Image Feature Extraction
Transformers
Safetensors
English
keural_vision
vision
vision-encoder
image-text
contrastive-learning
custom-architecture
vlm
Instructions to use mkd-hika/keural-vision-encoder-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mkd-hika/keural-vision-encoder-poc 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-poc")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mkd-hika/keural-vision-encoder-poc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 411 Bytes
4272807 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"do_resize": true,
"size": {"height": 256, "width": 256},
"do_normalize": true,
"image_mean": [0.485, 0.456, 0.406],
"image_std": [0.229, 0.224, 0.225],
"do_rescale": true,
"rescale_factor": 0.00392156862745098,
"image_processor_type": "KeuralImageProcessor",
"supported_sizes": [
{"height": 224, "width": 224},
{"height": 256, "width": 256},
{"height": 320, "width": 320}
]
}
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