efficientnet
models
Our great sponsors
efficientnet | models | |
---|---|---|
9 | 7 | |
2,057 | 7,192 | |
- | 3.6% | |
0.0 | 4.8 | |
3 months ago | 8 days ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
efficientnet
-
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.
-
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.
-
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?
-
[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.
-
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.
-
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.
models
-
AMD Accelerates AI Adoption on Windows 11 With New Developer Tools for Ryzen AI
Uh, maybe they didn't feel the need to look. I already pointed you to the ONNX project. Here are some ONNX-based. These are just the ones being shared with the community. The limit of AMD's responsibility is writing the low-level libraries to support ONNX.
-
Need Help With Darknet YOLOv4-Tiny Model In Unity Barracuda
I am new to object detection models and I need help running my object detection Darknet YOLOv4-Tiny Model In Unity Barracuda. I trained my model and then i converted it to ONNX format with 2 methods. One method was using pytorch-YOLOv4 from github and the other by converting my model to tensorflow and then to onnx and shown here: "https://github.com/onnx/models/blob/main/vision/object_detection_segmentation/yolov4/dependencies/Conversion.ipynb"
-
Need Help Converting Darknet Yolov4-tiny Model to ONNX
Then i tried to convert it again using another method that i found here "https://github.com/onnx/models/blob/main/vision/object_detection_segmentation/yolov4/dependencies/Conversion.ipynb" in order to convert it from darknet to tensorflow and then to onnx but i didn't have any luck.
-
Text generation with GPT-2 in Ruby
Here we use the GPT-2 model distributed by the ONNX official. Download GPT-2-LM-HEAD from the link.
-
YOLOv7 object detection in Ruby in 10 minutes
Download pre-trained models from the ONNX Model Zoo
-
Has anyone successfully converted an onnx model to tensorflow? Here's the problems I'm having...
Instructions to reproduce the problem: I am trying to convert a proprietary model at work but for now i'll use mobilenetv2-7.onnx to explain/reproduce the issue.
-
How to identify identical frames that are not technically duplicates? Ie if I am taking a video of a car, it stops for 1 minute (and within that minute nothing changes visually), and then drives away. How would I remove all but 1 of the frames when it is stopped?
One approach could be run a pre-trained object detector (like one of these) on each frame and then a simple object tracker on top of it (like this).
What are some alternatives?
mmpretrain - OpenMMLab Pre-training Toolbox and Benchmark
SSD-Mobilenet-Custom-Object-Detector-Model-using-Tensorflow-2 - This repository contains the script and process to create custom SSD Mobilenet model for object detection
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
netron - Visualizer for neural network, deep learning and machine learning models
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
onnx-tensorflow - Tensorflow Backend for ONNX
models - Models and examples built with TensorFlow
redisai-examples - RedisAI showcase
PaddleClas - A treasure chest for visual classification and recognition powered by PaddlePaddle
TensorFlow-Examples - TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
image-quality-assessment - Convolutional Neural Networks to predict the aesthetic and technical quality of images.
tensorboard - TensorFlow's Visualization Toolkit