Instructions to use zeromodels/efficientdet_d7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use zeromodels/efficientdet_d7 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/efficientdet_d7") - Notebooks
- Google Colab
- Kaggle
| { | |
| "library_name": "zeromodels", | |
| "zeromodels_version": "1.2.6", | |
| "model_module": "zeromodels.models.efficientdet", | |
| "model_class": "EfficientDetModel", | |
| "variant": "efficientdet_d7", | |
| "weights": "model.weights.h5", | |
| "schema_version": 2, | |
| "weight_dtype": "float32", | |
| "model_type": "efficientdet", | |
| "vision_config": { | |
| "backbone_name": "efficientnet_b6", | |
| "image_size": 1536, | |
| "num_classes": 90, | |
| "min_level": 3, | |
| "max_level": 7, | |
| "num_scales": 3, | |
| "aspect_ratios": [ | |
| 1.0, | |
| 2.0, | |
| 0.5 | |
| ], | |
| "anchor_scale": 5.0, | |
| "fpn_num_filters": 384, | |
| "fpn_cell_repeats": 8, | |
| "box_class_repeats": 5, | |
| "act_type": "swish", | |
| "separable_conv": true, | |
| "apply_bn_for_resampling": true, | |
| "conv_after_downsample": false, | |
| "conv_bn_act_pattern": false, | |
| "fpn_weight_method": "sum", | |
| "survival_prob": null | |
| } | |
| } |