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+<svg class="func_g" x="1158.593648667853" y="312" width="4.253650855746493" height="23"><g>
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+<svg class="func_g" x="1158.593648667853" y="336" width="4.253650855746493" height="23"><g>
+    <title>~:0:&lt;built-in method numpy.core._multiarray_umath.implement_array_function&gt; 0.16% (10 10 2.8000000000000003e-05 0.00016010000000000002)</title>
     <rect height="100%" width="100%" fill="rgb(204, 204, 204)" rx="2" ry="2" />
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-    <title>&lt;__array_function__ internals&gt;:177:sum 0.06% (10 10 9.7e-06 0.000125)</title>
+<svg class="func_g" x="1158.593648667853" y="360" width="4.253650855746493" height="23"><g>
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     <rect height="100%" width="100%" fill="rgb(190, 190, 190)" rx="2" ry="2" />
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+<svg class="func_g" x="1158.593648667853" y="384" width="2.5804628110555843" height="23"><g>
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+<svg class="func_g" x="1158.593648667853" y="408" width="2.5804628110555843" height="23"><g>
+    <title>/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/openpifpaf/decoder/cifcaf.py:224:__call__ 0.86% (5 5 0.0001533 0.00042)</title>
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+<svg class="func_g" x="1162.1741114789086" y="384" width="1" height="23"><g>
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+<svg class="func_g" x="1162.1741114789086" y="408" width="1" height="23"><g>
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+<svg class="func_g" x="1164.2941306530831" y="264" width="1" height="23"><g>
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+<svg class="func_g" x="1164.2941306530831" y="312" width="1" height="23"><g>
+    <title>/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/openpifpaf/decoder/cifcaf.py:224:__call__ 0.86% (5 5 0.0001533 0.00042)</title>
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+<svg class="func_g" x="1165.7971080886189" y="264" width="1.6132850906161682" height="23"><g>
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+    <title>/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/torch/_tensor.py:980:__iter__ 0.22% (2 2 3.86e-05 3.86e-05)</title>
+    <rect height="100%" width="100%" fill="rgb(225, 225, 225)" rx="2" ry="2" />
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+<svg class="func_g" x="1165.7971080886189" y="312" width="1.6132850906161682" height="23"><g>
+    <title>/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/openpifpaf/decoder/cifcaf.py:224:__call__ 0.06% (2 2 1.13e-05 5.44e-05)</title>
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diff --git a/dev/_images/tutorial_opencv_6_0.png b/dev/_images/tutorial_opencv_6_0.png
index e3e90b4bb..85cf83642 100644
Binary files a/dev/_images/tutorial_opencv_6_0.png and b/dev/_images/tutorial_opencv_6_0.png differ
diff --git a/dev/_sources/faq.ipynb b/dev/_sources/faq.ipynb
index f19e3f507..f4e8ad3aa 100644
--- a/dev/_sources/faq.ipynb
+++ b/dev/_sources/faq.ipynb
@@ -65,9 +65,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Installation with older PyTorch versions\n",
+        "## Installation with different PyTorch versions\n",
         "\n",
-        "When installing `openpifpaf-vita` with some older PyTorch version, an error (`undefined symbol`) can happen.\n",
+        "When installing `openpifpaf-vita` with a different PyTorch version than the default one provided by `pip` (last stable version), an error (`undefined symbol`) can happen.\n",
         "Try using the `pip` option `--no-build-isolation` to solve the issue."
       ]
     },
diff --git a/dev/cli_help.html b/dev/cli_help.html
index e27982270..6e0591c1f 100644
--- a/dev/cli_help.html
+++ b/dev/cli_help.html
@@ -600,8 +600,8 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
 
 decoder configuration:
   --decoder DECODER [DECODER ...]
-                        Decoders to be considered: [&#39;cifdet&#39;, &#39;cifcafdense&#39;,
-                        &#39;trackingpose&#39;, &#39;posesimilarity&#39;, &#39;cifcaf&#39;]. (default:
+                        Decoders to be considered: [&#39;cifcafdense&#39;, &#39;cifdet&#39;,
+                        &#39;posesimilarity&#39;, &#39;cifcaf&#39;, &#39;trackingpose&#39;]. (default:
                         None)
   --seed-threshold SEED_THRESHOLD
                         minimum threshold for seeds (default: 0.2)
@@ -632,10 +632,6 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
 CifCafDense decoder:
   --dense-connections [DENSE_CONNECTIONS]
 
-trackingpose decoder:
-  --trackingpose-track-recovery
-  --trackingpose-single-seed
-
 PoseSimilarity:
   --posesimilarity-distance {crafted,euclidean,euclidean4,oks}
   --posesimilarity-oks-inflate POSESIMILARITY_OKS_INFLATE
@@ -665,11 +661,19 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
   --ablation-caf-no-rescore
   --ablation-independent-kp
 
+trackingpose decoder:
+  --trackingpose-track-recovery
+  --trackingpose-single-seed
+
 logger:
   -q, --quiet           only show warning messages or above (default: False)
   --debug               print debug messages (default: False)
   --log-stats           enable stats logging (default: False)
 
+MobileNetV3:
+  --mobilenetv3-no-pretrain
+                        use randomly initialized models (default: True)
+
 XCiT:
   --xcit-out-channels XCIT_OUT_CHANNELS
                         number of output channels for optional projection
@@ -695,22 +699,14 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
                         resolution) and 4 (lowest resolution) (default: 3)
   --swin-no-pretrain    use randomly initialized models (default: True)
 
-SqueezeNet:
-  --squeezenet-no-pretrain
-                        use randomly initialized models (default: True)
-
-MobileNetV2:
-  --mobilenetv2-no-pretrain
-                        use randomly initialized models (default: True)
-
-MobileNetV3:
-  --mobilenetv3-no-pretrain
-                        use randomly initialized models (default: True)
-
 ShuffleNetv2:
   --shufflenetv2-no-pretrain
                         use randomly initialized models (default: True)
 
+SqueezeNet:
+  --squeezenet-no-pretrain
+                        use randomly initialized models (default: True)
+
 shufflenetv2k:
   --shufflenetv2k-input-conv2-stride SHUFFLENETV2K_INPUT_CONV2_STRIDE
                         stride of the optional 2nd input convolution (default:
@@ -727,6 +723,10 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
   --shufflenetv2k-group-norm
   --shufflenetv2k-leaky-relu
 
+MobileNetV2:
+  --mobilenetv2-no-pretrain
+                        use randomly initialized models (default: True)
+
 ResNet:
   --resnet-no-pretrain  use randomly initialized models (default: True)
   --resnet-pool0-stride RESNET_POOL0_STRIDE
@@ -912,22 +912,31 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
   --separate-debug-ax
   --disable-cuda        disable CUDA (default: False)
 
