efficientnet
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| efficientnet | PaddleClas | |
|---|---|---|
| 9 | 2 | |
| 2,057 | 5,251 | |
| - | 1.2% | |
| 0.0 | 5.6 | |
| 3 months ago | 12 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.
PaddleClas
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Baidu AI Research Team Introduces ‘PP-ShiTu’: A Practical Lightweight Image Recognition System
Quick 5 Min Read | Paper | Github
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Baidu Research Introduces PP-LCNet: A Lightweight CPU Convolutional Neural Network With Better Accuracy And Performance
3 Min Quick Read | Paper| Github PaddleClas
What are some alternatives?
mmpretrain - OpenMMLab Pre-training Toolbox and Benchmark
Swin-Transformer-Object-Detection - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
Swin-Transformer-Tensorflow - Unofficial implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" (https://arxiv.org/abs/2103.14030)
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
Swin-Transformer-Semantic-Segmentation - This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Semantic Segmentation.
models - Models and examples built with TensorFlow
Video-Swin-Transformer - This is an official implementation for "Video Swin Transformers".
models - A collection of pre-trained, state-of-the-art models in the ONNX format
DWPose - "Effective Whole-body Pose Estimation with Two-stages Distillation" (ICCV 2023, CV4Metaverse Workshop)
image-quality-assessment - Convolutional Neural Networks to predict the aesthetic and technical quality of images.
HugsVision - HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision