efficientnet
labelImg
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| efficientnet | labelImg | |
|---|---|---|
| 9 | 13 | |
| 2,057 | 20,480 | |
| - | - | |
| 0.0 | 10.0 | |
| 3 months ago | 11 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT 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.
labelImg
What are some alternatives?
mmpretrain - OpenMMLab Pre-training Toolbox and Benchmark
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]
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
coco-annotator - :pencil2: Web-based image segmentation tool for object detection, localization, and keypoints
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
labelme - Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
models - Models and examples built with TensorFlow
VoTT - Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.
PaddleClas - A treasure chest for visual classification and recognition powered by PaddlePaddle
qStore - A proof of concept for using youtube as file storage.
models - A collection of pre-trained, state-of-the-art models in the ONNX format
awesome-public-datasets - A topic-centric list of HQ open datasets.