Zero-Shot Image Classification
Transformers
Safetensors
English
Chinese
fgclip2
text-generation
clip
custom_code
Instructions to use qihoo360/fg-clip2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qihoo360/fg-clip2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="qihoo360/fg-clip2-base", trust_remote_code=True) pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("qihoo360/fg-clip2-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 171fd18deb3c085ebcefe9ca08157b0d666c91b5fb4d2a0f99fb682593371c09
- Size of remote file:
- 34.4 MB
- SHA256:
- 58a1696e79c9d97937389ed116f552a15c84811d7b8023918b86f4bc5775b1b0
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