Instructions to use zeromodels/tf_efficientnet_b2_ap_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasFormers
How to use zeromodels/tf_efficientnet_b2_ap_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 zeromodels/tf_efficientnet_b2_ap_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://zeromodels/tf_efficientnet_b2_ap_in1k") - Notebooks
- Google Colab
- Kaggle
| { | |
| "library_name": "kerasformers", | |
| "kerasformers_version": "1.2.1", | |
| "model_module": "kerasformers.models.efficientnet", | |
| "model_class": "EfficientNetImageClassify", | |
| "variant": "tf_efficientnet_b2_ap_in1k", | |
| "weights": "model.weights.h5", | |
| "schema_version": 2, | |
| "weight_dtype": "float32", | |
| "model_type": "efficientnet", | |
| "vision_config": { | |
| "width_coefficient": 1.1, | |
| "depth_coefficient": 1.2, | |
| "dropout_rate": 0.3, | |
| "default_size": 260, | |
| "image_size": 260, | |
| "num_classes": 1000 | |
| } | |
| } |