-SqueezeNet:
-  --squeezenet-no-pretrain
-                        use randomly initialized models (default: True)
-
-MobileNetV2:
-  --mobilenetv2-no-pretrain
-                        use randomly initialized models (default: True)
-
-MobileNetV3:
-  --mobilenetv3-no-pretrain
-                        use randomly initialized models (default: True)
+SwinTransformer:
+  --swin-drop-path-rate SWIN_DROP_PATH_RATE
+                        drop path (stochastic depth) rate (default: 0.2)
+  --swin-input-upsample
+                        scales input image by a factor of 2 for higher res
+                        feature maps (default: False)
+  --swin-use-fpn        adds a FPN after the Swin network to obtain higher res
+                        feature maps (default: False)
+  --swin-fpn-out-channels SWIN_FPN_OUT_CHANNELS
+                        output channels of the FPN (None to use the default
+                        number of channels of the Swin network) (default:
+                        None)
+  --swin-fpn-level SWIN_FPN_LEVEL
+                        FPN pyramid level, must be between 1 (highest
+                        resolution) and 4 (lowest resolution) (default: 3)
+  --swin-no-pretrain    use randomly initialized models (default: True)
 
 ShuffleNetv2:
   --shufflenetv2-no-pretrain
                         use randomly initialized models (default: True)
 
+SqueezeNet:
+  --squeezenet-no-pretrain
+                        use randomly initialized models (default: True)
+
 shufflenetv2k:
   --shufflenetv2k-input-conv2-stride SHUFFLENETV2K_INPUT_CONV2_STRIDE
                         stride of the optional 2nd input convolution (default:
@@ -944,6 +953,10 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
   --shufflenetv2k-group-norm
   --shufflenetv2k-leaky-relu
 
+MobileNetV2:
+  --mobilenetv2-no-pretrain
+                        use randomly initialized models (default: True)
+
 ResNet:
   --resnet-no-pretrain  use randomly initialized models (default: True)
   --resnet-pool0-stride RESNET_POOL0_STRIDE
@@ -959,6 +972,10 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
                         create a network without the last block (default:
                         False)
 
+MobileNetV3:
+  --mobilenetv3-no-pretrain
+                        use randomly initialized models (default: True)
+
 XCiT:
   --xcit-out-channels XCIT_OUT_CHANNELS
                         number of output channels for optional projection
@@ -967,23 +984,6 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
                         (default: False)
   --xcit-no-pretrain    use randomly initialized models (default: True)
 
-SwinTransformer:
-  --swin-drop-path-rate SWIN_DROP_PATH_RATE
-                        drop path (stochastic depth) rate (default: 0.2)
-  --swin-input-upsample
-                        scales input image by a factor of 2 for higher res
-                        feature maps (default: False)
-  --swin-use-fpn        adds a FPN after the Swin network to obtain higher res
-                        feature maps (default: False)
-  --swin-fpn-out-channels SWIN_FPN_OUT_CHANNELS
-                        output channels of the FPN (None to use the default
-                        number of channels of the Swin network) (default:
-                        None)
-  --swin-fpn-level SWIN_FPN_LEVEL
-                        FPN pyramid level, must be between 1 (highest
-                        resolution) and 4 (lowest resolution) (default: 3)
-  --swin-no-pretrain    use randomly initialized models (default: True)
-
 CompositeField4:
   --cf4-dropout CF4_DROPOUT
                         [experimental] zeroing probability of feature in head
@@ -1038,7 +1038,7 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
 decoder configuration:
   --decoder DECODER [DECODER ...]
                         Decoders to be considered: [&#39;trackingpose&#39;,
-                        &#39;cifcafdense&#39;, &#39;cifcaf&#39;, &#39;posesimilarity&#39;, &#39;cifdet&#39;].
+                        &#39;cifcafdense&#39;, &#39;cifcaf&#39;, &#39;cifdet&#39;, &#39;posesimilarity&#39;].
                         (default: None)
   --seed-threshold SEED_THRESHOLD
                         minimum threshold for seeds (default: 0.2)
@@ -1233,10 +1233,6 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
   --debug               print debug messages (default: False)
   --log-stats           enable stats logging (default: False)
 
-SqueezeNet:
-  --squeezenet-no-pretrain
-                        use randomly initialized models (default: True)
-
 MobileNetV2:
   --mobilenetv2-no-pretrain
                         use randomly initialized models (default: True)
@@ -1245,26 +1241,6 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
   --mobilenetv3-no-pretrain
                         use randomly initialized models (default: True)
 
-ShuffleNetv2:
-  --shufflenetv2-no-pretrain
-                        use randomly initialized models (default: True)
-
-shufflenetv2k:
-  --shufflenetv2k-input-conv2-stride SHUFFLENETV2K_INPUT_CONV2_STRIDE
-                        stride of the optional 2nd input convolution (default:
-                        0)
-  --shufflenetv2k-input-conv2-outchannels SHUFFLENETV2K_INPUT_CONV2_OUTCHANNELS
-                        out channels of the optional 2nd input convolution
-                        (default: None)
-  --shufflenetv2k-stage4-dilation SHUFFLENETV2K_STAGE4_DILATION
-                        dilation factor of stage 4 (default: 1)
-  --shufflenetv2k-kernel SHUFFLENETV2K_KERNEL
-                        kernel width (default: 5)
-  --shufflenetv2k-conv5-as-stage
-  --shufflenetv2k-instance-norm
-  --shufflenetv2k-group-norm
-  --shufflenetv2k-leaky-relu
-
 ResNet:
   --resnet-no-pretrain  use randomly initialized models (default: True)
   --resnet-pool0-stride RESNET_POOL0_STRIDE
@@ -1305,6 +1281,30 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
                         resolution) and 4 (lowest resolution) (default: 3)
   --swin-no-pretrain    use randomly initialized models (default: True)
 
+ShuffleNetv2:
+  --shufflenetv2-no-pretrain
+                        use randomly initialized models (default: True)
+
+SqueezeNet:
+  --squeezenet-no-pretrain
+                        use randomly initialized models (default: True)
+
+shufflenetv2k:
+  --shufflenetv2k-input-conv2-stride SHUFFLENETV2K_INPUT_CONV2_STRIDE
+                        stride of the optional 2nd input convolution (default:
+                        0)
+  --shufflenetv2k-input-conv2-outchannels SHUFFLENETV2K_INPUT_CONV2_OUTCHANNELS
+                        out channels of the optional 2nd input convolution
+                        (default: None)
+  --shufflenetv2k-stage4-dilation SHUFFLENETV2K_STAGE4_DILATION
+                        dilation factor of stage 4 (default: 1)
+  --shufflenetv2k-kernel SHUFFLENETV2K_KERNEL
+                        kernel width (default: 5)
+  --shufflenetv2k-conv5-as-stage
+  --shufflenetv2k-instance-norm
+  --shufflenetv2k-group-norm
+  --shufflenetv2k-leaky-relu
+
 CompositeField4:
   --cf4-dropout CF4_DROPOUT
                         [experimental] zeroing probability of feature in head
@@ -1381,13 +1381,6 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
   --soft-clamp SOFT_CLAMP
                         soft clamp (default: 5.0)
 
