Zero-Shot Image Classification
Transformers
Safetensors
glance_image
feature-extraction
glance
statement-tuning
image-text-matching
custom_code
Instructions to use stefra/blip-large-glance-image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stefra/blip-large-glance-image with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="stefra/blip-large-glance-image", trust_remote_code=True) pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("stefra/blip-large-glance-image", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download processor_config.json from stefra/blip-large-glance-image: direct link, hf CLI and curl.
- Browser
- Download file 544 Bytes
-
https://huggingface.co/stefra/blip-large-glance-image/resolve/main/processor_config.json
- Command line
-
hf download hf://stefra/blip-large-glance-image/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/stefra/blip-large-glance-image/resolve/main/processor_config.json
544 Bytes
| { | |
| "image_processor": { | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "BlipImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 384, | |
| "width": 384 | |
| }, | |
| "size_divisor": 32 | |
| }, | |
| "processor_class": "BlipProcessor" | |
| } | |