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<title>PySIFT: GPU-Resident Deterministic SIFT</title>
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<h1>PySIFT</h1>
<p><strong>GPU-Resident Deterministic SIFT for Deep Learning Vision Pipelines</strong></p>
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<a class="paper" href="https://arxiv.org/abs/2605.17869" target="_blank" rel="noopener">arXiv Paper</a>
<a href="https://github.com/SivaIITM/PySIFT" target="_blank" rel="noopener">GitHub Code</a>
<a class="pypi" href="https://pypi.org/project/staysift/" target="_blank" rel="noopener">pip install staysift</a>
<a href="https://www.kaggle.com/code/sivakumarksce24d040/pysift-tutorial" target="_blank" rel="noopener" style="background-color:#20BEFF;color:white;padding:10px 20px;text-decoration:none;border-radius:5px;font-weight:bold;">Kaggle Tutorial</a>
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<p>A pure-Python, GPU-resident SIFT implementation that matches OpenCV SIFT accuracy while running <strong>26%
faster end-to-end</strong> with <strong>4x matching speedup</strong>. Zero-copy DLPack interop keeps tensors on the
GPU across the full pipeline.</p>
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<tr><th>Benchmark</th><th>Metric</th><th>PySIFT vs OpenCV</th></tr>
<tr><td>HPatches</td><td>MMA@10</td><td>+2.2pp</td></tr>
<tr><td>IMC Phototourism</td><td>Inliers/pair</td><td>303 vs 205 (+47%)</td></tr>
<tr><td>MegaDepth-1500</td><td>AUC@10</td><td>+5.6pp</td></tr>
<tr><td>ROxford5K</td><td>mAP</td><td>+7.5pp</td></tr>
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<h3>Quick Start</h3>
<pre><code>pip install staysift
from pysift import PySIFT
sift = PySIFT()
keypoints, descriptors = sift.detectAndCompute(gray_image)</code></pre>
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