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Top 23 Python pretrained-model Projects
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Project mention: Huggingface Self Supervised ViT Scripts | reddit.com/r/learnmachinelearning | 2023-01-27
I'm interested in using SSL with ViT on my custom data. I'm trying to run the examples here https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining specifically the run-mae.py script. If I use the cifar10 default example, everything runs fine so I know that my environment is set up correctly.
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pytorch-image-models
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more
The other thing is, try to find tricks to speed up your experiments (if not having done so already). The most obvious are mixed precision training, have your model train on a lower resolution input first and then increase the resolution later in the training, stochastic depth, and a bunch more stuffs. Look for implementations in https://github.com/rwightman/pytorch-image-models .
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InfluxDB
Build time-series-based applications quickly and at scale.. InfluxDB is the Time Series Platform where developers build real-time applications for analytics, IoT and cloud-native services. Easy to start, it is available in the cloud or on-premises.
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Project mention: A stem splitting algorythm update would be cool! | reddit.com/r/KoalaSampler | 2023-01-26
I think Koala is using Spleeter. There's also an open source alternative called Demucs, which yields far better results, especially on vocals and drums.
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I used this with the popular pytorch implementation of EfficientNet. You can see what I'm talking about here https://github.com/lukemelas/EfficientNet-PyTorch/blob/master/efficientnet_pytorch/model.py on line 127. Once you understand this code it is pretty straightforward to modify your forward pass to allow "stochastic depth" during inference.
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PaddleNLP
👑 Easy-to-use and powerful NLP library with 🤗 Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including 🗂Text Classification, 🔍 Neural Search, ❓ Question Answering, ℹ️ Information Extraction, 📄 Document Intelligence, 💌 Sentiment Analysis and 🖼 Diffusion AIGC system etc.
PaddleNLP
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This basic example from segmentation models PyTorch repo would be good tutorial to start with. The library is very good, I like the unet, fpn and deeplabv3+ architectures with regnety as encoder https://github.com/qubvel/segmentation_models.pytorch/blob/master/examples/binary_segmentation_intro.ipynb
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mmf, which is a multimodal pytorch framework by facebook research, was released around 2-3 years ago and is now poorly maintained.
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Sonar
Write Clean Python Code. Always.. Sonar helps you commit clean code every time. With over 225 unique rules to find Python bugs, code smells & vulnerabilities, Sonar finds the issues while you focus on the work.
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facenet-pytorch
Pretrained Pytorch face detection (MTCNN) and facial recognition (InceptionResnet) models
For now, I am trying to evaluate and get the accuracy of the FaceNet module. Like this example on facenet-pytorch, getting the accuracy relies on this file (pairs.txt) provided by the official site. Format description below:
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Efficient-AI-Backbones
Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.
Project mention: Researchers From China Introduce Vision GNN (ViG): A Graph Neural Network For Computer Vision Systems | reddit.com/r/machinelearningnews | 2022-06-08Continue reading | Check out the paper, github
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Project mention: A break down of Depth Guided StableDiffusion 2 though visualizing internals | reddit.com/r/computervision | 2022-12-21
The unet is also conditioned by an encoding of the text prompt. The tokenizer first maps the positive and negate prompts into vectors of 77 token indices, which are then encoded into embedding tensors using OpenCLIP-ViT/H.
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PINTO_model_zoo
A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
Download the ONNX model from this project: 307_YOLOv7
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pyannote-audio
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
Project mention: I won several speaker diarization challenges with pyannote.audio | news.ycombinator.com | 2022-12-02 -
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FARM
:house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.
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gen-efficientnet-pytorch
Pretrained EfficientNet, EfficientNet-Lite, MixNet, MobileNetV3 / V2, MNASNet A1 and B1, FBNet, Single-Path NAS
Project mention: My depth2mask extension is ready (works with 1.5 and the webui) | reddit.com/r/StableDiffusion | 2022-11-25if cache isn't there, you can also download it from https://github.com/rwightman/gen-efficientnet-pytorch
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CogView
Text-to-Image generation. The repo for NeurIPS 2021 paper "CogView: Mastering Text-to-Image Generation via Transformers".
