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-<meta property="og:description" content="Super fast and high accuracy lightweight anchor-free object detection model. Real-time on mobile devices.  Github link: https:&#x2F;&#x2F;github.com&#x2F;RangiLyu&#x2F;nanodet 1. Introduction NanoDet, a high-performance">
+<meta property="og:description" content="NanoDet, a high-performance and lightweight object detection model optimized for mobile devices, was open-sourced. It quickly gained popularity, reaching 5.7k stars on GitHub. It received upgrades in">
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-<meta property="article:modified_time" content="2024-10-30T10:07:17.662Z">
+<meta property="article:modified_time" content="2024-10-30T10:14:03.750Z">
 <meta property="article:author" content="Tianliang Zhang">
+<meta property="article:tag" content="Object Detection">
+<meta property="article:tag" content="Lightweight">
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@@ -203,7 +205,7 @@
 
     <span hidden itemprop="post" itemscope itemtype="http://schema.org/CreativeWork">
       <meta itemprop="name" content="NanoDet-Plus | Tianliang">
-      <meta itemprop="description" content="">
+      <meta itemprop="description" content="NanoDet, a high-performance and lightweight object detection model optimized for mobile devices, was open-sourced. It quickly gained popularity, reaching 5.7k stars on GitHub. It received upgrades in three key areas, label assignment, multi-scale feature fusion, and training techniques. These enhancements, now incorporated into NanoDet-Plus, achieve higher accuracy on the COCO dataset with real-time inference speeds on mobile.">
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@@ -225,7 +227,16 @@ <h1 class="post-title" itemprop="name headline">
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       <span class="post-meta-item-text">Edited on</span>
-      <time title="Modified: 2024-10-30 20:07:17" itemprop="dateModified" datetime="2024-10-30T20:07:17+10:00">2024-10-30</time>
+      <time title="Modified: 2024-10-30 20:14:03" itemprop="dateModified" datetime="2024-10-30T20:14:03+10:00">2024-10-30</time>
+    </span>
+    <span class="post-meta-item">
+      <span class="post-meta-item-icon">
+        <i class="far fa-folder"></i>
+      </span>
+      <span class="post-meta-item-text">In</span>
+        <span itemprop="about" itemscope itemtype="http://schema.org/Thing">
+          <a href="/categories/Object-Detection/" itemprop="url" rel="index"><span itemprop="name">Object Detection</span></a>
+        </span>
     </span>
 
   
@@ -238,6 +249,7 @@ <h1 class="post-title" itemprop="name headline">
     </span>
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+            <div class="post-description">NanoDet, a high-performance and lightweight object detection model optimized for mobile devices, was open-sourced. It quickly gained popularity, reaching 5.7k stars on GitHub. It received upgrades in three key areas, label assignment, multi-scale feature fusion, and training techniques. These enhancements, now incorporated into NanoDet-Plus, achieve higher accuracy on the COCO dataset with real-time inference speeds on mobile.</div>
         </div>
       </header>
 
@@ -481,6 +493,10 @@ <h1 id="conclusion">6. Conclusion</h1>
     
 
     <footer class="post-footer">
+          <div class="post-tags">
+              <a href="/tags/Object-Detection/" rel="tag"># Object Detection</a>
+              <a href="/tags/Lightweight/" rel="tag"># Lightweight</a>
+          </div>
 
         
 
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+      </span>
+      <span class="links-of-author-item">
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+      <span class="links-of-author-item">
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+  <div class="post-block">
+    <div class="post-content">
+      <div class="collection-title">
+        <h1 class="collection-header">Instance Segmentation
+          <small>Category</small>
+        </h1>
+      </div>
+
+      
+    <div class="collection-year">
+      <span class="collection-header">2022</span>
+    </div>
+
+  <article itemscope itemtype="http://schema.org/Article">