-Bce Loss:
-  --r-smooth R_SMOOTH   r_{smooth} for SmoothL1 regressions (default: 0.0)
-
-Laplace Loss:
-  --laplace-soft-clamp LAPLACE_SOFT_CLAMP
-                        soft clamp for Laplace (default: 5.0)
-
 Bce Loss:
   --background-weight BACKGROUND_WEIGHT
                         BCE weight where ground truth is background (default:
@@ -1410,6 +1403,13 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
   --scale-soft-clamp SCALE_SOFT_CLAMP
                         soft clamp for scale (default: 5.0)
 
+Bce Loss:
+  --r-smooth R_SMOOTH   r_{smooth} for SmoothL1 regressions (default: 0.0)
+
+Laplace Loss:
+  --laplace-soft-clamp LAPLACE_SOFT_CLAMP
+                        soft clamp for Laplace (default: 5.0)
+
 trainer:
   --epochs EPOCHS       number of epochs to train (default: None)
   --train-batches TRAIN_BATCHES
@@ -2150,9 +2150,9 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
 
 decoder configuration:
   --decoder DECODER [DECODER ...]
-                        Decoders to be considered: [&#39;cifdet&#39;, &#39;trackingpose&#39;,
-                        &#39;cifcafdense&#39;, &#39;cifcaf&#39;, &#39;posesimilarity&#39;]. (default:
-                        None)
+                        Decoders to be considered: [&#39;cifcaf&#39;,
+                        &#39;posesimilarity&#39;, &#39;cifdet&#39;, &#39;trackingpose&#39;,
+                        &#39;cifcafdense&#39;]. (default: None)
   --seed-threshold SEED_THRESHOLD
                         minimum threshold for seeds (default: 0.2)
   --instance-threshold INSTANCE_THRESHOLD
@@ -2179,13 +2179,6 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
   --tr-minimum-threshold TR_MINIMUM_THRESHOLD
                         minimum-pose threshold for tracking. (default: 0.1)
 
-trackingpose decoder:
-  --trackingpose-track-recovery
-  --trackingpose-single-seed
-
-CifCafDense decoder:
-  --dense-connections [DENSE_CONNECTIONS]
-
 CifCaf decoder:
   --force-complete-pose
   --force-complete-caf-th FORCE_COMPLETE_CAF_TH
@@ -2215,27 +2208,51 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
   --posesimilarity-distance {crafted,euclidean,euclidean4,oks}
   --posesimilarity-oks-inflate POSESIMILARITY_OKS_INFLATE
 
+trackingpose decoder:
+  --trackingpose-track-recovery
+  --trackingpose-single-seed
+
+CifCafDense decoder:
+  --dense-connections [DENSE_CONNECTIONS]
+
 logger:
   -q, --quiet           only show warning messages or above (default: False)
   --debug               print debug messages (default: False)
   --log-stats           enable stats logging (default: False)
 
-SqueezeNet:
-  --squeezenet-no-pretrain
-                        use randomly initialized models (default: True)
-
-MobileNetV2:
-  --mobilenetv2-no-pretrain
-                        use randomly initialized models (default: True)
+XCiT:
+  --xcit-out-channels XCIT_OUT_CHANNELS
+                        number of output channels for optional projection
+                        layer (None for no projection layer) (default: None)
+  --xcit-out-maxpool    adds max-pooling to backbone output feature map
+                        (default: False)
+  --xcit-no-pretrain    use randomly initialized models (default: True)
 
-MobileNetV3:
-  --mobilenetv3-no-pretrain
-                        use randomly initialized models (default: True)
+SwinTransformer:
+  --swin-drop-path-rate SWIN_DROP_PATH_RATE
+                        drop path (stochastic depth) rate (default: 0.2)
+  --swin-input-upsample
+                        scales input image by a factor of 2 for higher res
+                        feature maps (default: False)
+  --swin-use-fpn        adds a FPN after the Swin network to obtain higher res
+                        feature maps (default: False)
+  --swin-fpn-out-channels SWIN_FPN_OUT_CHANNELS
+                        output channels of the FPN (None to use the default
+                        number of channels of the Swin network) (default:
+                        None)
+  --swin-fpn-level SWIN_FPN_LEVEL
+                        FPN pyramid level, must be between 1 (highest
+                        resolution) and 4 (lowest resolution) (default: 3)
+  --swin-no-pretrain    use randomly initialized models (default: True)
 
 ShuffleNetv2:
   --shufflenetv2-no-pretrain
                         use randomly initialized models (default: True)
 
+SqueezeNet:
+  --squeezenet-no-pretrain
+                        use randomly initialized models (default: True)
+
 shufflenetv2k:
   --shufflenetv2k-input-conv2-stride SHUFFLENETV2K_INPUT_CONV2_STRIDE
                         stride of the optional 2nd input convolution (default:
@@ -2252,6 +2269,14 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
   --shufflenetv2k-group-norm
   --shufflenetv2k-leaky-relu
 
+MobileNetV2:
+  --mobilenetv2-no-pretrain
+                        use randomly initialized models (default: True)
+
+MobileNetV3:
+  --mobilenetv3-no-pretrain
+                        use randomly initialized models (default: True)
+
 ResNet:
   --resnet-no-pretrain  use randomly initialized models (default: True)
   --resnet-pool0-stride RESNET_POOL0_STRIDE
@@ -2267,31 +2292,6 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
                         create a network without the last block (default:
                         False)
 
-XCiT:
-  --xcit-out-channels XCIT_OUT_CHANNELS
-                        number of output channels for optional projection
-                        layer (None for no projection layer) (default: None)
-  --xcit-out-maxpool    adds max-pooling to backbone output feature map
-                        (default: False)
-  --xcit-no-pretrain    use randomly initialized models (default: True)
-
-SwinTransformer:
-  --swin-drop-path-rate SWIN_DROP_PATH_RATE
-                        drop path (stochastic depth) rate (default: 0.2)
-  --swin-input-upsample
-                        scales input image by a factor of 2 for higher res
-                        feature maps (default: False)
-  --swin-use-fpn        adds a FPN after the Swin network to obtain higher res
-                        feature maps (default: False)
-  --swin-fpn-out-channels SWIN_FPN_OUT_CHANNELS
-                        output channels of the FPN (None to use the default
-                        number of channels of the Swin network) (default:
-                        None)
-  --swin-fpn-level SWIN_FPN_LEVEL
-                        FPN pyramid level, must be between 1 (highest
-                        resolution) and 4 (lowest resolution) (default: 3)
-  --swin-no-pretrain    use randomly initialized models (default: True)
-
 CompositeField4:
   --cf4-dropout CF4_DROPOUT
                         [experimental] zeroing probability of feature in head
@@ -2426,6 +2426,7 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
 </div>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>usage: python3 -m openpifpaf.export_onnx [-h] [--version]
+                                         [--mobilenetv3-no-pretrain]
                                          [--xcit-out-channels XCIT_OUT_CHANNELS]
                                          [--xcit-out-maxpool]
                                          [--xcit-no-pretrain]
@@ -2435,10 +2436,8 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
                                          [--swin-fpn-out-channels SWIN_FPN_OUT_CHANNELS]
                                          [--swin-fpn-level SWIN_FPN_LEVEL]
                                          [--swin-no-pretrain]
-                                         [--squeezenet-no-pretrain]
-                                         [--mobilenetv2-no-pretrain]
-                                         [--mobilenetv3-no-pretrain]
                                          [--shufflenetv2-no-pretrain]
+                                         [--squeezenet-no-pretrain]
                                          [--shufflenetv2k-input-conv2-stride SHUFFLENETV2K_INPUT_CONV2_STRIDE]
                                          [--shufflenetv2k-input-conv2-outchannels SHUFFLENETV2K_INPUT_CONV2_OUTCHANNELS]
                                          [--shufflenetv2k-stage4-dilation SHUFFLENETV2K_STAGE4_DILATION]
@@ -2446,6 +2445,7 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
                                          [--shufflenetv2k-conv5-as-stage]
                                          [--shufflenetv2k-instance-norm | --shufflenetv2k-group-norm]
                                          [--shufflenetv2k-leaky-relu]
+                                         [--mobilenetv2-no-pretrain]
                                          [--resnet-no-pretrain]
                                          [--resnet-pool0-stride RESNET_POOL0_STRIDE]
                                          [--resnet-input-conv-stride RESNET_INPUT_CONV_STRIDE]
@@ -2480,6 +2480,10 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
   --input-width INPUT_WIDTH
   --input-height INPUT_HEIGHT
 
