efficientnet-lite-keras
pytorch2keras
efficientnet-lite-keras | pytorch2keras | |
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1 | 2 | |
38 | 846 | |
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0.0 | 0.0 | |
about 1 year ago | over 1 year ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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efficientnet-lite-keras
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[P] EfficientNet-lite in Keras (functional API).
Github Link
pytorch2keras
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Help Needed: Converting PlantNet-300k Pretrained Model Weights from Tar to h5 Format Help
It's almost certainly a pickled pytorch model so you will first need to load it using pytorch and then write it out to h5 (legacy keras format) with https://github.com/gmalivenko/pytorch2keras.
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Is Pytorch good deployment wise?
But of course, you can always train a model with one framework and run inference with another. For instance, pytorch2keras does exactly this.
What are some alternatives?
segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
NudeNet - Neural Nets for Nudity Detection and Censoring
ssd_keras - A Keras port of Single Shot MultiBox Detector
InvoiceNet - Deep neural network to extract intelligent information from invoice documents.
GLOM-TensorFlow - An attempt at the implementation of GLOM, Geoffrey Hinton's paper for emergent part-whole hierarchies from data
facenet-pytorch - Pretrained Pytorch face detection (MTCNN) and facial recognition (InceptionResnet) models
PINTO_model_zoo - A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
onnx-tensorflow - Tensorflow Backend for ONNX
tpu - Reference models and tools for Cloud TPUs.
cnn-watermark-removal - Fully convolutional deep neural network to remove transparent overlays from images
nobuco - Pytorch to Keras/Tensorflow conversion made intuitive
TensorFlow-object-detection-tutorial - The purpose of this tutorial is to learn how to install and prepare TensorFlow framework to train your own convolutional neural network object detection classifier for multiple objects, starting from scratch