Instructions to use kerasformers/eomt_large_coco_instance_640 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasFormers
How to use kerasformers/eomt_large_coco_instance_640 with KerasFormers:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Keras
How to use kerasformers/eomt_large_coco_instance_640 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://kerasformers/eomt_large_coco_instance_640") - Notebooks
- Google Colab
- Kaggle
File size: 660 Bytes
a8d772f | 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 | {
"library_name": "kerasformers",
"kerasformers_version": "1.1.3",
"model_module": "kerasformers.models.eomt",
"model_class": "EoMTUniversalSegment",
"variant": "eomt_large_coco_instance_640",
"weights": "model.weights.h5",
"model_type": "eomt",
"hidden_dim": 1024,
"num_hidden_layers": 24,
"num_heads": 16,
"depths": 4,
"num_queries": 200,
"num_classes": 80,
"layerscale_value": 1e-05,
"patch_size": 16,
"num_register_tokens": 4,
"num_upscale_blocks": 2,
"mlp_ratio": 4,
"drop_path_rate": 0.0,
"attention_dropout": 0.0,
"use_swiglu_ffn": false,
"layer_norm_eps": 1e-06,
"image_size": 640
} |