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Top 23 image-classification Open-Source Projects
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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
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
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label-studio
Label Studio is a multi-type data labeling and annotation tool with standardized output format
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albumentations
Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
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Swin-Transformer
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
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pytorch-grad-cam
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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awesome-project-ideas
Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas
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super-gradients
Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
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notebooks
Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.
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deepdetect
Deep Learning API and Server in C++14 support for Caffe, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE
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sparseml
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
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SaaSHub
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Project mention: The CEO of Ultralytics (yolov8) using LLMs to engage with commenters on GitHub | news.ycombinator.com | 2024-02-12Yep, I noticed this a while ago. It posts easily identifiable ChatGPT responses. It also posts garbage wrong answers which makes it worse than useless. Totally disrespectful to the userbase.
https://github.com/ultralytics/ultralytics/issues/5748#issue...
Project mention: Is it easier to go from Pytorch to TF and Keras than the other way around? | /r/pytorch | 2023-05-13I also need to learn Pyspark so right now I am going to download the Fashion Mnist dataset, use Pyspark to downsize each image and put the into separate folders according to their labels (just to show employers I can do some basic ETL with Pyspark, not sure how I am going to load for training in Pytorch yet though). Then I am going to write the simplest Le Net to try to categorize the fashion MNIST dataset (results will most likely be bad but it's okay). Next, try to learn transfer learning in Pytorch for both CNN or maybe skip ahead to ViT. Ideally at this point I want to study the Attention mechanism a bit more and try to implement Simple Vit which I saw here: https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/simple_vit.py
14. LabelStudio by Human Signal | Github | tutorial
You can use albumentations if you are comfortable with using open source libraries https://github.com/albumentations-team/albumentations
Project mention: Samsung expected to report 80% profit plunge as losses mount at chip business | news.ycombinator.com | 2023-10-10> there is really nothing that "normal" AI requires that is bound to CUDA. pyTorch and Tensorflow are backend agnostic (ideally...).
There are a lot of optimizations that CUDA has that are nowhere near supported in other software or even hardware. Custom cuda kernels also aren't as rare as one might think, they will often just be hidden unless you're looking at libraries. Our more well known example is going to be StyleGAN[0] but it isn't uncommon to see elsewhere, even in research code. Swin even has a cuda kernel[1]. Or find torch here[1] (which github reports that 4% of the code is cuda (and 42% C++ and 2% C)). These things are everywhere. I don't think pytorch and tensorflow could ever be agnostic, there will always be a difference just because you have to spend resources differently (developing kernels is time resource). We can draw evidence by looking at Intel MKL, which is still better than open source libraries and has been so for a long time.
I really do want AMD to compete in this space. I'd even love a third player like Intel. We really do need competition here, but it would be naive to think that there's going to be a quick catchup here. AMD has a lot of work to do and posting a few bounties and starting a company (idk, called "micro grad"?) isn't going to solve the problem anytime soon.
And fwiw, I'm willing to bet that most AI companies would rather run in house servers than from cloud service providers. The truth is that right now just publishing is extremely correlated to compute infrastructure (doesn't need to be but with all the noise we've just said "fuck the poor" because rejecting is easy) and anyone building products has costly infrastructure.
[0] https://github.com/NVlabs/stylegan2-ada-pytorch/blob/d72cc7d...
[1] https://github.com/microsoft/Swin-Transformer/blob/2cb103f2d...
[2] https://github.com/pytorch/pytorch/tree/main/aten/src
For the two examples we will be looking at, we will be using pytorch_grad_cam, an incredible open source package that makes working with GradCam very easy. There are excellent other tutorials to check out on the repo as well.
Sorry about that! I should have checked before sharing that link.
It looks like Microsoft published the code on GitHub, so you might be able to deploy it via Azure. (I haven't tried it.)
https://github.com/Microsoft/ailab/blob/master/Sketch2Code/R...
Most computer vision models are trained to predict on a preset list of label classes. In object detection, for instance, many of the most popular models like YOLOv8 and YOLO-NAS are pretrained with the classes from the MS COCO dataset. If you download the weights checkpoints for these models and run prediction on your dataset, you will generate object detection bounding boxes for the 80 COCO classes.
Yeah, inference[1] is our open source package for running locally (either directly in Python or via a Docker container). It works with all the models on Universe, models you train yourself (assuming we support the architecture; we have a bunch of notebooks available[2]), or train in our platform, plus several more general foundation models[3] (for things like embeddings, zero-shot detection, question answering, OCR, etc).
We also have a hosted API[4] you can hit for most models we support (except some of the large vision models that are really GPU-heavy) if you prefer.
[1] https://github.com/roboflow/inference
[2] https://github.com/roboflow/notebooks
[3] https://inference.roboflow.com/foundation/about/
[4] https://docs.roboflow.com/deploy/hosted-api
Project mention: Instance segmentation of small objects in grainy drone imagery | /r/computervision | 2023-12-09
Project mention: Exploring Open-Source Alternatives to Landing AI for Robust MLOps | dev.to | 2023-12-13For those seeking a lightweight solution for setting up deep learning REST APIs across platforms without the complexity of Kubernetes, Deepdetect is worth considering.
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.
Project mention: Camera-Related Package that works well with C# in Raspberry Pi OS 64-Bit or any other Arm64 device | /r/dotnet | 2023-05-23
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
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Index
What are some of the best open-source image-classification projects? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | pytorch-image-models | 29,751 |
| 2 | ultralytics | 22,624 |
| 3 | vit-pytorch | 17,910 |
| 4 | label-studio | 16,469 |
| 5 | albumentations | 13,395 |
| 6 | Swin-Transformer | 12,917 |
| 7 | pytorch-grad-cam | 9,410 |
| 8 | techniques | 7,739 |
| 9 | ailab | 7,628 |
| 10 | awesome-project-ideas | 7,404 |
| 11 | autogluon | 7,091 |
| 12 | gluon-cv | 5,751 |
| 13 | PaddleClas | 5,251 |
| 14 | super-gradients | 4,322 |
| 15 | notebooks | 4,134 |
| 16 | hub | 3,436 |
| 17 | catalyst | 3,223 |
| 18 | mmpretrain | 3,156 |
| 19 | deepdetect | 2,493 |
| 20 | efficientnet | 2,057 |
| 21 | Emgu CV | 1,990 |
| 22 | sparseml | 1,974 |
| 23 | ailia-models | 1,814 |
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