DavidLandup0/deepvision

PyTorch and TensorFlow/Keras image models with automatic weight conversions and equal API/implementations - Vision Transformer (ViT), ResNetV2, EfficientNetV2, NeRF, SegFormer, MixTransformer, (planned...) DeepLabV3+, ConvNeXtV2, YOLO, etc.

38
/ 100
Emerging

This project helps machine learning engineers and researchers quickly build and train computer vision models. It takes raw image data and outputs trained models for tasks like image classification and semantic segmentation. The key benefit is allowing seamless switching between TensorFlow and PyTorch frameworks, using the same model architectures and easily converting weights.

No commits in the last 6 months.

Use this if you are a machine learning practitioner who needs to develop computer vision applications and wants the flexibility to work across both TensorFlow and PyTorch without rewriting model code or worrying about framework compatibility.

Not ideal if you are new to deep learning or only ever work within a single machine learning framework like a specific version of TensorFlow or PyTorch.

image classification semantic segmentation computer vision engineering deep learning model training framework interoperability
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 14 / 25

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Stars

42

Forks

7

Language

Python

License

Apache-2.0

Last pushed

Jul 01, 2023

Commits (30d)

0

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