Instructions to use keras/dfine_small_obj2coco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/dfine_small_obj2coco with KerasHub:
import keras_hub # Create a ObjectDetector model task = keras_hub.models.ObjectDetector.from_preset("hf://keras/dfine_small_obj2coco")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/dfine_small_obj2coco") - Keras
How to use keras/dfine_small_obj2coco with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://keras/dfine_small_obj2coco") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dcddf22193af24d802fa15fbcee8b60203407a25e8ff1cc00f1ed5d6fa5bc893
- Size of remote file:
- 43.1 MB
- SHA256:
- 60ec70a5d24ed508311a66eb8d9bb6889f62456f65172e93cab5ceed296e6ce7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.