netron
models
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netron | models | |
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32 | 7 | |
26,040 | 7,158 | |
- | 3.1% | |
9.9 | 5.2 | |
7 days ago | 6 days ago | |
JavaScript | Jupyter Notebook | |
MIT License | 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.
netron
- Visualizer for neural network, deep learning and machine learning models
- Netron: Visualizer for Machine Learning Models
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
In exploring open-source projects, I've come across several promising tools capable of managing deep-learning models for images. Significantly, tools such as NETRON provide visualization of neural networks, while SHAP can be used for evaluating the significance of outputs.
- Netron is a viewer for neural network, deep learning and machine learning models
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Operationalize TensorFlow Models With ML.NET
We need to find out the exact input and output tensor names. A tool like Netron makes this super easy. Open the original .tflite and/or the ONNX model in Netron and click the Model Properties button in the lower left corner.
- Netron: A viewer for neural network, deep learning and machine learning models
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Visualize PyTorch Models with NNViz
How is this different from e.g Netron https://github.com/lutzroeder/netron
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[P]Visualizing a neural network.
Netron (https://netron.app/) is the best and mostly used NN visualizer. Just save your model and then simply load it via netron to look its layers and weights. If you want a more complex visualization then you can also play with Zetane ( but its paid, also have a free version) engine.
- How do I visualize this NN Architecture?
- FLaNK Stack for 15 May 2023
models
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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.
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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"
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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.
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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.
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YOLOv7 object detection in Ruby in 10 minutes
Download pre-trained models from the ONNX Model Zoo
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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.
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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?
openpilot - openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for 250+ supported car makes and models.
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
ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform
onnx-tensorflow - Tensorflow Backend for ONNX
models - Models and examples built with TensorFlow
redisai-examples - RedisAI showcase
pwnagotchi - (⌐■_■) - Deep Reinforcement Learning instrumenting bettercap for WiFi pwning.
TensorFlow-Examples - TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
PlotNeuralNet - Latex code for making neural networks diagrams
tensorboard - TensorFlow's Visualization Toolkit
onnxruntime-ruby - Run ONNX models in Ruby