|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Transformers||112,514||64||1,869||3 hours ago||114||July 18, 2023||838||apache-2.0||Python|
|🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.|
|Pytorch||71,144||3,341||6,728||2 hours ago||37||May 08, 2023||12,781||other||Python|
|Tensors and Dynamic neural networks in Python with strong GPU acceleration|
|Keras||59,440||578||4 hours ago||80||June 27, 2023||96||apache-2.0||Python|
|Deep Learning for humans|
|Yolov5||41,861||2 days ago||8||September 21, 2021||228||agpl-3.0||Python|
|YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite|
|Annotated_deep_learning_paper_implementations||36,223||1||7 days ago||78||September 24, 2022||27||mit||Jupyter Notebook|
|🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠|
|Made With Ml||34,182||6 days ago||5||May 15, 2019||2||mit||Jupyter Notebook|
|Learn how to design, develop, deploy and iterate on production-grade ML applications.|
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|DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.|
|Ray||27,881||80||298||7 hours ago||87||July 24, 2023||3,435||apache-2.0||Python|
|Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.|
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|Deep learning framework to train, deploy, and ship AI products Lightning fast.|
|Fastai||24,551||184||145||10 hours ago||146||March 28, 2023||186||apache-2.0||Jupyter Notebook|
|The fastai deep learning library|
The aim for this repository is to contain clean, readable and tested code to reproduce few-shot learning research.
This project is written in python 3.6 and Pytorch and assumes you have a GPU.
See these Medium articles for some more information
requirements.txt. Install with
pip install -r requirements.txt preferably in a virtualenv.
DATA_PATH variable in
config.py to the location where
you store the Omniglot and miniImagenet datasets.
After acquiring the data and running the setup scripts your folder structure should look like
DATA_PATH/ Omniglot/ images_background/ images_evaluation/ miniImageNet/ images_background/ images_evaluation/
Omniglot dataset. Download from https://github.com/brendenlake/omniglot/tree/master/python,
place the extracted files into
DATA_PATH/Omniglot_Raw and run
miniImageNet dataset. Download files from
data/miniImageNet/images and run
After adding the datasets run
pytest in the root directory to run
experiments/experiments.txt contains the hyperparameters I
used to obtain the results given below.
experiments/proto_nets.py to reproduce results from Prototpyical
Networks for Few-shot Learning
(Snell et al).
A differentiable nearest neighbours classifier.
experiments/matching_nets.py to reproduce results from Matching
Networks for One Shot Learning
(Vinyals et al).
I had trouble reproducing the results of this paper using the cosine distance metric as I found the converge to be slow and final performance dependent on the random initialisation. However I was able to reproduce (and slightly exceed) the results of this paper using the l2 distance metric.
|This Repo (cosine)||92.0||93.2||75.6||77.8|
|This Repo (l2)||98.3||99.8||92.8||97.8|
|Published (cosine, FCE)||44.2||57.0|
|This Repo (cosine, FCE)||42.8||53.6|
|This Repo (l2)||46.0||58.4|
I used max pooling instead of strided convolutions in order to be consistent with the other papers. The miniImageNet experiments using 2nd order MAML took me over a day to run.
experiments/maml.py to reproduce results from Model-Agnostic
(Finn et al).
NB: For MAML n, k and q are fixed between train and test. You may need to adjust meta-batch-size to fit your GPU. 2nd order MAML uses a lot more memory.
|This Repo (1)||95.5||99.5||92.2||97.7|
|This Repo (2)||98.1||99.8||91.6||95.9|
|This Repo (1)||46.4||63.3|
|This Repo (2)||47.5||64.7|
Number in brackets indicates 1st or 2nd order MAML.