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-dev/keural-vision-encoder-mid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mkd-dev/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-dev/keural-vision-encoder-mid")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mkd-dev/keural-vision-encoder-mid", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download benchmark_radar_chart.png from mkd-dev/keural-vision-encoder-mid: direct link, hf CLI and curl.
- Browser
- Download file 374 kB
-
https://huggingface.co/mkd-dev/keural-vision-encoder-mid/resolve/main/benchmark_radar_chart.png
- Command line
-
hf download hf://mkd-dev/keural-vision-encoder-mid/benchmark_radar_chart.png
-
curl -L -o benchmark_radar_chart.png https://huggingface.co/mkd-dev/keural-vision-encoder-mid/resolve/main/benchmark_radar_chart.png
374 kB

- Xet hash:
- 3abdf9ecc43acb97c41a363c624be4518cdae3e0e98778df97dafae830370925
- Size of remote file:
- 374 kB
- SHA256:
- 9c915550422be8efcdaf16ecdb35c3002ccfbb2f06e273a7ce8754a47c089378
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