+MobileNetV3:
+  --mobilenetv3-no-pretrain
+                        use randomly initialized models (default: True)
+
 XCiT:
   --xcit-out-channels XCIT_OUT_CHANNELS
                         number of output channels for optional projection
@@ -2505,22 +2509,14 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
                         resolution) and 4 (lowest resolution) (default: 3)
   --swin-no-pretrain    use randomly initialized models (default: True)
 
-SqueezeNet:
-  --squeezenet-no-pretrain
-                        use randomly initialized models (default: True)
-
-MobileNetV2:
-  --mobilenetv2-no-pretrain
-                        use randomly initialized models (default: True)
-
-MobileNetV3:
-  --mobilenetv3-no-pretrain
-                        use randomly initialized models (default: True)
-
 ShuffleNetv2:
   --shufflenetv2-no-pretrain
                         use randomly initialized models (default: True)
 
+SqueezeNet:
+  --squeezenet-no-pretrain
+                        use randomly initialized models (default: True)
+
 shufflenetv2k:
   --shufflenetv2k-input-conv2-stride SHUFFLENETV2K_INPUT_CONV2_STRIDE
                         stride of the optional 2nd input convolution (default:
@@ -2537,6 +2533,10 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
   --shufflenetv2k-group-norm
   --shufflenetv2k-leaky-relu
 
+MobileNetV2:
+  --mobilenetv2-no-pretrain
+                        use randomly initialized models (default: True)
+
 ResNet:
   --resnet-no-pretrain  use randomly initialized models (default: True)
   --resnet-pool0-stride RESNET_POOL0_STRIDE
@@ -2618,10 +2618,17 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
 </div>
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>usage: python3 -m openpifpaf.export_coreml [-h] [--version]
-                                           [--squeezenet-no-pretrain]
-                                           [--mobilenetv2-no-pretrain]
-                                           [--mobilenetv3-no-pretrain]
+                                           [--xcit-out-channels XCIT_OUT_CHANNELS]
+                                           [--xcit-out-maxpool]
+                                           [--xcit-no-pretrain]
+                                           [--swin-drop-path-rate SWIN_DROP_PATH_RATE]
+                                           [--swin-input-upsample]
+                                           [--swin-use-fpn]
+                                           [--swin-fpn-out-channels SWIN_FPN_OUT_CHANNELS]
+                                           [--swin-fpn-level SWIN_FPN_LEVEL]
+                                           [--swin-no-pretrain]
                                            [--shufflenetv2-no-pretrain]
+                                           [--squeezenet-no-pretrain]
                                            [--shufflenetv2k-input-conv2-stride SHUFFLENETV2K_INPUT_CONV2_STRIDE]
                                            [--shufflenetv2k-input-conv2-outchannels SHUFFLENETV2K_INPUT_CONV2_OUTCHANNELS]
                                            [--shufflenetv2k-stage4-dilation SHUFFLENETV2K_STAGE4_DILATION]
@@ -2629,21 +2636,14 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
                                            [--shufflenetv2k-conv5-as-stage]
                                            [--shufflenetv2k-instance-norm | --shufflenetv2k-group-norm]
                                            [--shufflenetv2k-leaky-relu]
+                                           [--mobilenetv2-no-pretrain]
+                                           [--mobilenetv3-no-pretrain]
                                            [--resnet-no-pretrain]
                                            [--resnet-pool0-stride RESNET_POOL0_STRIDE]
                                            [--resnet-input-conv-stride RESNET_INPUT_CONV_STRIDE]
                                            [--resnet-input-conv2-stride RESNET_INPUT_CONV2_STRIDE]
                                            [--resnet-block5-dilation RESNET_BLOCK5_DILATION]
                                            [--resnet-remove-last-block]
-                                           [--xcit-out-channels XCIT_OUT_CHANNELS]
-                                           [--xcit-out-maxpool]
-                                           [--xcit-no-pretrain]
-                                           [--swin-drop-path-rate SWIN_DROP_PATH_RATE]
-                                           [--swin-input-upsample]
-                                           [--swin-use-fpn]
-                                           [--swin-fpn-out-channels SWIN_FPN_OUT_CHANNELS]
-                                           [--swin-fpn-level SWIN_FPN_LEVEL]
-                                           [--swin-no-pretrain]
                                            [--cf4-dropout CF4_DROPOUT]
                                            [--cf4-no-inplace-ops]
                                            [--checkpoint CHECKPOINT]
@@ -2666,22 +2666,39 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
   --input-height INPUT_HEIGHT
   --minimum-deployment-target {iOS13,iOS14}
 
-SqueezeNet:
-  --squeezenet-no-pretrain
-                        use randomly initialized models (default: True)
-
-MobileNetV2:
-  --mobilenetv2-no-pretrain
-                        use randomly initialized models (default: True)
+XCiT:
+  --xcit-out-channels XCIT_OUT_CHANNELS
+                        number of output channels for optional projection
+                        layer (None for no projection layer) (default: None)
+  --xcit-out-maxpool    adds max-pooling to backbone output feature map
+                        (default: False)
+  --xcit-no-pretrain    use randomly initialized models (default: True)
 
-MobileNetV3:
-  --mobilenetv3-no-pretrain
-                        use randomly initialized models (default: True)
+SwinTransformer:
+  --swin-drop-path-rate SWIN_DROP_PATH_RATE
+                        drop path (stochastic depth) rate (default: 0.2)
+  --swin-input-upsample
+                        scales input image by a factor of 2 for higher res
+                        feature maps (default: False)
+  --swin-use-fpn        adds a FPN after the Swin network to obtain higher res
+                        feature maps (default: False)
+  --swin-fpn-out-channels SWIN_FPN_OUT_CHANNELS
+                        output channels of the FPN (None to use the default
+                        number of channels of the Swin network) (default:
+                        None)
+  --swin-fpn-level SWIN_FPN_LEVEL
+                        FPN pyramid level, must be between 1 (highest
+                        resolution) and 4 (lowest resolution) (default: 3)
+  --swin-no-pretrain    use randomly initialized models (default: True)
 
