efficientnet
mmpretrain
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efficientnet | mmpretrain | |
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9 | 2 | |
2,057 | 3,156 | |
- | 4.1% | |
0.0 | 7.8 | |
3 months ago | 6 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.
mmpretrain
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMClassification: OpenMMLab image classification toolbox and benchmark.
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how to recognize digits from this pics(i have many of them) so kindly suggest generic that can work for other similar images. I have searched alot for the source code on github but not found the correct solution. most of these solutions were incorrect while other were incomplete. Kindly help me :(
MMClassification or TIMM would be good starting points for training a classification model.
What are some alternatives?
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
ModelZoo.pytorch - Hands on Imagenet training. Unofficial ModelZoo project on Pytorch. MobileNetV3 Top1 75.64🌟 GhostNet1.3x 75.78🌟
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
senet.pytorch - PyTorch implementation of SENet
models - Models and examples built with TensorFlow
AdelaiDet - AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.
PaddleClas - A treasure chest for visual classification and recognition powered by PaddlePaddle
pytorch-image-models - PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
models - A collection of pre-trained, state-of-the-art models in the ONNX format
TFLiteClassification - TensorFlow Lite Image Classification Python Implementation
image-quality-assessment - Convolutional Neural Networks to predict the aesthetic and technical quality of images.
biprop - Identify a binary weight or binary weight and activation subnetwork within a randomly initialized network by only pruning and binarizing the network.