Instructions to use zeromodels/res2net50_26w_8s_in1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ZeroModels
How to use zeromodels/res2net50_26w_8s_in1k 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/res2net50_26w_8s_in1k") - Keras
How to use zeromodels/res2net50_26w_8s_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/res2net50_26w_8s_in1k") - Notebooks
- Google Colab
- Kaggle
Migrate to zeromodels (rename kf_*.json -> zm_*.json, fix refs in config + README, ensure tag + badge)
d60066e verified | { | |
| "library_name": "zeromodels", | |
| "zeromodels_version": "1.2.1", | |
| "model_module": "zeromodels.models.res2net", | |
| "model_class": "Res2NetImageClassify", | |
| "variant": "res2net50_26w_8s_in1k", | |
| "weights": "model.weights.h5", | |
| "schema_version": 2, | |
| "weight_dtype": "float32", | |
| "model_type": "res2net", | |
| "vision_config": { | |
| "depth": [ | |
| 3, | |
| 4, | |
| 6, | |
| 3 | |
| ], | |
| "base_width": 26, | |
| "scale": 8, | |
| "cardinality": 1, | |
| "num_classes": 1000 | |
| } | |
| } |