Image Feature Extraction
Transformers
Safetensors
kanana2_vision_encoder
feature-extraction
vision-encoder
custom_code
Instructions to use kakaocorp/kanana-2-vision-encoder-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kakaocorp/kanana-2-vision-encoder-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="kakaocorp/kanana-2-vision-encoder-tiny", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kakaocorp/kanana-2-vision-encoder-tiny", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from kakaocorp/kanana-2-vision-encoder-tiny: direct link, hf CLI and curl.
- Browser
- Download file 296 Bytes
-
https://huggingface.co/kakaocorp/kanana-2-vision-encoder-tiny/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://kakaocorp/kanana-2-vision-encoder-tiny/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/kakaocorp/kanana-2-vision-encoder-tiny/resolve/main/preprocessor_config.json
296 Bytes
| { | |
| "auto_map": { | |
| "AutoImageProcessor": "image_processing_kanana2_ve.Kanana2VisionEncoderImageProcessor" | |
| }, | |
| "min_pixels": 102400, | |
| "max_pixels": 1638400, | |
| "image_mean": [0.5, 0.5, 0.5], | |
| "image_std": [0.5, 0.5, 0.5], | |
| "patch_size": 16, | |
| "temporal_patch_size": 2, | |
| "merge_size": 2 | |
| } | |