datasets
jax-models
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| datasets | jax-models | |
|---|---|---|
| 5 | 6 | |
| 4,175 | 138 | |
| 1.5% | - | |
| 9.4 | 0.0 | |
| 4 days ago | almost 2 years 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.
datasets
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TensorFlow Datasets (TFDS): a collection of ready-to-use datasets
I tried Librispeech, a very common dataset for speech recognition, in both HF and TFDS.
TFDS performed extremely bad.
First it failed because the official hosting server only allows 5 simultaneous connections, and TFDS totally ignored that and makes up to 50 simultaneous downloads and that breaks. I wonder if anyone actually tested this?
Then you need to have some computer with 30GB to do the preparation, which might fail on your computer. This is where I stopped. https://github.com/tensorflow/datasets/issues/3887. It might be fixed now but it took them 8 months to respond to my issue.
On HF, it just worked. There was a smaller issue in how the dataset was split up but that is fixed now, and their response was very fast and great.
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We built a pi controlled hydroponics box that grows your plants 1.5x faster using ML
but it looks like none of your plants are supported by the plantvillage model, or do I understand something wrong? https://github.com/tensorflow/datasets/blob/master/tensorflow_datasets/image_classification/plant_village.py#L57
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Voice Recognition with Tensorflow
To do our example, we're going to use some audio files released by Google.
jax-models
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[D] How to contribute to open source ML and DL without having access to high quality setup?
I was in the same position as you are and the best thing you can do is to start reproducing papers (that's what I did with jax-models). This will
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[D] Should We Be Using JAX in 2022?
I've been using JAX, especially Flax for quite some time now for my reproducibility initiative (jax_models) and this is what I really appreciate about the framework
- Weekly updated open sourced model implementations in Flax
- Weekly updated open sourced deep learning model implementations in Flax
- [P] Weekly updated open sourced model implementations in Flax
What are some alternatives?
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
flax - Flax is a neural network library for JAX that is designed for flexibility.
flaxmodels - Pretrained deep learning models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet, etc.
FedScale - FedScale is a scalable and extensible open-source federated learning (FL) platform.
GradCache - Run Effective Large Batch Contrastive Learning Beyond GPU/TPU Memory Constraint
trax - Trax — Deep Learning with Clear Code and Speed
elegy - A High Level API for Deep Learning in JAX
jaxopt - Hardware accelerated, batchable and differentiable optimizers in JAX.
equinox - Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
ESC-50 - ESC-50: Dataset for Environmental Sound Classification
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more