Instructions to use zeromodels/efficientdet_d2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ZeroModels
How to use zeromodels/efficientdet_d2 with ZeroModels:
# pip install -U zeromodels # ZeroModels is pure Keras 3, so pick a backend: "jax", "torch" or "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" from zeromodels import AutoZModel # AutoZModel reads the repo's model_type and loads the matching class. # For a task head use the matching loader, e.g. AutoZMImageClassify / AutoZMDetect / # AutoZMSemanticSegment / AutoZMTextGenerate (see zeromodels.auto). model = AutoZModel.from_weights("zeromodels/efficientdet_d2") - Keras
How to use zeromodels/efficientdet_d2 with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://zeromodels/efficientdet_d2") - Notebooks
- Google Colab
- Kaggle
Download model.weights.h5 from zeromodels/efficientdet_d2: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/zeromodels/efficientdet_d2/resolve/main/model.weights.h5
- Command line
-
hf download hf://zeromodels/efficientdet_d2/model.weights.h5
-
curl -L -o model.weights.h5 https://huggingface.co/zeromodels/efficientdet_d2/resolve/main/model.weights.h5
34.4 MB
- Xet hash:
- b41b7a428f9d82f1fa32e9e023717c6a1dfd761c7daa3de2165030d05efcc143
- Size of remote file:
- 34.4 MB
- SHA256:
- 83536f6e8373901b5de103dec6db76ade2e5d264d9786c1828b67ff1665b6013
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