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
File size: 902 Bytes
23ea8fe | 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 | {
"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
}
} |