Image Feature Extraction
Transformers
Safetensors
English
Chinese
minicpmv
histopathology
multimodal
spatial-transcriptomics
custom_code
Instructions to use openbmb/SciCore-Omics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/SciCore-Omics with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="openbmb/SciCore-Omics", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openbmb/SciCore-Omics", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 836 Bytes
368a46d | 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 | {
"image_processor_type": "MiniCPMVImageProcessor",
"auto_map": {
"AutoProcessor": "processing_minicpmv.MiniCPMVProcessor",
"AutoImageProcessor": "image_processing_minicpmv.MiniCPMVImageProcessor"
},
"processor_class": "MiniCPMVProcessor",
"max_slice_nums": 9,
"scale_resolution": 448,
"patch_size": 14,
"use_image_id": true,
"image_feature_size": 64,
"im_start": "<image>",
"im_end": "</image>",
"gene_start": "<gene>",
"gene_end": "</gene>",
"slice_start": "<slice>",
"slice_end": "</slice>",
"unk": "<unk>",
"im_id_start": "<image_id>",
"im_id_end": "</image_id>",
"gene_id_start": "<gene_id>",
"gene_id_end": "</gene_id>",
"slice_mode": true,
"norm_mean": [0.5, 0.5, 0.5],
"norm_std": [0.5, 0.5, 0.5],
"version": 2.6
} |