Closed NielsRogge closed 4 months ago
That's awesome! Thank you very much for integrating our omdet-turbo. This is exactly what we intended to do. I will merge this commit.
Thanks so much for merging my PR! Could you try out running the add_hf.py
script so that download metrics will work for your model? 🤗 (feel free to push to https://huggingface.co/omlab/OmDet-Turbo_tiny_SWIN_T rather than my user account, it will just create a new commit)
!pip install huggingface_hub
!huggingface-cli login
python add_hf.py
Dear @P3ngLiu and team,
Thanks for this nice work! I see you already pushed the model to the hub which is great: https://huggingface.co/omlab/OmDet-Turbo_tiny_SWIN_T/tree/main, however currently download metrics aren't working for your model + there are no tags in the model card which means it's hard for people to find it.
I wrote a quick PoC to showcase that you can easily have integration with the 🤗 hub so that you can automatically load the OmDet model using
from_pretrained
(and push it usingpush_to_hub
), track download number (similar to models in the Transformers library), and have a nice model card. It leverages the PyTorchModelHubMixin class which allows to inherits these methods.Usage is as follows:
This means people don't need to manually download a checkpoint first in their local environment, it just loads it automatically from the hub. The
safetensors
format is used to ensure safe serialization of the weights rather than pickle.To improve discoverability, we can add a "zero-shot-object-detection" tag to the model card similar to the ones here: https://huggingface.co/models?pipeline_tag=zero-shot-object-detection&sort=trending. I opened a PR for that here: https://huggingface.co/omlab/OmDet-Turbo_tiny_SWIN_T/discussions/1
Would you be interested in this integration?
Kind regards,
Niels ML Engineer @ HF 🤗