CodeGeeX
pytorch-image-models
CodeGeeX | pytorch-image-models | |
---|---|---|
9 | 35 | |
7,786 | 29,828 | |
1.0% | 1.5% | |
2.0 | 9.4 | |
about 1 month ago | 6 days ago | |
Python | Python | |
Apache License 2.0 | 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.
CodeGeeX
- For Developers - THUDM/CodeGeeX: CodeGeeX: An Open Multilingual Code Generation Model
- Impressive open-source code generation model with 13 billion parameters, pre-trained on a large code corpus of more than 20 programming languages
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A List of AI Tools to Boost Productivity - 50+ Tools
Codegeex](https://codegeex.cn/)
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Is there an open source equivalent of GitHub co-pilot?
You can use CodeGeeX or AI Programmer
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Using CodeGeeX as a GitHub Copilot alternative
According to its official docs, “CodeGeeX is a large-scale multilingual code generation model with 13 billion parameters pre-trained on a large code corpus of more than 20 programming languages”. Simply put, CodeGeeX is a code generation tool powered by artificial intelligence that helps you write code faster.
- CodeGeeX: An Open Multilingual Code Generation Model (By Thudm)
- Just found a github copilot alternative, and it's free.
- A free copilot from China is trained entirely on Huawei chips
- CodeGeeX: An Open Multilingual Code Generative Model
pytorch-image-models
- FLaNK AI Weekly 18 March 2024
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[D] Hugging face and Timm
I am a PyTorch user I work in CV, I usually use the PyTorch models. However, I see people use timm in research papers to train their models I don't understand what it is timm is it a new framework like PyTorch? Further, when I click https://pypi.org/project/timm/ homepage it takes me to hugging face GitHub https://github.com/huggingface/pytorch-image-models is there any connection between timm and hugging face many of my friends use hugging face but I also don't know about hugging face I use simple PyTorch and torchvision.models.
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FLaNK Stack Weekly for 07August2023
https://github.com/huggingface/pytorch-image-models https://huggingface.co/docs/timm/index
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[R] Nvidia RTX 4090 ML benchmarks. Under QEMU/KVM. Image + Transformers. FP16/FP32.
pytorch-image-models
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Inference on resent, cant work out the problem?
additionally, you might find the timm library handy for this sort of work.
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Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows
This is still being pursued. Ross Wightmann's timm[0,1] package (now on Hugging Face) has done a lot of this. There's also a V2 of ConvNext[2]. Ross does write about this a lot on Twitter fwiw. I should also mention that there are still many transformer based networks that still beat convs. So there probably won't be a resurgence in convs until someone can show that there's a really strong reason for them. They have some advantages but they also might not be flexible enough for the long range tasks in segmentation and detection. But maybe they are.
FAIR definitely did great work with ConvNext, and I do hope to see more. There always needs to be people pushing unpopular paradigms.
[0] https://github.com/huggingface/pytorch-image-models
[1] https://arxiv.org/abs/2110.00476
[2] https://arxiv.org/abs/2301.00808
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Problems with Learning Rate Finder in Pytorch Lightning
I am doing Binary classification with a pre-trained EfficientNet tf_efficientnet_l2. I froze all weights during training and replaced the classifier with a custom trainable one that looks like:
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PyTorch at the Edge: Deploying Over 964 TIMM Models on Android with TorchScript and Flutter
In this post, I’m going to show you how you can pick from over 900+ SOTA models on TIMM, train them using best practices with Fastai, and deploy them on Android using Flutter.
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ImageNet Advise
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 .
- Doubt about transformers
What are some alternatives?
spleeter - Deezer source separation library including pretrained models.
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
mmdetection - OpenMMLab Detection Toolbox and Benchmark
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 [Moved to: https://github.com/huggingface/pytorch-image-models]
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
mmcv - OpenMMLab Computer Vision Foundation
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
yolact - A simple, fully convolutional model for real-time instance segmentation.
tensorflow-image-models - TensorFlow port of PyTorch Image Models (timm) - image models with pretrained weights.
vision_transformer