Instructions to use HanzhiZhang/lowResSegModel_Object with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HanzhiZhang/lowResSegModel_Object with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="HanzhiZhang/lowResSegModel_Object")# Load model directly from transformers import AutoProcessor, AutoModelForMaskGeneration processor = AutoProcessor.from_pretrained("HanzhiZhang/lowResSegModel_Object") model = AutoModelForMaskGeneration.from_pretrained("HanzhiZhang/lowResSegModel_Object", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 468 Bytes
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"_name_or_path": "facebook/sam-vit-base",
"architectures": [
"SamModel"
],
"initializer_range": 0.02,
"mask_decoder_config": {
"model_type": ""
},
"model_type": "sam",
"prompt_encoder_config": {
"model_type": ""
},
"torch_dtype": "float32",
"transformers_version": "4.41.2",
"vision_config": {
"dropout": 0.0,
"initializer_factor": 1.0,
"intermediate_size": 6144,
"model_type": "",
"projection_dim": 512
}
}
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