+    <header class="post-header">
+      <div class="post-meta-container">
+        <time itemprop="dateCreated"
+              datetime="2022-05-20T10:32:23+10:00"
+              content="2022-05-20">
+          05-20
+        </time>
+      </div>
+
+      <div class="post-title">
+          <a class="post-title-link" href="/2022/05/20/K-Net/" itemprop="url">
+            <span itemprop="name">K-Net:Towards Unified Image Segmentation</span>
+          </a>
+      </div>
+
+      
+    </header>
+  </article>
+
+  <article itemscope itemtype="http://schema.org/Article">
+    <header class="post-header">
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+        <time itemprop="dateCreated"
+              datetime="2022-05-19T10:39:42+10:00"
+              content="2022-05-19">
+          05-19
+        </time>
+      </div>
+
+      <div class="post-title">
+          <a class="post-title-link" href="/2022/05/19/SOTR/" itemprop="url">
+            <span itemprop="name">SOTR:Segmenting Objects with Transformers [ICCV 2021]</span>
+          </a>
+      </div>
+
+      
+    </header>
+  </article>
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+    <span class="author" itemprop="copyrightHolder">Tianliang Zhang</span>
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   <div class="category-all">
-    <ul class="category-list"><li class="category-list-item"><a class="category-list-link" href="/categories/Book/">Book</a><span class="category-list-count">1</span></li><li class="category-list-item"><a class="category-list-link" href="/categories/Deep-Learning/">Deep Learning</a><span class="category-list-count">5</span></li><li class="category-list-item"><a class="category-list-link" href="/categories/Machine-Learning/">Machine Learning</a><span class="category-list-count">1</span></li><li class="category-list-item"><a class="category-list-link" href="/categories/Object-Detection/">Object Detection</a><span class="category-list-count">4</span></li><li class="category-list-item"><a class="category-list-link" href="/categories/Pedestrian-Detection/">Pedestrian Detection</a><span class="category-list-count">3</span></li><li class="category-list-item"><a class="category-list-link" href="/categories/Python/">Python</a><span class="category-list-count">3</span></li><li class="category-list-item"><a class="category-list-link" href="/categories/Tools/">Tools</a><span class="category-list-count">1</span></li></ul>
+    <ul class="category-list"><li class="category-list-item"><a class="category-list-link" href="/categories/Book/">Book</a><span class="category-list-count">1</span></li><li class="category-list-item"><a class="category-list-link" href="/categories/Deep-Learning/">Deep Learning</a><span class="category-list-count">5</span></li><li class="category-list-item"><a class="category-list-link" href="/categories/Instance-Segmentation/">Instance Segmentation</a><span class="category-list-count">2</span></li><li class="category-list-item"><a class="category-list-link" href="/categories/Machine-Learning/">Machine Learning</a><span class="category-list-count">1</span></li><li class="category-list-item"><a class="category-list-link" href="/categories/Object-Detection/">Object Detection</a><span class="category-list-count">5</span></li><li class="category-list-item"><a class="category-list-link" href="/categories/Pedestrian-Detection/">Pedestrian Detection</a><span class="category-list-count">3</span></li><li class="category-list-item"><a class="category-list-link" href="/categories/Python/">Python</a><span class="category-list-count">3</span></li><li class="category-list-item"><a class="category-list-link" href="/categories/Tools/">Tools</a><span class="category-list-count">1</span></li></ul>
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diff --git a/index.html b/index.html
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           <a href="/categories/">
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+        <span class="site-state-item-count">8</span>
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         <i class="far fa-calendar-check"></i>
