Keras Python Packages

tqdm

:zap: A Fast, Extensible Progress Bar for Python and CLI

wandb

The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

onnx

Open standard for machine learning interoperability

onnx2tf

A tool for converting ONNX files to LiteRT/TFLite/TensorFlow, PyTorch native code (nn.Module), TorchScript (.pt), state_dict (.pt), Exported Program (.pt2), and Dynamo ONNX. It also supports direct conversion from LiteRT to PyTorch.

onnxmltools

ONNXMLTools enables conversion of models to ONNX

torchinfo

View model summaries in PyTorch!

tensorflow-decision-forests

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

onnx-weekly

Open standard for machine learning interoperability

tf2onnx

Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX

scikeras

Scikit-Learn API wrapper for Keras.

tdqm

:zap: A Fast, Extensible Progress Bar for Python and CLI

ncnn

ncnn is a high-performance neural network inference framework optimized for the mobile platform

pnnx

ncnn is a high-performance neural network inference framework optimized for the mobile platform

keras-tuner

A Hyperparameter Tuning Library for Keras

neptune

📘 The experiment tracker for foundation model training

nudenet

Lightweight nudity detection

keras-ocr

A packaged and flexible version of the CRAFT text detector and Keras CRNN recognition model.

torchsummary

Model summary in PyTorch similar to `model.summary()` in Keras

netron

Visualizer for neural network, deep learning and machine learning models

tensorflow-model-optimization

A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

fast-plate-ocr

Lightweight & fast OCR models for license plate text recognition.

keras-nlp-nightly

Pretrained model hub for Keras 3.

torch-summary

View model summaries in PyTorch!

neptune-client

📘 The experiment tracker for foundation model training