robust-models-transfer
Official repository for our NeurIPS 2020 *oral* "Do Adversarially Robust ImageNet Models Transfer Better?" (by microsoft)
onnx
Open standard for machine learning interoperability (by onnx)
| robust-models-transfer | onnx | |
|---|---|---|
| 1 | 38 | |
| 239 | 16,959 | |
| - | 1.6% | |
| 10.0 | 9.5 | |
| 11 months ago | 6 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
robust-models-transfer
Posts with mentions or reviews of robust-models-transfer.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-09-18.
-
38TB of data accidentally exposed by Microsoft AI researchers
Looks like it was up for 2 years with that old link[1]. Fixed two months ago.
[1] https://github.com/microsoft/robust-models-transfer/blame/a9...
onnx
Posts with mentions or reviews of onnx.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-12-01.
- Onyx, a new programming language powered by WebAssembly
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From Lab to Live: Implementing Open-Source AI Models for Real-Time Unsupervised Anomaly Detection in Images
Once your model has been trained and validated using Anomalib, the next step is to prepare it for real-time implementation. This is where ONNX (Open Neural Network Exchange) or OpenVINO (Open Visual Inference and Neural network Optimization) comes into play.
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Object detection with ONNX, Pipeless and a YOLO model
ONNX is an open format from the Linux Foundation to represent machine learning models. It is becoming extensively adopted by the Machine Learning community and is compatible with most of the machine learning frameworks like PyTorch, TensorFlow, etc. Converting a model between any of those formats and ONNX is really simple and can be done in most cases with a single command.
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38TB of data accidentally exposed by Microsoft AI researchers
ONNX[0], model-as-protosbufs, continuing to gain adoption will hopefully solve this issue.
[0] https://github.com/onnx/onnx
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Reddit’s LLM text model for Ads Safety
Running inference for large models on CPU is not a new problem and fortunately there has been great development in many different optimization frameworks for speeding up matrix and tensor computations on CPU. We explored multiple optimization frameworks and methods to improve latency, namely TorchScript, BetterTransformer and ONNX.
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Operationalize TensorFlow Models With ML.NET
ONNX is a format for representing machine learning models in a portable way. Additionally, ONNX models can be easily optimized and thus become smaller and faster.
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Onnx Runtime: “Cross-Platform Accelerated Machine Learning”
I would say onnx.ai [0] provides more information about ONNX for those who aren’t working with ML/DL.
[0] https://onnx.ai
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Does ONNX Runtime not support Double/float64?
It's not clear why you thing this sub is appropriate for some third party system with a Python interface. Why don't you try their discussion group: https://github.com/onnx/onnx/discussions
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Async behaviour in python web frameworks
This kind of indirection through standardisation is pretty common to make compatibility between different kinds of software components easier. Some other good examples are the LSP project from Microsoft and ONNX to represent machine learning models. The first provides a standard so that IDEs don't have to re-invent the weel for every programming language. The latter decouples training frameworks from inference frameworks. Going back to WSGI, you can find a pretty extensive rationale for the WSGI standard here if interested.
- Pickle safety in Python
What are some alternatives?
When comparing robust-models-transfer and onnx you can also consider the following projects:
modelscan - Protection against Model Serialization Attacks
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
stable-diffusion-webui - Stable Diffusion web UI
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/Sygil-Dev/sygil-webui]
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
stable-diffusion - A latent text-to-image diffusion model
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/sd-webui/stable-diffusion-webui]
iree - A retargetable MLIR-based machine learning compiler and runtime toolkit.
tensorflow-directml - Fork of TensorFlow accelerated by DirectML
hummingbird - Hummingbird compiles trained ML models into tensor computation for faster inference.
invisible-watermark - python library for invisible image watermark (blind image watermark)
dvc - 🦉 ML Experiments and Data Management with Git
robust-models-transfer vs modelscan
onnx vs onnxruntime
onnx vs stable-diffusion-webui
onnx vs stable-diffusion-webui
onnx vs sentence-transformers
onnx vs stable-diffusion
onnx vs stable-diffusion-webui
onnx vs iree
onnx vs tensorflow-directml
onnx vs hummingbird
onnx vs invisible-watermark
onnx vs dvc