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-      <time title="Modified: 2024-10-30 20:07:17" itemprop="dateModified" datetime="2024-10-30T20:07:17+10:00">2024-10-30</time>
+      <time title="Modified: 2024-10-30 20:14:03" itemprop="dateModified" datetime="2024-10-30T20:14:03+10:00">2024-10-30</time>
+    </span>
+    <span class="post-meta-item">
+      <span class="post-meta-item-icon">
+        <i class="far fa-folder"></i>
+      </span>
+      <span class="post-meta-item-text">In</span>
+        <span itemprop="about" itemscope itemtype="http://schema.org/Thing">
+          <a href="/categories/Object-Detection/" itemprop="url" rel="index"><span itemprop="name">Object Detection</span></a>
+        </span>
     </span>
 
   
@@ -224,236 +233,15 @@ <h2 class="post-title" itemprop="name headline">
     
     
     <div class="post-body" itemprop="articleBody">
-          <blockquote>
-<p>Super fast and high accuracy lightweight anchor-free object detection
-model. Real-time on mobile devices.</p>
-</blockquote>
-<p>Github link: <a
-href="https://github.com/RangiLyu/nanodet">https://github.com/RangiLyu/nanodet</a></p>
-<h1 id="introduction">1. Introduction</h1>
-<p>NanoDet, a high-performance and lightweight object detection model
-optimized for mobile devices, was open-sourced at the end of 2021. It
-quickly gained popularity, reaching 5.7k stars on GitHub. In response to
-the rise of competing lightweight models like YOLOX-Nano, FBNetV5, and
-PP-YOLO, NanoDet received upgrades in three key areas: <strong>label
-assignment</strong>, <strong>multi-scale feature fusion</strong>, and
-<strong>training techniques</strong>. These enhancements, now
-incorporated into NanoDet-Plus, achieve higher accuracy on the COCO
-dataset with real-time inference speeds on mobile.</p>
-<p>In this article, I’ll introduce these improvements and their impact
-on NanoDet’s performance.</p>
-<h1 id="label-assignment">2. Label Assignment</h1>
-<p>Label Assignment is a crucial aspect of training object detection
-models. The earliest methods relied on position-based matching, evolving
-to anchor IOU-based matching, and more recently, to dynamic matching
-based on matching cost. Each of these advancements has driven
-substantial improvements in detection performance. In the previous
-version, NanoDet utilized ATSS (Adaptive Training Sample Selection) for
-label matching. Although ATSS dynamically selects samples on each
-feature map layer based on the mean and variance of IOU, it remains a
-static matching strategy grounded in prior information, such as anchor
-position and center.</p>
-<p>Recent research has increasingly focused on global dynamic matching
-strategies. For instance, DETR utilizes the Hungarian matching algorithm
-for bilateral matching, OTA employs Sinkhorn iterations to solve optimal
-transport in matching, and YOLOX adopts SimOTA, an approximation of OTA.
-While these strategies have shown impressive results in large models,
-applying matching-cost-based dynamic matching to lightweight detection
-models introduces challenges not present in larger models.</p>
-<h2 id="dynamic-matching">2.1 Dynamic Matching</h2>
-<p>Dynamic matching based on matching cost involves directly calculating
-the <strong>matching cost</strong> between the detection head’s output
-and each ground truth instance. This cost typically includes
-classification and regression losses. The result is an <strong>NxM cost
-matrix</strong>, where N represents the predicted points on the feature
-map and M represents all ground truths. Using this cost matrix, various
-dynamic matching strategies—such as Bipartite Graph Matching,
-Transmission Optimization, or Top-K selection—determine the optimal
-matches.</p>
-<p>Unlike anchor-based matching, which <strong>relies on static prior
-information</strong> like IOU with anchors, this approach dynamically
-finds the best matches based on the current predictions. As the model’s
-predictions improve, the matching results are likely to be more accurate
-as well.</p>
-<p>A natural question arises: if label assignment depends on predicted
-outputs, but those predictions rely on training through label
-assignment, how does the model learn effectively from a randomly
-initialized state? This can seem like a chicken-and-egg problem.
