efficientnet
gemma_pytorch
| efficientnet | gemma_pytorch | |
|---|---|---|
| 9 | 6 | |
| 2,058 | 5,034 | |
| - | 3.2% | |
| 0.0 | 7.7 | |
| 3 months ago | 18 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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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.
gemma_pytorch
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Getting Started with Gemma Models
Gemma is a family of lightweight, open-source machine learning models developed by Google AI. These models are designed to be accessible and efficient, making AI development more available for a broad range of users. Released on February 21st, 2024, Gemma is built from the same research and technology that was used to create the Gemini models. Amongst the key features, which are being lightweight and open-source, Gemma is also text-based. It excels in tasks like text summarization, question answering, and reasoning.
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Gemma doesn't suck anymore – 8 bug fixes
Here are the missing links:
* Gemma, a family of open models from Google: https://ai.google.dev/gemma
* Unsloth is a tool/method for training models faster (IIUC): https://github.com/unslothai/unsloth
- The official PyTorch implementation of Google's Gemma models
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Gemma: New Open Models
The release page has comparisons to Mistral everywhere: https://ai.google.dev/gemma
What are some alternatives?
mmpretrain - OpenMMLab Pre-training Toolbox and Benchmark
llama.cpp - LLM inference in C/C++
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
gemma - Open weights LLM from Google DeepMind.
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
models - Models and examples built with TensorFlow
gemma.cpp - lightweight, standalone C++ inference engine for Google's Gemma models.
PaddleClas - A treasure chest for visual classification and recognition powered by PaddlePaddle
text-to-text-transfer-transformer - Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"
models - A collection of pre-trained, state-of-the-art models in the ONNX format
ai-on-gke