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_recall_at5_chart.png from mkd-dev/keural-vision-encoder-mid: direct link, hf CLI and curl.
- Browser
- Download file 226 kB
-
https://huggingface.co/mkd-dev/keural-vision-encoder-mid/resolve/main/benchmark_recall_at5_chart.png
- Command line
-
hf download hf://mkd-dev/keural-vision-encoder-mid/benchmark_recall_at5_chart.png
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curl -L -o benchmark_recall_at5_chart.png https://huggingface.co/mkd-dev/keural-vision-encoder-mid/resolve/main/benchmark_recall_at5_chart.png
226 kB

- Xet hash:
- 4b21343ae26e24cd2ba97f8f8786e15f1deadc0875cc7b70d1fa3fc49d2086ad
- Size of remote file:
- 226 kB
- SHA256:
- 821f653da750827b5c8cc97710e5c907ec5544052bfc5ddd239d3f913fe34909
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