-However, due to the neural network’s inherent noise resilience, it can
-start training even from random initializations. As long as assigned
-points fall within the ground truth bounding boxes, the model begins to
-learn and fit the most easily detectable features.</p>
-<p>For dense object detection models, unlike sparse predictors like
-DETR, dynamic matching strategies also use positional constraints to
-improve accuracy. For instance, OTA and SimOTA apply a 5x5 center region
-constraint to limit the degrees of freedom in matching, ensuring more
-stable learning.</p>
-<h3 id="the-challenge-of-dynamic-matching-in-small-models">2.1.1 The
-Challenge of Dynamic Matching in Small Models</h3>
-<p>For small models, the detection head is significantly lighter. In
-NanoDet, only two Depthwise Separable Convolutional modules are used to
-predict classification and regression simultaneously. This is in stark
-contrast to large models, which often use four groups of 3x3
-convolutions with 256 channels dedicated separately to classification
-and regression. Calculating Matching Cost from a randomly initialized
-state for such a lightweight detection head presents a considerable
-challenge.</p>
-<h2 id="training-auxiliary-modules-in-nanodet-plus">2.2 Training
-Auxiliary Modules in NanoDet-Plus</h2>
-<p>NanoDet-Plus introduces a simpler and more lightweight training
-auxiliary module, the <strong>Assign Guidance Module (AGM)</strong>,
-paired with a <strong>Dynamic Soft Label Assigner (DSLA)</strong>
-strategy. Together, these components address the optimal label matching
-problem specifically for lightweight models. The architecture of
-NanoDet-Plus is shown below:</p>
-<img src="/2024/10/28/NanoDet-Plus/NanoDet-Plus-Network.png" class="" title="The overall architecture of NanoDet-Plus">
-<p>The Assign Guidance Module (AGM) consists of four 3x3 convolutions
-with a GN (Group Normalization) layer and shares parameters across
-feature maps at different scales, similar to the detection heads in
-large models. With parameter sharing and the absence of Depthwise
-Separable Convolutions (which are less GPU-friendly), AGM requires
-minimal training resources. Additionally, AGM is used only during
-training and is discarded afterward, so it has no impact on inference
-speed.</p>
-<p>The classification scores and bounding boxes predicted by AGM are fed
-into the Dynamic Soft Label Assigner (DSLA) to calculate the
-<strong>Matching Cost</strong>. This cost function comprises three
-components: classification cost, regression cost, and distance cost.</p>
-<p><span class="math display">\[C_{cls} = CE(P, Y_{soft}) \times
-(Y_{soft}-P)^2\]</span></p>
-<p><span class="math display">\[C_{reg} = -log(IOU)\]</span></p>
-<p><span class="math display">\[C_{dis} = \alpha^{|x_{pred} -
-x_{gt}|-\beta}\]</span></p>
-<p>The final cost function is defined as: $ C = C_{cls} + C_{reg} +
-C_{dis} $, where the distance cost $ C_{dis} $ is optional. Including $
-C_{dis} $ can accelerate AGM’s convergence in the early training stages,
-which is particularly useful for fine-tuning scenarios. Integrating AGM
-and DSLA in the previous NanoDet version achieved a 2.1 mAP improvement
-on the COCO dataset.</p>
-<table>
-<thead>
-<tr class="header">
-<th style="text-align: center;">Method</th>
-<th style="text-align: center;">mAP 0.5:0.95</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td style="text-align: center;">NanoDet</td>
-<td style="text-align: center;">20.6</td>
-</tr>
-<tr class="even">
-<td style="text-align: center;">NanoDet + DSLA</td>
-<td style="text-align: center;">21.9</td>
-</tr>
-<tr class="odd">
-<td style="text-align: center;">NanoDet + DSLA + AGM</td>
-<td style="text-align: center;">22.7</td>
-</tr>
-</tbody>
-</table>
