efficientnet
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| efficientnet | ast | |
|---|---|---|
| 9 | 1 | |
| 2,058 | 1,005 | |
| - | - | |
| 0.0 | 2.3 | |
| 3 months ago | 12 months ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" 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.
ast
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Increasing Model Dimensionality
Unless this is purely an educational exercise, just finetune an imagenet model, or better yet a model trained on spectrograms (cochleagrams if you can find one).
What are some alternatives?
mmpretrain - OpenMMLab Pre-training Toolbox and Benchmark
soxan - Wav2Vec for speech recognition, classification, and audio classification
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
CodeSearchNet - Datasets, tools, and benchmarks for representation learning of code.
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
SimCLR - PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
models - Models and examples built with TensorFlow
open-metric-learning - Library for metric learning pipelines and models.
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.
labelImg - LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source data labeling tool for images, text, hypertext, audio, video and time-series data.