imagenette
pytorch-optimizer
imagenette | pytorch-optimizer | |
---|---|---|
9 | 3 | |
877 | 2,948 | |
0.0% | - | |
0.0 | 3.1 | |
over 1 year ago | about 2 months ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | Apache License 2.0 |
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imagenette
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[P] Graph path traversal with semantic graphs
This idea isn't exclusive to text, the same can be done for images. See this example from the imagenette dataset.
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Ask HN: In 2022, what is the proper way to get into machine/deep learning?
FastAI has ready-to-run code that does just this. They seem to have an ImageNet package https://github.com/fastai/imagenette
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How can I download ImageNet dataset with only 20 or 30 classes?
Imagenette is a smaller subset with only 10 classes. https://github.com/fastai/imagenette
You can try this https://github.com/fastai/imagenette which is subset of the main dataset.
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[D] How can I download ImageNet dataset with only 20 or 30 classes?
Download entire imagenet and annotations. Filter out all annotations that do not contain the classes you're interested in. Consider imagenette.
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[R] Dataset for research paper
You can try Imagenette, which is a 10-class subset of ImageNet but with the same number of images per class. There are also two datasets linked in the README for increased difficulty.
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ResNet from scratch - ImageNet
If you're looking for something more bite-sized, how about Imagenette?
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[D] Tiny-Imagenet original size images
Fastai has made something like that. Does this fit the bill https://github.com/fastai/imagenette?
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[R] AdasOptimizer Update: Cifar-100+MobileNetV2 Adas generalizes with Adas 15% better and 9x faster than Adam
You don't need Imagenet to verify it really works or not, Checkout https://github.com/fastai/imagenette the fastai folks have a small subset of Imagenet, which has 3 types of the dataset, test on them. If AdasOptimizer really works it you should be able to beat their results, or at least see where it stands.
pytorch-optimizer
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[D]: Implementation: Deconvolutional Paragraph Representation Learning
The specific implementation is from (here)[https://github.com/jettify/pytorch-optimizer] since pytorch doesn't have it directly.
- VQGAN+CLIP : "RAdam" from torch_optimizer could not be imported ?
- [R] AdasOptimizer Update: Cifar-100+MobileNetV2 Adas generalizes with Adas 15% better and 9x faster than Adam
What are some alternatives?
tiny-imagenet
sam - SAM: Sharpness-Aware Minimization (PyTorch)
DemonRangerOptimizer - Quasi Hyperbolic Rectified DEMON Adam/Amsgrad with AdaMod, Gradient Centralization, Lookahead, iterative averaging and decorrelated Weight Decay
AdasOptimizer - ADAS is short for Adaptive Step Size, it's an optimizer that unlike other optimizers that just normalize the derivative, it fine-tunes the step size, truly making step size scheduling obsolete, achieving state-of-the-art training performance
VQGAN-CLIP - Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
simple-sam - Sharpness-Aware Minimization for Efficiently Improving Generalization
RAdam - On the Variance of the Adaptive Learning Rate and Beyond
PythonPID_Tuner - Python PID Tuner - Based on a FOPDT model obtained using a Open Loop Process Reaction Curve