-<h1 id="model-structure-improvement">3. Model Structure Improvement</h1>
-<p>In last version of NanoDet, shufflenet v2 is used as backbone, with
-PAFPN (without convolution) as Neck. Two group of Depthwise Separable
-Convolutions as detection head, which merged clssification and
-regression. For limitting the size of parameters is in 1M, all
-convolutions in neck are removed, which are more radical, and degrade
-the ability of multi-scale feature fusion.</p>
-<p>In the previous version of NanoDet, ShuffleNet v2 was used as the
-backbone, paired with a convolution-free PAFPN as the neck. The
-detection head merged classification and regression branches using only
-two groups of Depthwise Separable Convolutions. Reflecting on this
-design, the decision to remove all convolutions from the neck to limit
-the model’s parameter size to under 1M was perhaps too aggressive, as it
-reduced the effectiveness of multi-scale feature fusion.</p>
-<h2 id="feature-fusion-improvements">3.1 Feature Fusion
-Improvements</h2>
-<p>Some lightweight models, like YOLOX, PicoDet, and YOLOv5, utilize
-<strong>CSP-PAN</strong> as their feature pyramid module. In
-NanoDet-Plus, <strong>Ghost-PAN</strong>, an optimized version of PAN,
-leverages the GhostBlock from GhostNet for efficient feature fusion
-across multiple layers. Its core unit consists of <strong>a 1x1
-convolution and a 3x3 depthwise convolution</strong>, keeping both
-parameter count and computation low.</p>
-<img src="/2024/10/28/NanoDet-Plus/GhostNet_module.png" class="" title="GhostNet module">
-<p>With only 190k parameters, Ghost-PAN adds approximately 1ms to the
-running time on ARM devices, with even less impact on x86 and GPU.
-Despite its compact size, Ghost-PAN achieves a notable performance
-boost, improving mAP by 2%.</p>
-<h2 id="improvement-of-the-detection-head">3.2 Improvement of the
-Detection Head</h2>
-<p>The ThunderNet paper proposed that, in lightweight models, changing
-the depthwise component of depthwise separable convolution from 3x3 to
-5x5 can improve the detector’s receptive field and performance with
-minimal parameter increase. This technique has now become common in
-lightweight models, so NanoDet-Plus has also adopted a 5x5 kernel size
-for the depthwise convolutions in the detection head.</p>
-<p>Additionally, PicoDet introduced an extra downsampling layer to
-enhance the original 3-layer feature structure used in NanoDet. To
-achieve comparable performance, NanoDet-Plus has incorporated this
-improvement, resulting in an approximate 0.7 mAP increase.</p>
-<h1 id="training-trick-improvement">4. Training Trick Improvement</h1>
-<p>Here’s a refined version of the “Training Trick Improvement”
-section:</p>
-<ol start="4" type="1">
-<li>Training Trick Improvement</li>
-</ol>
-<p>As an open-source project, NanoDet aims to be user-friendly and
-accessible, rather than solely focused on maximizing performance
-metrics. The previous generation of NanoDet used the traditional SGD
-with momentum and MultiStepLR for training. While this approach is
-effective for experienced practitioners who can leverage large learning
-rates and gradual decay, it poses challenges for newcomers who may
-struggle with model convergence.</p>
-<p>To improve the user experience, NanoDet-Plus has made comprehensive
-updates to its training strategy:</p>
-<ul>
-<li>Optimizer: Changed from SGD with momentum to AdamW, which is less
-sensitive to hyperparameters and offers faster convergence.</li>
-<li>Learning Rate Schedule: Switched from MultiStepLR to
-CosineAnnealingLR for smoother learning rate decay.</li>
-<li>Gradient Clipping: Added during backpropagation to prevent NaN
-losses caused by unoptimized parameter settings.</li>
-<li>Exponential Moving Average (EMA): Incorporated the popular