 ShuffleNetv2:
   --shufflenetv2-no-pretrain
                         use randomly initialized models (default: True)
 
+SqueezeNet:
+  --squeezenet-no-pretrain
+                        use randomly initialized models (default: True)
+
 shufflenetv2k:
   --shufflenetv2k-input-conv2-stride SHUFFLENETV2K_INPUT_CONV2_STRIDE
                         stride of the optional 2nd input convolution (default:
@@ -2698,6 +2715,14 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
   --shufflenetv2k-group-norm
   --shufflenetv2k-leaky-relu
 
+MobileNetV2:
+  --mobilenetv2-no-pretrain
+                        use randomly initialized models (default: True)
+
+MobileNetV3:
+  --mobilenetv3-no-pretrain
+                        use randomly initialized models (default: True)
+
 ResNet:
   --resnet-no-pretrain  use randomly initialized models (default: True)
   --resnet-pool0-stride RESNET_POOL0_STRIDE
@@ -2713,31 +2738,6 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
                         create a network without the last block (default:
                         False)
 
-XCiT:
-  --xcit-out-channels XCIT_OUT_CHANNELS
-                        number of output channels for optional projection
-                        layer (None for no projection layer) (default: None)
-  --xcit-out-maxpool    adds max-pooling to backbone output feature map
-                        (default: False)
-  --xcit-no-pretrain    use randomly initialized models (default: True)
-
-SwinTransformer:
-  --swin-drop-path-rate SWIN_DROP_PATH_RATE
-                        drop path (stochastic depth) rate (default: 0.2)
-  --swin-input-upsample
-                        scales input image by a factor of 2 for higher res
-                        feature maps (default: False)
-  --swin-use-fpn        adds a FPN after the Swin network to obtain higher res
-                        feature maps (default: False)
-  --swin-fpn-out-channels SWIN_FPN_OUT_CHANNELS
-                        output channels of the FPN (None to use the default
-                        number of channels of the Swin network) (default:
-                        None)
-  --swin-fpn-level SWIN_FPN_LEVEL
-                        FPN pyramid level, must be between 1 (highest
-                        resolution) and 4 (lowest resolution) (default: 3)
-  --swin-no-pretrain    use randomly initialized models (default: True)
-
 CompositeField4:
   --cf4-dropout CF4_DROPOUT
                         [experimental] zeroing probability of feature in head
diff --git a/dev/dev.html b/dev/dev.html
index 9a7b45d02..16eaf210e 100644
--- a/dev/dev.html
+++ b/dev/dev.html
@@ -649,38 +649,39 @@ <h2>Build Guide<a class="headerlink" href="#build-guide" title="Permalink to thi
 anyio==4.0.0
 argon2-cffi==23.1.0
 argon2-cffi-bindings==21.2.0
-arrow==1.2.3
+arrow==1.3.0
 astroid==2.6.6
 async-lru==2.0.4
 attrs==21.4.0
-Babel==2.12.1
+Babel==2.13.0
 backcall==0.2.0
 beautifulsoup4==4.12.2
-bleach==6.0.0
+bleach==6.1.0
 certifi==2022.12.7
-cffi==1.15.1
+cffi==1.16.0
 charset-normalizer==2.1.1
 click==8.1.7
 colorama==0.4.6
 comm==0.1.4
 contourpy==1.1.1
-coverage==7.3.1
+coverage==7.3.2
 cpplint==1.6.1
-cycler==0.11.0
+cycler==0.12.1
 debugpy==1.8.0
 decorator==5.1.1
 defusedxml==0.7.1
 docutils==0.17.1
 entrypoints==0.4
 exceptiongroup==1.1.3
-fastjsonschema==2.18.0
+fastjsonschema==2.18.1
 filelock==3.9.0
 flameprof==0.4
-fonttools==4.42.1
+fonttools==4.43.1
 fqdn==1.5.1
+fsspec==2023.4.0
 gitdb==4.0.10
 GitPython==3.1.37
-greenlet==2.0.2
+greenlet==3.0.0
 idna==3.4
 imagesize==1.4.1
 importlib-metadata==6.8.0
@@ -691,7 +692,7 @@ <h2>Build Guide<a class="headerlink" href="#build-guide" title="Permalink to thi
 ipywidgets==7.8.1
 isoduration==20.11.0
 isort==5.12.0
-jedi==0.19.0
+jedi==0.19.1
 Jinja2==3.1.2
 json5==0.9.14
 jsonpointer==2.4
@@ -702,8 +703,8 @@ <h2>Build Guide<a class="headerlink" href="#build-guide" title="Permalink to thi
 jupyter-lsp==2.2.0
 jupyter-server-mathjax==0.2.6
 jupyter-sphinx==0.3.2
-jupyter_client==8.3.1
-jupyter_core==5.3.2
+jupyter_client==8.4.0
+jupyter_core==5.4.0
 jupyter_server==2.7.3
 jupyter_server_terminals==0.4.4
 jupyterlab==4.0.6
@@ -739,13 +740,13 @@ <h2>Build Guide<a class="headerlink" href="#build-guide" title="Permalink to thi
 opencv-python==4.8.1.78
 openpifpaf-vita @ file:///home/runner/work/openpifpaf/openpifpaf
 overrides==7.4.0
-packaging==23.1
+packaging==23.2
 pandocfilters==1.5.0
 parso==0.8.3
 pexpect==4.8.0
 pickleshare==0.7.5
 Pillow==9.3.0
-platformdirs==3.10.0
+platformdirs==3.11.0
 pluggy==1.3.0
 prometheus-client==0.17.1
 prompt-toolkit==3.0.39
@@ -802,18 +803,19 @@ <h2>Build Guide<a class="headerlink" href="#build-guide" title="Permalink to thi
 tinycss2==1.2.1
 toml==0.10.2
 tomli==2.0.1
-torch==2.0.1+cpu
-torchvision==0.15.2+cpu
+torch==2.1.0+cpu
+torchvision==0.16.0+cpu
 tornado==6.3.3
-traitlets==5.10.1
+traitlets==5.11.2
+types-python-dateutil==2.8.19.14
 typing_extensions==4.4.0
 uc-micro-py==1.0.2
 uri-template==1.3.0
 urllib3==1.26.13
-wcwidth==0.2.6
+wcwidth==0.2.8
 webcolors==1.13
 webencodings==0.5.1
-websocket-client==1.6.3
+websocket-client==1.6.4
 widgetsnbextension==3.6.6
 wrapt==1.12.1
 zipp==3.17.0
@@ -829,7 +831,7 @@ <h2>Build Guide<a class="headerlink" href="#build-guide" title="Permalink to thi
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>OpenPifPaf 0.14.0+23.g6effda5
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>OpenPifPaf 0.14.1+1.ga2bc4dc
 </pre></div>
 </div>
 </div>
diff --git a/dev/faq.html b/dev/faq.html
index 3354a265c..1fad8d797 100644
--- a/dev/faq.html
+++ b/dev/faq.html