Project mention: CogView2 web app is available at site replicate.com | reddit.com/r/MediaSynthesis | 2022-06-18This web app, which is mentioned in the CogView (1) GitHub repo, is/was using a "slightly different" model than the CogView2 GitHub repo.
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deepsparse
Inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application
Project mention: [D] How to get the fastest PyTorch inference and what is the "best" model serving framework? | reddit.com/r/MachineLearning | 2022-10-28For 1), what is the easiest way to speed up inference (assume only PyTorch and primarily GPU but also some CPU)? I have been using ONNX and Torchscript but there is a bit of a learning curve and sometimes it can be tricky to get the model to actually work. Is there anything else worth trying? I am enthused by things like TorchDynamo (although I have not tested it extensively) due to its apparent ease of use. I also saw the post yesterday about Kernl using (OpenAI) Triton kernels to speed up transformer models which also looks interesting. Are things like SageMaker Neo or NeuralMagic worth trying? My only reservation with some of these is they still seem to be pretty model/architecture specific. I am a little reluctant to put much time into these unless I know others have had some success first.
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Project mention: Fine-tuning open source models to emulate ChatGPT for code explanation. | reddit.com/r/learnmachinelearning | 2023-01-27
One option I’m considering is using fine tuners like the one from HuggingFace or Jina AI to fine-tune open source models like GPT-J or OPT to improve specific use-cases like code explanation. With the funding that we have, I wouldn’t want to cheap out on fine-tuning and expect something good.
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Project mention: [P] Football Player 3D Pose Estimation using YOLOv7 | reddit.com/r/MachineLearning | 2022-12-20
You can try trt_pose rather than YOLO. It's super fast. I am also doing 3D pose estimation, and with trt_pose I get the 2D at more than 100fps. https://github.com/NVIDIA-AI-IOT/trt_pose
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CogView2
official code repo for paper "CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers"
Project mention: Wiskkey's lists of text-to-image systems and related resources | reddit.com/r/bigsleep | 2022-09-10(Tier 2) CogView2.
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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).
The latest post mention was on 2023-01-27.
Python pretrained-models related posts
- Fine-tuning open source models to emulate ChatGPT for code explanation.
- Supervised Image Classifiers and Out Of Band Input Images?
- How would you approach this kind of Info/Entity extraction problem?
- Efficient way to tune a network by changing hyperparameters?
- Need help with prompt engineering and fine tuning
- Help with Fine-Tuning for eCommerce/listing data
- How do I fine tune a large amount of data?
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Index
What are some of the best open-source pretrained-model projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | transformers | 78,809 |
| 2 | pytorch-image-models | 23,140 |
| 3 | spleeter | 21,484 |
| 4 | EfficientNet-PyTorch | 7,259 |
| 5 | PaddleNLP | 7,177 |
| 6 | segmentation_models.pytorch | 6,694 |
| 7 | mmf | 5,122 |
| 8 | PaddleClas | 4,635 |
| 9 | facenet-pytorch | 3,272 |
| 10 | Efficient-AI-Backbones | 3,016 |
| 11 | open_clip | 2,933 |
| 12 | PINTO_model_zoo | 2,406 |
| 13 | pyannote-audio | 2,214 |
| 14 | efficientnet | 1,992 |
| 15 | asteroid | 1,635 |
| 16 | FARM | 1,617 |
| 17 | gen-efficientnet-pytorch | 1,507 |
| 18 | CogView | 1,276 |
| 19 | deepsparse | 1,240 |
| 20 | BERT-NER | 1,079 |
| 21 | finetuner | 819 |
| 22 | trt_pose | 807 |
| 23 | CogView2 | 805 |
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