Getting Started
Working with the examples is an easy way to get started with CoreNet.
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└── tutorials
├── train_a_new_model_on_a_new_dataset_from_scratch.ipynb
├── guide_slurm_and_multi_node_training.md
├── clip.ipynb
├── semantic_segmentation.ipynb
└── object_detection.ipynb
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Training Recipes
CoreNet provides reproducible training recipes, in addition to the pretrained model
weights and checkpoints for the publications that are listed in projects/ directory.
Publication project directories generally contain the following contents:
* `README.md` provides documentation, links to the pretrained weights, and citations.
* `/.yaml` provides configuration for reproducing the trainings and evaluations.
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└── projects
├── kv-prediction (*)
├── byteformer
├── catlip
├── clip
├── fastvit
├── mobilenet_v1
├── mobilenet_v2
├── mobilenet_v3
├── mobileone
├── mobilevit
├── mobilevit_v2
├── openelm
├── range_augment
├── resnet
└── vit
(*) Newly released.
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MLX Examples
MLX examples demonstrate how to run CoreNet models efficiently on Apple Silicon.
Please find further information in the README.md file within the corresponding example directory.
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└──mlx_example
├── clip
└── open_elm
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Model Implementations
Models are organized by tasks (e.g. "classification"). You can find all model implementations for each
task in the corresponding task folder.
Each model class is decorated by a
`@MODEL_REGISTRY.register(name="", type="")` decorator.
To use a model class in CoreNet training or evaluation,
assign `models..name = ` in the YAML configuration.
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└── corenet
└── modeling
└── models
├── audio_classification
├── classification
├── detection
├── language_modeling
├── multi_modal_img_text
└── segmentation
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Datasets
Similarly to the models, datasets are also categorized by tasks.
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└── corenet
└── data
└── datasets
├── audio_classification
├── classification
├── detection
├── language_modeling
├── multi_modal_img_text
└── segmentation
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Other key directories
In this section, we have highlighted the rest of the key directories that implement
classes corresponding to the names that are referenced in the YAML configurations.
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└── corenet
├── loss_fn
├── metrics
├── optims
│ └── scheduler
├── train_eval_pipelines
├── data
│ ├── collate_fns
│ ├── sampler
│ ├── text_tokenizer
│ ├── transforms
│ └── video_reader
└── modeling
├── layers
├── modules
├── neural_augmentor
└── text_encoders
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