art models in the ONNX format
comfyui-colab - comfyui colabs templates new nodes