CoreNet is a deep neural network toolkit that allows researchers and engineers to train standard and novel small and large-scale models for variety of

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2024-04-24 01:30:02

CoreNet is a deep neural network toolkit that allows researchers and engineers to train standard and novel small and large-scale models for variety of tasks, including foundation models (e.g., CLIP and LLM), object classification, object detection, and semantic segmentation.

You will need Git LFS (instructions below) to run tests and Jupyter notebooks (instructions) in this repository, and to contribute to it so we recommend that you install and activate it first.

On Linux we recommend to use Python 3.10+ and PyTorch (version >= v2.1.0), on macOS system Python 3.9+ should be sufficient.

Note that on macOS the file system is case insensitive, and case sensitivity can cause issues with Git. You should access the repository on disk as if the path were case sensitive, i.e. with the same capitalization as you see when you list the directories ls. You can switch to such a path with the cd $(pwd -P) command.

Each model class is decorated by a @MODEL_REGISTRY.register(name="<model_name>", type="<task_name>") decorator. To use a model class in CoreNet training or evaluation, assign moels.<task_name>.name = <model_name> in the YAML configuration.