@@ -472,8 +472,8 @@ <h1 class="site-logo" id="site-title">OpenPifPaf Guide DEV</h1>
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#installation-with-older-pytorch-versions">
-   Installation with older PyTorch versions
+  <a class="reference internal nav-link" href="#installation-with-different-pytorch-versions">
+   Installation with different PyTorch versions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
@@ -541,8 +541,8 @@ <h2> On this page </h2>
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
-  <a class="reference internal nav-link" href="#installation-with-older-pytorch-versions">
-   Installation with older PyTorch versions
+  <a class="reference internal nav-link" href="#installation-with-different-pytorch-versions">
+   Installation with different PyTorch versions
   </a>
  </li>
  <li class="toc-h2 nav-item toc-entry">
@@ -620,9 +620,9 @@ <h2>Installation Problem<a class="headerlink" href="#installation-problem" title
 a particular combination be added to this list, please file a
 <a class="reference external" href="https://github.com/vita-epfl/openpifpaf/issues">GitHub issue</a>.</p>
 </section>
-<section id="installation-with-older-pytorch-versions">
-<h2>Installation with older PyTorch versions<a class="headerlink" href="#installation-with-older-pytorch-versions" title="Permalink to this headline">#</a></h2>
-<p>When installing <code class="docutils literal notranslate"><span class="pre">openpifpaf-vita</span></code> with some older PyTorch version, an error (<code class="docutils literal notranslate"><span class="pre">undefined</span> <span class="pre">symbol</span></code>) can happen.
+<section id="installation-with-different-pytorch-versions">
+<h2>Installation with different PyTorch versions<a class="headerlink" href="#installation-with-different-pytorch-versions" title="Permalink to this headline">#</a></h2>
+<p>When installing <code class="docutils literal notranslate"><span class="pre">openpifpaf-vita</span></code> with a different PyTorch version than the default one provided by <code class="docutils literal notranslate"><span class="pre">pip</span></code> (last stable version), an error (<code class="docutils literal notranslate"><span class="pre">undefined</span> <span class="pre">symbol</span></code>) can happen.
 Try using the <code class="docutils literal notranslate"><span class="pre">pip</span></code> option <code class="docutils literal notranslate"><span class="pre">--no-build-isolation</span></code> to solve the issue.</p>
 </section>
 <section id="matplotlib-backend">
diff --git a/dev/performance.html b/dev/performance.html
index 88e85829c..18e9593a3 100644
--- a/dev/performance.html
+++ b/dev/performance.html
@@ -519,7 +519,7 @@ <h2>Profile Decoder<a class="headerlink" href="#profile-decoder" title="Permalin
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>INFO:__main__:neural network device: cpu (CUDA available: False, count: 0)
 INFO:__main__:Running Python 3.10.13
-INFO:__main__:Running PyTorch 2.0.1+cpu
+INFO:__main__:Running PyTorch 2.1.0+cpu
 DEBUG:openpifpaf.show.painters:color connections = False, lw = 6, marker = 3
 DEBUG:openpifpaf.network.factory:Shell(
   (base_net): ShuffleNetV2K(
@@ -769,8 +769,6 @@ <h2>Profile Decoder<a class="headerlink" href="#profile-decoder" title="Permalin
   )
 )
 DEBUG:openpifpaf.decoder.factory:head names = [&#39;cif&#39;, &#39;caf&#39;]
-DEBUG:openpifpaf.signal:subscribe to eval_reset
-DEBUG:openpifpaf.decoder.pose_similarity:valid keypoints = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
 DEBUG:openpifpaf.visualizer.base:cif: indices = []
 DEBUG:openpifpaf.show.painters:color connections = True, lw = 2, marker = 6
 DEBUG:openpifpaf.show.painters:color connections = False, lw = 6, marker = 3
@@ -778,6 +776,8 @@ <h2>Profile Decoder<a class="headerlink" href="#profile-decoder" title="Permalin
 DEBUG:openpifpaf.visualizer.base:caf: indices = []
 DEBUG:openpifpaf.show.painters:color connections = True, lw = 2, marker = 6
 DEBUG:openpifpaf.show.painters:color connections = False, lw = 6, marker = 3
+DEBUG:openpifpaf.signal:subscribe to eval_reset
+DEBUG:openpifpaf.decoder.pose_similarity:valid keypoints = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
 DEBUG:openpifpaf.visualizer.base:cif: indices = []
 DEBUG:openpifpaf.show.painters:color connections = True, lw = 2, marker = 6
 DEBUG:openpifpaf.show.painters:color connections = False, lw = 6, marker = 3
@@ -794,111 +794,111 @@ <h2>Profile Decoder<a class="headerlink" href="#profile-decoder" title="Permalin
 DEBUG:openpifpaf.transforms.pad:valid area before pad: [  0.   0. 639. 426.], image size = (640, 427)
 DEBUG:openpifpaf.transforms.pad:pad with (0, 3, 1, 3)
 DEBUG:openpifpaf.transforms.pad:valid area after pad: [  0.   3. 639. 426.], image size = (641, 433)
-DEBUG:openpifpaf.decoder.decoder:nn processing time: 472.2ms
-DEBUG:openpifpaf.decoder.decoder:parallel execution with worker &lt;openpifpaf.decoder.decoder.DummyPool object at 0x7f6dc3267df0&gt;
+DEBUG:openpifpaf.decoder.decoder:nn processing time: 845.1ms
+DEBUG:openpifpaf.decoder.decoder:parallel execution with worker &lt;openpifpaf.decoder.decoder.DummyPool object at 0x7f34d7e87df0&gt;
 DEBUG:openpifpaf.decoder.multi:task 0
-DEBUG:openpifpaf.decoder.cifcaf:cpp annotations = 5 (14.5ms)
+DEBUG:openpifpaf.decoder.cifcaf:cpp annotations = 5 (15.3ms)
 INFO:openpifpaf.decoder.cifcaf:annotations 5: [16, 14, 13, 12, 12]
 INFO:openpifpaf.profiler:writing profile file profile_decoder.prof
-         386 function calls in 0.016 seconds
+         386 function calls in 0.017 seconds
 
