Image Feature Extraction
TensorRT
ONNX
PyTorch
computer-vision
image-retrieval
animal-re-identification
cat-identification
Instructions to use RicePasteM/MeowID-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TensorRT
How to use RicePasteM/MeowID-Base with TensorRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
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README.md
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- [Hugging Face — RicePasteM/MeowID-Base](https://huggingface.co/RicePasteM/MeowID-Base)
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- [ModelScope — RicePasteM/MeowID-Base](https://modelscope.cn/models/RicePasteM/MeowID-Base)
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## Limitations
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- Face and whole-cat embeddings occupy different spaces and must not be compared or merged directly.
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- Open-set acceptance thresholds must be calibrated for the target cameras, lighting, gallery size, and operating conditions.
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- Whole-cat fallback remains sensitive to severe occlusion and visually similar coat patterns.
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- TensorRT engines are environment-specific and should be rebuilt for other deployment targets.
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- ECSeg-X cropping currently uses the PyTorch backend.
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## Citation
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- [Hugging Face — RicePasteM/MeowID-Base](https://huggingface.co/RicePasteM/MeowID-Base)
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- [ModelScope — RicePasteM/MeowID-Base](https://modelscope.cn/models/RicePasteM/MeowID-Base)
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## Citation
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