Image Feature Extraction
Transformers
ONNX
siglip
zero-shot-image-classification
robotics
edge-deployment
anima
forge
int8
quantized
vision
zero-shot
ros2
jetson
real-time
Eval Results (legacy)
Instructions to use robotflowlabs/siglip-so400m-patch14-384-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use robotflowlabs/siglip-so400m-patch14-384-int8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="robotflowlabs/siglip-so400m-patch14-384-int8")# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("robotflowlabs/siglip-so400m-patch14-384-int8") model = AutoModelForZeroShotImageClassification.from_pretrained("robotflowlabs/siglip-so400m-patch14-384-int8") - Notebooks
- Google Colab
- Kaggle
| { | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "SiglipImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "processor_class": "SiglipProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 384, | |
| "width": 384 | |
| } | |
| } | |