Instructions to use KRAFTON/Raon-VisionEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KRAFTON/Raon-VisionEncoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="KRAFTON/Raon-VisionEncoder", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("KRAFTON/Raon-VisionEncoder", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 253 Bytes
acd771b | 1 2 3 4 5 6 | # Originally from OpenCLIP (https://github.com/mlfoundations/open_clip)
OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)
INCEPTION_MEAN = (0.5, 0.5, 0.5)
INCEPTION_STD = (0.5, 0.5, 0.5)
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