    Ordered by: internal time
 
    ncalls  tottime  percall  cumtime  percall filename:lineno(function)
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 INFO:openpifpaf.network.trainer:applying ema
-INFO:openpifpaf.network.trainer:{&#39;type&#39;: &#39;train-epoch&#39;, &#39;epoch&#39;: 3, &#39;loss&#39;: -10.77339, &#39;head_losses&#39;: [-10.60994, -0.16345, 0.0], &#39;time&#39;: 69.5, &#39;n_clipped_grad&#39;: 0, &#39;max_norm&#39;: 0.0}
+INFO:openpifpaf.network.trainer:{&#39;type&#39;: &#39;train-epoch&#39;, &#39;epoch&#39;: 3, &#39;loss&#39;: -10.77601, &#39;head_losses&#39;: [-10.61176, -0.16424, 0.0], &#39;time&#39;: 126.8, &#39;n_clipped_grad&#39;: 0, &#39;max_norm&#39;: 0.0}
 INFO:openpifpaf.network.trainer:model written: cifar10_tutorial.pkl.epoch003
 INFO:openpifpaf.network.trainer:training state written: cifar10_tutorial.pkl.optim.epoch003
-INFO:openpifpaf.network.trainer:{&#39;type&#39;: &#39;val-epoch&#39;, &#39;epoch&#39;: 3, &#39;loss&#39;: -10.82073, &#39;head_losses&#39;: [-10.63834, -0.18239, 0.0], &#39;time&#39;: 10.8}
+INFO:openpifpaf.network.trainer:{&#39;type&#39;: &#39;val-epoch&#39;, &#39;epoch&#39;: 3, &#39;loss&#39;: -10.81898, &#39;head_losses&#39;: [-10.64132, -0.17767, 0.0], &#39;time&#39;: 18.6}
 </pre></div>
 </div>
 </div>
@@ -873,7 +873,7 @@ <h2>Plot Training Logs<a class="headerlink" href="#plot-training-logs" title="Pe
 <img alt="_images/plugins_cifar10_12_6.png" src="_images/plugins_cifar10_12_6.png" />
 <img alt="_images/plugins_cifar10_12_7.png" src="_images/plugins_cifar10_12_7.png" />
 <img alt="_images/plugins_cifar10_12_8.png" src="_images/plugins_cifar10_12_8.png" />
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>cifar10_tutorial.pkl.log: {&#39;message&#39;: &#39;&#39;, &#39;levelname&#39;: &#39;INFO&#39;, &#39;name&#39;: &#39;openpifpaf.network.trainer&#39;, &#39;asctime&#39;: &#39;2023-09-28 17:40:36,657&#39;, &#39;type&#39;: &#39;train&#39;, &#39;epoch&#39;: 2, &#39;batch&#39;: 3100, &#39;n_batches&#39;: 3125, &#39;time&#39;: 0.021, &#39;data_time&#39;: 0.001, &#39;lr&#39;: 3e-06, &#39;loss&#39;: -10.544, &#39;head_losses&#39;: [-10.368, -0.175, 0.0]}
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>cifar10_tutorial.pkl.log: {&#39;message&#39;: &#39;&#39;, &#39;levelname&#39;: &#39;INFO&#39;, &#39;name&#39;: &#39;openpifpaf.network.trainer&#39;, &#39;asctime&#39;: &#39;2023-10-11 17:04:23,425&#39;, &#39;type&#39;: &#39;train&#39;, &#39;epoch&#39;: 2, &#39;batch&#39;: 3100, &#39;n_batches&#39;: 3125, &#39;time&#39;: 0.035, &#39;data_time&#39;: 0.002, &#39;lr&#39;: 3e-06, &#39;loss&#39;: -10.81, &#39;head_losses&#39;: [-10.614, -0.196, 0.0]}
 </pre></div>
 </div>
 </div>
@@ -903,7 +903,7 @@ <h2>Prediction<a class="headerlink" href="#prediction" title="Permalink to this
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[{&quot;category_id&quot;: 1, &quot;category&quot;: &quot;plane&quot;, &quot;score&quot;: 0.403, &quot;bbox&quot;: [5.1, 5.09, 21.02, 21.07]}, {&quot;category_id&quot;: 9, &quot;category&quot;: &quot;ship&quot;, &quot;score&quot;: 0.376, &quot;bbox&quot;: [4.95, 5.17, 20.99, 20.99]}, {&quot;category_id&quot;: 3, &quot;category&quot;: &quot;bird&quot;, &quot;score&quot;: 0.318, &quot;bbox&quot;: [4.96, 5.0, 21.05, 21.0]}, {&quot;category_id&quot;: 4, &quot;category&quot;: &quot;cat&quot;, &quot;score&quot;: 0.247, &quot;bbox&quot;: [4.93, 5.17, 20.97, 21.0]}, {&quot;category_id&quot;: 5, &quot;category&quot;: &quot;deer&quot;, &quot;score&quot;: 0.245, &quot;bbox&quot;: [5.02, 4.99, 21.06, 21.05]}, {&quot;category_id&quot;: 6, &quot;category&quot;: &quot;dog&quot;, &quot;score&quot;: 0.224, &quot;bbox&quot;: [4.81, 5.12, 21.07, 20.95]}, {&quot;category_id&quot;: 10, &quot;category&quot;: &quot;truck&quot;, &quot;score&quot;: 0.2, &quot;bbox&quot;: [5.0, 4.92, 20.89, 20.98]}, {&quot;category_id&quot;: 8, &quot;category&quot;: &quot;horse&quot;, &quot;score&quot;: 0.171, &quot;bbox&quot;: [5.05, 5.18, 20.99, 20.95]}][{&quot;category_id&quot;: 10, &quot;category&quot;: &quot;truck&quot;, &quot;score&quot;: 0.471, &quot;bbox&quot;: [4.86, 4.65, 21.05, 21.11]}, {&quot;category_id&quot;: 2, &quot;category&quot;: &quot;car&quot;, &quot;score&quot;: 0.469, &quot;bbox&quot;: [5.14, 4.66, 21.06, 20.96]}, {&quot;category_id&quot;: 9, &quot;category&quot;: &quot;ship&quot;, &quot;score&quot;: 0.212, &quot;bbox&quot;: [4.96, 5.0, 21.02, 21.12]}, {&quot;category_id&quot;: 1, &quot;category&quot;: &quot;plane&quot;, &quot;score&quot;: 0.155, &quot;bbox&quot;: [4.8, 5.05, 21.11, 20.99]}][{&quot;category_id&quot;: 9, &quot;category&quot;: &quot;ship&quot;, &quot;score&quot;: 0.326, &quot;bbox&quot;: [4.89, 4.85, 21.03, 21.05]}, {&quot;category_id&quot;: 1, &quot;category&quot;: &quot;plane&quot;, &quot;score&quot;: 0.322, &quot;bbox&quot;: [4.93, 4.97, 21.01, 21.0]}, {&quot;category_id&quot;: 3, &quot;category&quot;: &quot;bird&quot;, &quot;score&quot;: 0.284, &quot;bbox&quot;: [4.95, 4.75, 21.15, 21.07]}, {&quot;category_id&quot;: 10, &quot;category&quot;: &quot;truck&quot;, &quot;score&quot;: 