CNN-Transformer-for-motion-prediciton
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| Format: | Recurso digital |
| Langue: | anglais |
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2026
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| _version_ | 1866901884293349376 |
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| author | Liu, Jiaxin |
| author_facet | Liu, Jiaxin |
| contents | <div dir="auto"> <h2>Introduction</h2> <a href="https://github.com/husandaimei/CNN-Transformer-for-motion-prediciton#introduction"></a></div> <p dir="auto">This project implements a <strong>CNN + Transformer</strong> framework for image-sequence-based motion prediction of active micromotors.<br>Given a sequence of images, the model predicts the motion state at each frame, including:</p> <ul> <li><code>cos(θ)</code></li> <li><code>sin(θ)</code></li> <li><code>normalized speed</code></li> </ul> <p dir="auto">The project includes:</p> <ul> <li>model definition</li> <li>training pipeline</li> <li>custom sequence loss</li> <li>prediction and visualization</li> <li>training log export</li> </ul> <div dir="auto"> <h2>Model</h2> <a href="https://github.com/husandaimei/CNN-Transformer-for-motion-prediciton#model"></a></div> <p dir="auto">The model consists of three parts:</p> <ol> <li> <p dir="auto"><strong>CNN feature extractor</strong><br>ResNet18 is used to extract spatial features from each frame.</p> </li> <li> <p dir="auto"><strong>Transformer encoder</strong><br>Temporal dependencies across image sequences are modeled by a Transformer encoder.</p> </li> <li> <p dir="auto"><strong>Regression head</strong><br>The model outputs a 3-dimensional vector for each frame:</p> <div> <pre><code>[cos(theta), sin(theta), speed]</code></pre> </div> </li> </ol> <div dir="auto"> <h2>Requirements</h2> <a href="https://github.com/husandaimei/CNN-Transformer-for-motion-prediciton#requirements"></a></div> <p dir="auto">This project requires the following environment and dependencies:</p> <ul> <li>Python 3.8+</li> <li>PyTorch</li> <li>torchvision</li> <li>numpy</li> <li>pandas</li> <li>matplotlib</li> <li>opencv-python</li> <li>tqdm</li> <li>openpyxl</li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19245390 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | CNN-Transformer-for-motion-prediciton Liu, Jiaxin microrobot micromanipulation Janus micromotor <div dir="auto"> <h2>Introduction</h2> <a href="https://github.com/husandaimei/CNN-Transformer-for-motion-prediciton#introduction"></a></div> <p dir="auto">This project implements a <strong>CNN + Transformer</strong> framework for image-sequence-based motion prediction of active micromotors.<br>Given a sequence of images, the model predicts the motion state at each frame, including:</p> <ul> <li><code>cos(θ)</code></li> <li><code>sin(θ)</code></li> <li><code>normalized speed</code></li> </ul> <p dir="auto">The project includes:</p> <ul> <li>model definition</li> <li>training pipeline</li> <li>custom sequence loss</li> <li>prediction and visualization</li> <li>training log export</li> </ul> <div dir="auto"> <h2>Model</h2> <a href="https://github.com/husandaimei/CNN-Transformer-for-motion-prediciton#model"></a></div> <p dir="auto">The model consists of three parts:</p> <ol> <li> <p dir="auto"><strong>CNN feature extractor</strong><br>ResNet18 is used to extract spatial features from each frame.</p> </li> <li> <p dir="auto"><strong>Transformer encoder</strong><br>Temporal dependencies across image sequences are modeled by a Transformer encoder.</p> </li> <li> <p dir="auto"><strong>Regression head</strong><br>The model outputs a 3-dimensional vector for each frame:</p> <div> <pre><code>[cos(theta), sin(theta), speed]</code></pre> </div> </li> </ol> <div dir="auto"> <h2>Requirements</h2> <a href="https://github.com/husandaimei/CNN-Transformer-for-motion-prediciton#requirements"></a></div> <p dir="auto">This project requires the following environment and dependencies:</p> <ul> <li>Python 3.8+</li> <li>PyTorch</li> <li>torchvision</li> <li>numpy</li> <li>pandas</li> <li>matplotlib</li> <li>opencv-python</li> <li>tqdm</li> <li>openpyxl</li> </ul> |
| title | CNN-Transformer-for-motion-prediciton |
| topic | microrobot micromanipulation Janus micromotor |
| url | https://doi.org/10.5281/zenodo.19245390 |