-model-smoothing strategy for further stability.</li>
-</ul>
-<p>These adjustments make NanoDet-Plus easier to train effectively, even
-for users without extensive tuning experience.</p>
-<p>Here’s a refined and clear version of the “Deployment Optimization”
-section:</p>
-<h1 id="deployment-optimization">5. Deployment Optimization</h1>
-<p>In the previous generation of NanoDet, the multi-scale detection head
-produced six outputs: classification and regression outputs for each of
-the three feature map scales. This setup was challenging for users
-unfamiliar with the model structure.</p>
-<img src="/2024/10/28/NanoDet-Plus/v1_output.jpg" class="" title="Output of the previous NanoDet">
-<p>To make the model more accessible, NanoDet-Plus has <strong>reduced
-the number of outputs to just one</strong>. All output tensors are
-pre-reshaped and concatenated into a single tensor. While this approach
-introduces a few additional operations, causing a slight slowdown in
-post-processing speed, it is far easier for users who may not fully
-understand the model’s architecture. For advanced users well-versed in
-model structure, using the original output configuration should still be
-manageable if they wish to optimize further.</p>
-<img src="/2024/10/28/NanoDet-Plus/v2_output.jpg" class="" title="Output of NanoDet-Plus">
-<p>Following these output adjustments, consistent updates were made
-across the C++ code for deployment backends, including ncnn, MNN,
-OpenVINO, and the Android Demo. The post-processing code and interfaces
-are now standardized across these platforms, so understanding one
-implementation enables understanding the others.</p>
-<h1 id="conclusion">6. Conclusion</h1>
-<p>NanoDet-Plus addresses the limitations of the previous generation in
-areas such as label assignment, model structure, and training strategy.
-With the introduction of modules like <strong>AGM</strong>,
-<strong>DSLA</strong>, and <strong>Ghost-PAN</strong>, alongside
-comprehensive improvements in training strategy, NanoDet-Plus is now
-even easier to train. Deployment has also been optimized with a
-simplified output structure, making it more accessible. Detailed demos
-and guides are provided for ncnn, MNN, OpenVINO, and Android, helping
-users get started quickly.</p>
-<p>Below is a comparison of NanoDet-Plus with other models:</p>
-<img src="/2024/10/28/NanoDet-Plus/NanoDet-result.jpg" class="" title="Performance comparison of NanoDet with other models">
-
+          <p>NanoDet, a high-performance and lightweight object detection model optimized for mobile devices, was open-sourced. It quickly gained popularity, reaching 5.7k stars on GitHub. It received upgrades in three key areas, label assignment, multi-scale feature fusion, and training techniques. These enhancements, now incorporated into NanoDet-Plus, achieve higher accuracy on the COCO dataset with real-time inference speeds on mobile.</p>
+          <!--noindex-->
+            <div class="post-button">
+              <a class="btn" href="/2024/10/28/NanoDet-Plus/">
+                Read more &raquo;
+              </a>
+            </div>
+          <!--/noindex-->
+        
       
     </div>
 
@@ -515,7 +303,16 @@ <h2 class="post-title" itemprop="name headline">
         <i class="far fa-calendar-check"></i>
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       <span class="post-meta-item-text">Edited on</span>
-      <time title="Modified: 2024-06-12 22:41:06" itemprop="dateModified" datetime="2024-06-12T22:41:06+10:00">2024-06-12</time>
+      <time title="Modified: 2024-10-30 20:14:23" itemprop="dateModified" datetime="2024-10-30T20:14:23+10:00">2024-10-30</time>
+    </span>
+    <span class="post-meta-item">
