Zero-Shot Image Classification
Transformers
Safetensors
tipsv2
image-feature-extraction
vision
image-text
contrastive-learning
zero-shot
feature-extraction
custom_code
Instructions to use google/tipsv2-so400m14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/tipsv2-so400m14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="google/tipsv2-so400m14", 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 AutoModel model = AutoModel.from_pretrained("google/tipsv2-so400m14", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update files for transformers integration
#1
by guarin HF Staff - opened
Updates the repo to smoothly integrate with transformers.
PR: https://github.com/huggingface/transformers/pull/46347
Changes:
- Update
config.jsonto match transformers structure - Update config classes to handle new
config.json - Add processor and tokenizer configs
- Update DPT
model.safetensorsfiles to include DPT head + backbone weights - Update DPT model code to handle extra weights in
model.safetensorsfile