node-efficientnet
efficientnet
| node-efficientnet | efficientnet | |
|---|---|---|
| 133 | 9 | |
| 250 | 2,058 | |
| - | - | |
| 0.0 | 0.0 | |
| 3 months ago | 3 months ago | |
| TypeScript | Python | |
| MIT License | Apache License 2.0 |
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node-efficientnet
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.
What are some alternatives?
face-api.js - JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js
mmpretrain - OpenMMLab Pre-training Toolbox and Benchmark
nsfw-filter - A free, open source, and privacy-focused browser extension to block “not safe for work” content built using TypeScript and TensorFlow.js.
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
TFLiteClassification - TensorFlow Lite Image Classification Python Implementation
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
cvat - Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale. [Moved to: https://github.com/cvat-ai/cvat]
models - Models and examples built with TensorFlow
pigallery - PiGallery: AI-powered Self-hosted Secure Multi-user Image Gallery and Detailed Image analysis using Machine Learning, EXIF Parsing and Geo Tagging
PaddleClas - A treasure chest for visual classification and recognition powered by PaddlePaddle
models - A collection of pre-trained, state-of-the-art models in the ONNX format
image-quality-assessment - Convolutional Neural Networks to predict the aesthetic and technical quality of images.