+      <span class="post-meta-item-icon">
+        <i class="far fa-folder"></i>
+      </span>
+      <span class="post-meta-item-text">In</span>
+        <span itemprop="about" itemscope itemtype="http://schema.org/Thing">
+          <a href="/categories/Instance-Segmentation/" itemprop="url" rel="index"><span itemprop="name">Instance Segmentation</span></a>
+        </span>
     </span>
 
   
@@ -598,7 +395,16 @@ <h2 class="post-title" itemprop="name headline">
         <i class="far fa-calendar-check"></i>
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-      <time title="Modified: 2022-05-31 00:16:36" itemprop="dateModified" datetime="2022-05-31T00:16:36+10:00">2022-05-31</time>
+      <time title="Modified: 2024-10-30 20:14:40" itemprop="dateModified" datetime="2024-10-30T20:14:40+10:00">2024-10-30</time>
+    </span>
+    <span class="post-meta-item">
+      <span class="post-meta-item-icon">
+        <i class="far fa-folder"></i>
+      </span>
+      <span class="post-meta-item-text">In</span>
+        <span itemprop="about" itemscope itemtype="http://schema.org/Thing">
+          <a href="/categories/Instance-Segmentation/" itemprop="url" rel="index"><span itemprop="name">Instance Segmentation</span></a>
+        </span>
     </span>
 
   
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           <a href="/categories/">
-        <span class="site-state-item-count">7</span>
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-        <span class="site-state-item-count">14</span>
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diff --git a/search.xml b/search.xml
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--- a/search.xml
+++ b/search.xml
@@ -1314,6 +1314,9 @@ K-Net，首先通过学习的静态内核将图像划分为不同的组，然后
 Cascade Mask R-CNN。我们希望 K-Net
 和分析能够为未来统一图像分割框架的研究铺平道路。</p>
 ]]></content>
+      <categories>
+        <category>Instance Segmentation</category>
+      </categories>
       <tags>
         <tag>Instance Segmentation</tag>
       </tags>
@@ -2693,6 +2696,9 @@ mask 或通过动态卷积操作连续分割对象。前者不需要额外的 fu
 原因是：一方面，由于非线性，动态卷积显着提高了表示能力。另一方面，动态卷积比静态卷积有助于更好更快地收敛。</p>
 <img src="/2022/05/19/SOTR/ICCV21_SOTR_Table7.png" class="" title="Table 7. 实验结果对比。">
 ]]></content>
+      <categories>
+        <category>Instance Segmentation</category>
+      </categories>
       <tags>
         <tag>Transformer</tag>
         <tag>Instance Segmentation</tag>
@@ -3104,5 +3110,12 @@ users get started quickly.</p>
 <p>Below is a comparison of NanoDet-Plus with other models:</p>
 <img src="/2024/10/28/NanoDet-Plus/NanoDet-result.jpg" class="" title="Performance comparison of NanoDet with other models">
 ]]></content>
+      <categories>
+        <category>Object Detection</category>
+      </categories>
+      <tags>
+        <tag>Object Detection</tag>
+        <tag>Lightweight</tag>
+      </tags>
   </entry>
 </search>
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+      <span class="links-of-author-item">
+        <a href="mailto:tianliangjay@gmail.com" title="E-Mail → mailto:tianliangjay@gmail.com" rel="noopener me" target="_blank"><i class="envelope fa-fw"></i>E-Mail</a>
+      </span>
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+        <a href="http://www.zhihu.com/people/xingkong_liang" title="知乎 → http:&#x2F;&#x2F;www.zhihu.com&#x2F;people&#x2F;xingkong_liang" rel="noopener me" target="_blank">知乎</a>
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+      <span class="links-of-author-item">
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+              content="2024-10-28">
+          10-28
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+      </div>
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       </div>
       <div class="site-state-item site-state-tags">
           <a href="/tags/">
