Zero-Shot Image Classification
Transformers
Safetensors
flexict
feature-extraction
medical-imaging
ct
vision-language
custom_code
Instructions to use ricklisz123/FlexiCT-3D-VLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ricklisz123/FlexiCT-3D-VLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="ricklisz123/FlexiCT-3D-VLM", trust_remote_code=True) pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ricklisz123/FlexiCT-3D-VLM", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 617 Bytes
bd37ec6 | 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 | {
"_processor_class": null,
"auto_map": {
"AutoImageProcessor": "image_processing_flexict.FlexiCTImageProcessor",
"AutoProcessor": "processing_flexict.FlexiCTVLMProcessor"
},
"clip_range": [
-1000.0,
1000.0
],
"do_orient_lps": true,
"do_resample": true,
"eps": 1e-06,
"image_processor_type": "FlexiCTImageProcessor",
"image_size": [
160,
160,
160
],
"max_length": 8192,
"model_variant": "vlm",
"preset": "default",
"processor_class": "FlexiCTVLMProcessor",
"target_spacing": [
2.0,
2.0,
2.0
],
"text_model_id": "Qwen/Qwen3-Embedding-0.6B"
}
|