Zero-Shot Image Classification
Transformers
ONNX
clip
robotics
edge-deployment
anima
forge
int8
quantized
vision
zero-shot
image-classification
ros2
jetson
real-time
Eval Results (legacy)
Instructions to use robotflowlabs/clip-vit-large-patch14-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use robotflowlabs/clip-vit-large-patch14-int8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="robotflowlabs/clip-vit-large-patch14-int8") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("robotflowlabs/clip-vit-large-patch14-int8") model = AutoModelForZeroShotImageClassification.from_pretrained("robotflowlabs/clip-vit-large-patch14-int8") - Notebooks
- Google Colab
- Kaggle
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