Zero-Shot Image Classification
Transformers
Safetensors
English
Chinese
fgclip2
text-generation
clip
custom_code
Instructions to use qihoo360/fg-clip2-so400m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qihoo360/fg-clip2-so400m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="qihoo360/fg-clip2-so400m", trust_remote_code=True) 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 AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("qihoo360/fg-clip2-so400m", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from qihoo360/fg-clip2-so400m: direct link, hf CLI and curl.
- Browser
- Download file 4.24 MB
-
https://huggingface.co/qihoo360/fg-clip2-so400m/resolve/main/tokenizer.model
- Command line
-
hf download hf://qihoo360/fg-clip2-so400m/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/qihoo360/fg-clip2-so400m/resolve/main/tokenizer.model
4.24 MB
- Xet hash:
- 4747d621061ff864406560d79c8c3e355c05f2e88ba30eba88c8d5881d3d220d
- Size of remote file:
- 4.24 MB
- SHA256:
- 61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
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