vision_transformer
TorchSharp
| vision_transformer | TorchSharp | |
|---|---|---|
| 7 | 5 | |
| 9,318 | 1,244 | |
| 2.5% | 3.2% | |
| 5.5 | 9.5 | |
| about 2 months ago | 4 days ago | |
| Jupyter Notebook | C# | |
| Apache License 2.0 | MIT License |
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vision_transformer
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Can I use CLIP to tag my picture collection?
And one last thing, should I even be thinking of using CLIP for these tasks when Google has released a better model here: https://github.com/google-research/vision_transformer/blob/main/model_cards/lit.md
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When the client's management is happy but their dev team is a pain
Google's vision transformers are type hinted.
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Improving Search Quality for Non-English Queries with Fine-tuned Multilingual CLIP Models
We’re going to look at a model that Open AI has trained with a broad multilingual dataset: The xlm-roberta-base-ViT-B-32 CLIP model, which uses the ViT-B/32image encoder, and the XLM-RoBERTa multilingual language model. Both of these are pre-trained:
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[R] How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
JAX Code: https://github.com/google-research/vision_transformer
- [D] (Paper Overview) MLP-Mixer: An all-MLP Architecture for Vision
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[P] Animesion: a framework, for anime (and related) character recognition. It uses Vision Transformers trained on a subset of Danbooru2018, that we rebranded as DAF:re, and can classify a given image into one of more than 3000 characters! Source code and checkpoints included.
For this project I used the pretrained models released by Google in Jax, using this particular PyTorch custom implementation. Those were pretrained on ImageNet21k with 14 M images among 21 K classes. Then yes I finetune on two datasets: one with 15 K images and 170 characters, and one with 3 K characters and almost 500 K images.
- Short term memory solutions for video tasks?
TorchSharp
- AI .NET
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Machine learning with NET
There exist TorchSharp that is a С# wrapper around the same libtorch.dll that is wrapped by Python code in PyTorch, https://github.com/dotnet/TorchSharp. Though I don't know how exhaustive it is.
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When the client's management is happy but their dev team is a pain
https://github.com/dotnet/TorchSharp here you go.
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Does anyone actually use ML.NET?
With TorchSharp, you have access to libtorch in .NET, the library that powers PyTorch. This is what's currently backing the Text Classification API
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.NET Core
In Artificial Intelligence Series-Overview
TorchSharp is a .NET library that provides access to libraries that support PyTorch. (Github https://github.com/xamarin/TorchSharp)
What are some alternatives?
pytorch-image-models - PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
TensorFlow.NET - .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.
nerfstudio - A collaboration friendly studio for NeRFs
NumSharp - High Performance Computation for N-D Tensors in .NET, similar API to NumPy.
ImageNet21K - Official Pytorch Implementation of: "ImageNet-21K Pretraining for the Masses"(NeurIPS, 2021) paper
TensorFlowSharp - TensorFlow API for .NET languages
Fashion12K_german_queries
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
fashion-200k - Fashion 200K dataset used in paper "Automatic Spatially-aware Fashion Concept Discovery."
DotMLBooks
typeshed - Collection of library stubs for Python, with static types