March-14-2024: Add support for Gemma (Keras 2.15 compatible).
March-05-2024: Add support for keras 3. Note that I still retain support for keras 2 and currently only support the TensorFlow backend. Support for Pytorch and JAX backends will be released at a later date.
January-14-2024: Add support for TensorFlow 2.15
January-11-2024: Add EVA02.
January-02-2024: Add DCNv3 and InternImage backbone.
April-01-2023: Add weights for ViT-SAM.
March-17-2023: Drop the support for old ResNet-50/101 h5 weights. Updated versions have been provided.
March-01-2023: Add support of TPU pod training, we will add an example project soon.
January-03-2023: Add support of ConvNeXtV2.
November-12-2022: Add support of MOAT.
September-7-2022: Drop support for TensorFlow < 2.10
March-16-2022: We add an example project in here, which is the offical implementation of the paper CAR: Class-aware Regularizations for Semantic Segmentation
March-16-2022: The backbone weights are now available in here, we will add more in future.
- Modern ResNet
- Xception-65
- MobileNetV2
- EfficientNetV1
- Feature Pyramid Network
- HRNet
- Vision Transformer
- Swin Transformer
- MobileNetV2
- ConvNeXt
- MOAT
- ConvNeXtV2
- InternImage
- EVA02
All backbones are independent of input size.
Weights can be downloaded in here
- Mixed precision training and inference
- Fully deterministic result (100%, see https://github.com/NVIDIA/framework-determinism)
- Training and inference on GPU <= 8
- Training and inference on TPU/TPU Pod
- Typical image augmentation
- Support for Windows 10/11
- Support for Windows WSL2
- Support for Apple M1 chip macOS
- Support Keras core, seamlessly use Pytorch and JAX backend.
- Added support for Panoptic segmentation and multimodality.
- TensorFlow >= 2.10 (For iseg <= 0.04, we support TensorFlow >= 2.4)
- Mixed precision only supports GPU architectures after Volta (included).
The following order can avoid many bugs. Make sure the NVIDIA and CUDA driver is the latest version.
conda create -n tf215 python=3.9 tqdm matplotlib gitpython -c conda-forge
pip install --upgrade pip setuptools
pip install --upgrade tensorrt
pip install tensorflow[and-cuda]==2.15 ml-dtypes
pip install tensorflow-text==2.15
pip install keras-nlp==0.8.2