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_curve_chart.png from mkd-dev/keural-vision-encoder-mid: direct link, hf CLI and curl.
- Browser
- Download file 254 kB
-
https://huggingface.co/mkd-dev/keural-vision-encoder-mid/resolve/main/benchmark_recall_curve_chart.png
- Command line
-
hf download hf://mkd-dev/keural-vision-encoder-mid/benchmark_recall_curve_chart.png
-
curl -L -o benchmark_recall_curve_chart.png https://huggingface.co/mkd-dev/keural-vision-encoder-mid/resolve/main/benchmark_recall_curve_chart.png
254 kB

- Xet hash:
- a35b66843f9b8df512217f25f14b8ece69d41b9b46f590aa9698a3eca0c07d2d
- Size of remote file:
- 254 kB
- SHA256:
- 3bf31e0cd78d77dcd7d9d0291d7096a6535f6385b34fad4219890f3c0edc8c45
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