-        <span class="site-state-item-count">14</span>
+        <span class="site-state-item-count">15</span>
         <span class="site-state-item-name">tags</span></a>
       </div>
   </nav>
diff --git a/tags/index.html b/tags/index.html
index 2403e27..146f997 100644
--- a/tags/index.html
+++ b/tags/index.html
@@ -131,12 +131,12 @@
       </div>
       <div class="site-state-item site-state-categories">
           <a href="/categories/">
-        <span class="site-state-item-count">7</span>
+        <span class="site-state-item-count">8</span>
         <span class="site-state-item-name">categories</span></a>
       </div>
       <div class="site-state-item site-state-tags">
           <a href="/tags/">
-        <span class="site-state-item-count">14</span>
+        <span class="site-state-item-count">15</span>
         <span class="site-state-item-name">tags</span></a>
       </div>
   </nav>
@@ -187,10 +187,10 @@ <h1 class="post-title" itemprop="name headline">tags
       
       <div class="post-body"><div class="tag-cloud">
   <div class="tag-cloud-title">
-    14 tags in total
+    15 tags in total
   </div>
   <div class="tag-cloud-tags">
-    <a href="/tags/Beancount/" style="font-size: 12px;" class="tag-cloud-0">Beancount</a> <a href="/tags/Book/" style="font-size: 12px;" class="tag-cloud-0">Book</a> <a href="/tags/Deep-Learning/" style="font-size: 30px;" class="tag-cloud-10">Deep Learning</a> <a href="/tags/GAN/" style="font-size: 12px;" class="tag-cloud-0">GAN</a> <a href="/tags/Instance-Segmentation/" style="font-size: 16.5px;" class="tag-cloud-3">Instance Segmentation</a> <a href="/tags/Keras/" style="font-size: 12px;" class="tag-cloud-0">Keras</a> <a href="/tags/Object-Detection/" style="font-size: 25.5px;" class="tag-cloud-8">Object Detection</a> <a href="/tags/Pandas/" style="font-size: 12px;" class="tag-cloud-0">Pandas</a> <a href="/tags/Pedestrian-Detection/" style="font-size: 21px;" class="tag-cloud-5">Pedestrian Detection</a> <a href="/tags/Pose-Estimation/" style="font-size: 12px;" class="tag-cloud-0">Pose Estimation</a> <a href="/tags/Python/" style="font-size: 16.5px;" class="tag-cloud-3">Python</a> <a href="/tags/Tools/" style="font-size: 12px;" class="tag-cloud-0">Tools</a> <a href="/tags/Transformer/" style="font-size: 21px;" class="tag-cloud-5">Transformer</a> <a href="/tags/docker/" style="font-size: 12px;" class="tag-cloud-0">docker</a>
+    <a href="/tags/Beancount/" style="font-size: 12px;" class="tag-cloud-0">Beancount</a> <a href="/tags/Book/" style="font-size: 12px;" class="tag-cloud-0">Book</a> <a href="/tags/Deep-Learning/" style="font-size: 30px;" class="tag-cloud-10">Deep Learning</a> <a href="/tags/GAN/" style="font-size: 12px;" class="tag-cloud-0">GAN</a> <a href="/tags/Instance-Segmentation/" style="font-size: 18px;" class="tag-cloud-3">Instance Segmentation</a> <a href="/tags/Keras/" style="font-size: 12px;" class="tag-cloud-0">Keras</a> <a href="/tags/Lightweight/" style="font-size: 12px;" class="tag-cloud-0">Lightweight</a> <a href="/tags/Object-Detection/" style="font-size: 30px;" class="tag-cloud-10">Object Detection</a> <a href="/tags/Pandas/" style="font-size: 12px;" class="tag-cloud-0">Pandas</a> <a href="/tags/Pedestrian-Detection/" style="font-size: 24px;" class="tag-cloud-7">Pedestrian Detection</a> <a href="/tags/Pose-Estimation/" style="font-size: 12px;" class="tag-cloud-0">Pose Estimation</a> <a href="/tags/Python/" style="font-size: 18px;" class="tag-cloud-3">Python</a> <a href="/tags/Tools/" style="font-size: 12px;" class="tag-cloud-0">Tools</a> <a href="/tags/Transformer/" style="font-size: 24px;" class="tag-cloud-7">Transformer</a> <a href="/tags/docker/" style="font-size: 12px;" class="tag-cloud-0">docker</a>
   </div>
 </div>