Instructions to use keras/dfine_small_coco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/dfine_small_coco with KerasHub:
import keras_hub # Create a ObjectDetector model task = keras_hub.models.ObjectDetector.from_preset("hf://keras/dfine_small_coco")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/dfine_small_coco") - Keras
How to use keras/dfine_small_coco 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_coco") - Notebooks
- Google Colab
- Kaggle
File size: 1,587 Bytes
d1115bb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | {
"module": "keras_hub.src.models.d_fine.d_fine_object_detector_preprocessor",
"class_name": "DFineObjectDetectorPreprocessor",
"config": {
"name": "d_fine_object_detector_preprocessor",
"trainable": true,
"dtype": {
"module": "keras",
"class_name": "DTypePolicy",
"config": {
"name": "float32"
},
"registered_name": null
},
"image_converter": {
"module": "keras_hub.src.models.d_fine.d_fine_image_converter",
"class_name": "DFineImageConverter",
"config": {
"name": "d_fine_image_converter",
"trainable": true,
"dtype": {
"module": "keras",
"class_name": "DTypePolicy",
"config": {
"name": "float32"
},
"registered_name": null
},
"image_size": [
640,
640
],
"scale": 0.00392156862745098,
"offset": null,
"interpolation": "bilinear",
"antialias": false,
"crop_to_aspect_ratio": true,
"pad_to_aspect_ratio": false,
"bounding_box_format": "yxyx"
},
"registered_name": "keras_hub>DFineImageConverter"
},
"config_file": "preprocessor.json"
},
"registered_name": "keras_hub>DFineObjectDetectorPreprocessor"
} |