Image Feature Extraction
Transformers
Safetensors
English
keural_vision
vision
vision-encoder
image-text
contrastive-learning
knowledge-distillation
adaptive-tokenization
Eval Results (legacy)
Instructions to use mkd-hika/keural-vision-encoder-mid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mkd-hika/keural-vision-encoder-mid with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="mkd-hika/keural-vision-encoder-mid")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mkd-hika/keural-vision-encoder-mid", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download benchmark_comparison_chart.png from mkd-hika/keural-vision-encoder-mid: direct link, hf CLI and curl.
- Browser
- Download file 273 kB
-
https://huggingface.co/mkd-hika/keural-vision-encoder-mid/resolve/main/benchmark_comparison_chart.png
- Command line
-
hf download hf://mkd-hika/keural-vision-encoder-mid/benchmark_comparison_chart.png
-
curl -L -o benchmark_comparison_chart.png https://huggingface.co/mkd-hika/keural-vision-encoder-mid/resolve/main/benchmark_comparison_chart.png
273 kB

- Xet hash:
- 6be1510d6742812af0881dbfd3dec97e3ec7b117d95c216812b2c36115b8f0db
- Size of remote file:
- 273 kB
- SHA256:
- 521a525e1a2a2ce5b40c6f845260931e603323254130079d78fd9e59428585d8
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