efficientnet
image-quality-assessment
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| efficientnet | image-quality-assessment | |
|---|---|---|
| 9 | 2 | |
| 2,057 | 2,000 | |
| - | 1.9% | |
| 0.0 | 0.0 | |
| 3 months ago | 4 months 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.
image-quality-assessment
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And so it begins: AI(Midjourney) wins art competition without anyone realising.
It's an ongoing research area, but here's a Google model that assigns images ratings based on how good they look. It's pretty good, and it's from 2019 so not even close to state of the art. Paired with Stable Diffusion, it could indeed curate itself. I might have to try that actually.
- Extracting Images from Video
What are some alternatives?
mmpretrain - OpenMMLab Pre-training Toolbox and Benchmark
student-teacher-anomaly-detection - Student–Teacher Anomaly Detection with Discriminative Latent Embeddings
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
GLOM-TensorFlow - An attempt at the implementation of GLOM, Geoffrey Hinton's paper for emergent part-whole hierarchies from data
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
DarkMark - Marking up images for use with Darknet.
models - Models and examples built with TensorFlow
imagededup - 😎 Finding duplicate images made easy!
PaddleClas - A treasure chest for visual classification and recognition powered by PaddlePaddle
These-People-Do-Not-Exist - AI that generates human faces which have never been seen before. The future is now 😁
models - A collection of pre-trained, state-of-the-art models in the ONNX format
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.