Zero-Shot Image Classification
Transformers
PyTorch
Safetensors
vision-text-dual-encoder
image generation
visual qa
text-image embedding
image-text embedding
sartify
visual conversional ai
image semantic retrival
african raw resourced languages
Instructions to use sartifyllc/AViLaMa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sartifyllc/AViLaMa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="sartifyllc/AViLaMa") 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, AutoModel processor = AutoProcessor.from_pretrained("sartifyllc/AViLaMa") model = AutoModel.from_pretrained("sartifyllc/AViLaMa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 660b7109624163889b0cc01f19598ca45f540e05ca8cc3fe8e4bcd7d8f3692c7
- Size of remote file:
- 13.6 MB
- SHA256:
- 7f4f8cebf07aea8174f1e075b4985c42f4354e6d81f9c5a59aef3ba6849c138a
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