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
File size: 544 Bytes
6ec75c7 | 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 | {
"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"
}
|