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"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("sartifyllc/AViLaMa") model = AutoModel.from_pretrained("sartifyllc/AViLaMa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 319 Bytes
6fef168 77621a7 6fef168 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"crop_size": 336,
"do_center_crop": true,
"do_normalize": true,
"do_resize": true,
"feature_extractor_type": "ViTFeatureExtractor",
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"resample": 3,
"size": 336
} |