Instructions to use kerasformers/tf_efficientnetv2_b3_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasFormers
How to use kerasformers/tf_efficientnetv2_b3_in1k with KerasFormers:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Keras
How to use kerasformers/tf_efficientnetv2_b3_in1k with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://kerasformers/tf_efficientnetv2_b3_in1k") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- README.md +24 -0
- model.weights.h5 +3 -0
README.md
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---
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pipeline_tag: image-classification
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license: apache-2.0
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base_model: timm/tf_efficientnetv2_b3.in1k
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library_name: kerasformers
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tags:
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- keras
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- kerasformers
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- image-classification
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- efficientnetv2
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---
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# tf_efficientnetv2_b3_in1k
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Pure-Keras 3 image-classification weight for [kerasformers](https://github.com/IMvision12/KerasFormers), converted from [timm/tf_efficientnetv2_b3.in1k](https://huggingface.co/timm/tf_efficientnetv2_b3.in1k).
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## Usage
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```python
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from kerasformers.models.efficientnetv2 import EfficientNetV2ImageClassify
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model = EfficientNetV2ImageClassify.from_weights("tf_efficientnetv2_b3_in1k")
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```
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License: **apache-2.0**, inherited from the upstream source.
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model.weights.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:c925c4b4aac09bfc0c9c486cf4cd4d89b4ba1cbbd894565280cf48aad0feee07
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size 58915280
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