Instructions to use zeromodels/eomt_large_coco_instance_640 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ZeroModels
How to use zeromodels/eomt_large_coco_instance_640 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/eomt_large_coco_instance_640") - Keras
How to use zeromodels/eomt_large_coco_instance_640 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/eomt_large_coco_instance_640") - Notebooks
- Google Colab
- Kaggle
Upload kf_preprocessor.json with huggingface_hub
Browse files- kf_preprocessor.json +19 -0
kf_preprocessor.json
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{
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"library_name": "kerasformers",
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"kerasformers_version": "1.1.3",
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"preprocessor_module": "kerasformers.models.eomt",
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"preprocessor_class": "EoMTImageProcessor",
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"variant": "eomt_large_coco_instance_640",
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"target_size": 640,
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"image_mean": [
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0.485,
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0.456,
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0.406
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],
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"image_std": [
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0.229,
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0.224,
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0.225
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],
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"data_format": null
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}
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