0.28, &quot;bbox&quot;: [5.0, 4.95, 21.11, 21.11]}, {&quot;category_id&quot;: 2, &quot;category&quot;: &quot;car&quot;, &quot;score&quot;: 0.274, &quot;bbox&quot;: [4.96, 5.01, 21.0, 20.94]}, {&quot;category_id&quot;: 4, &quot;category&quot;: &quot;cat&quot;, &quot;score&quot;: 0.23, &quot;bbox&quot;: [5.09, 5.03, 21.06, 21.07]}, {&quot;category_id&quot;: 8, &quot;category&quot;: &quot;horse&quot;, &quot;score&quot;: 0.218, &quot;bbox&quot;: [4.93, 5.04, 21.0, 21.02]}, {&quot;category_id&quot;: 5, &quot;category&quot;: &quot;deer&quot;, &quot;score&quot;: 0.204, &quot;bbox&quot;: [5.05, 4.89, 21.17, 21.2]}, {&quot;category_id&quot;: 6, &quot;category&quot;: &quot;dog&quot;, &quot;score&quot;: 0.188, &quot;bbox&quot;: [4.99, 4.91, 21.12, 21.01]}, {&quot;category_id&quot;: 7, &quot;category&quot;: &quot;frog&quot;, &quot;score&quot;: 0.16, &quot;bbox&quot;: [4.93, 4.99, 20.98, 21.01]}][{&quot;category_id&quot;: 10, &quot;category&quot;: &quot;truck&quot;, &quot;score&quot;: 0.369, &quot;bbox&quot;: [5.07, 5.11, 20.96, 20.96]}, {&quot;category_id&quot;: 8, &quot;category&quot;: &quot;horse&quot;, &quot;score&quot;: 0.319, &quot;bbox&quot;: [5.16, 5.08, 20.96, 21.0]}, {&quot;category_id&quot;: 2, &quot;category&quot;: &quot;car&quot;, &quot;score&quot;: 0.313, &quot;bbox&quot;: [5.02, 4.93, 20.99, 20.97]}, {&quot;category_id&quot;: 4, &quot;category&quot;: &quot;cat&quot;, &quot;score&quot;: 0.29, &quot;bbox&quot;: [5.03, 5.06, 20.98, 20.99]}, {&quot;category_id&quot;: 1, &quot;category&quot;: &quot;plane&quot;, &quot;score&quot;: 0.282, &quot;bbox&quot;: [4.99, 4.98, 20.96, 21.0]}, {&quot;category_id&quot;: 6, &quot;category&quot;: &quot;dog&quot;, &quot;score&quot;: 0.252, &quot;bbox&quot;: [5.05, 5.04, 20.96, 20.95]}, {&quot;category_id&quot;: 9, &quot;category&quot;: &quot;ship&quot;, &quot;score&quot;: 0.248, &quot;bbox&quot;: [4.95, 4.92, 21.04, 21.02]}, {&quot;category_id&quot;: 3, &quot;category&quot;: &quot;bird&quot;, &quot;score&quot;: 0.235, &quot;bbox&quot;: [5.01, 5.09, 20.93, 21.09]}, {&quot;category_id&quot;: 7, &quot;category&quot;: &quot;frog&quot;, &quot;score&quot;: 0.21, &quot;bbox&quot;: [5.03, 5.07, 20.98, 20.94]}, {&quot;category_id&quot;: 5, &quot;category&quot;: &quot;deer&quot;, &quot;score&quot;: 0.208, &quot;bbox&quot;: [5.08, 5.0, 20.94, 20.98]}]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[{&quot;category_id&quot;: 1, &quot;category&quot;: &quot;plane&quot;, &quot;score&quot;: 0.401, &quot;bbox&quot;: [4.98, 4.94, 20.97, 21.03]}, {&quot;category_id&quot;: 9, &quot;category&quot;: &quot;ship&quot;, &quot;score&quot;: 0.362, &quot;bbox&quot;: [4.95, 4.98, 21.04, 20.99]}, {&quot;category_id&quot;: 3, &quot;category&quot;: &quot;bird&quot;, &quot;score&quot;: 0.342, &quot;bbox&quot;: [5.15, 5.03, 21.03, 21.02]}, {&quot;category_id&quot;: 4, &quot;category&quot;: &quot;cat&quot;, &quot;score&quot;: 0.296, &quot;bbox&quot;: [5.02, 4.93, 21.08, 20.94]}, {&quot;category_id&quot;: 6, &quot;category&quot;: &quot;dog&quot;, &quot;score&quot;: 0.26, &quot;bbox&quot;: [5.05, 4.99, 20.91, 20.9]}, {&quot;category_id&quot;: 5, &quot;category&quot;: &quot;deer&quot;, &quot;score&quot;: 0.257, &quot;bbox&quot;: [5.17, 4.92, 20.94, 21.12]}, {&quot;category_id&quot;: 10, &quot;category&quot;: &quot;truck&quot;, &quot;score&quot;: 0.22, &quot;bbox&quot;: [5.03, 5.02, 21.04, 20.91]}, {&quot;category_id&quot;: 8, &quot;category&quot;: &quot;horse&quot;, &quot;score&quot;: 0.208, &quot;bbox&quot;: [5.16, 4.94, 21.01, 21.12]}][{&quot;category_id&quot;: 2, &quot;category&quot;: &quot;car&quot;, &quot;score&quot;: 0.472, &quot;bbox&quot;: [4.87, 5.1, 20.95, 20.87]}, {&quot;category_id&quot;: 10, &quot;category&quot;: &quot;truck&quot;, &quot;score&quot;: 0.427, &quot;bbox&quot;: [5.34, 5.81, 21.09, 21.06]}, {&quot;category_id&quot;: 9, &quot;category&quot;: &quot;ship&quot;, &quot;score&quot;: 0.214, &quot;bbox&quot;: [4.95, 5.14, 21.06, 21.05]}, {&quot;category_id&quot;: 1, &quot;category&quot;: &quot;plane&quot;, &quot;score&quot;: 0.181, &quot;bbox&quot;: [5.01, 5.09, 21.03, 21.02]}][{&quot;category_id&quot;: 1, &quot;category&quot;: &quot;plane&quot;, &quot;score&quot;: 0.345, &quot;bbox&quot;: [4.97, 4.9, 21.0, 20.98]}, {&quot;category_id&quot;: 9, &quot;category&quot;: &quot;ship&quot;, &quot;score&quot;: 0.334, &quot;bbox&quot;: [5.01, 5.06, 21.12, 21.09]}, {&quot;category_id&quot;: 10, &quot;category&quot;: &quot;truck&quot;, &quot;score&quot;: 0.312, &quot;bbox&quot;: [5.02, 5.12, 20.99, 20.99]}, {&quot;category_id&quot;: 3, &quot;category&quot;: &quot;bird&quot;, &quot;score&quot;: 0.273, &quot;bbox&quot;: [4.92, 5.01, 21.09, 21.06]}, {&quot;category_id&quot;: 2, &quot;category&quot;: &quot;car&quot;, &quot;score&quot;: 0.27, &quot;bbox&quot;: [5.11, 5.03, 20.94, 21.06]}, {&quot;category_id&quot;: 8, &quot;category&quot;: &quot;horse&quot;, &quot;score&quot;: 0.225, &quot;bbox&quot;: [5.05, 4.96, 20.99, 20.98]}, {&quot;category_id&quot;: 4, &quot;category&quot;: &quot;cat&quot;, &quot;score&quot;: 0.224, &quot;bbox&quot;: [5.03, 4.9, 20.99, 21.04]}, {&quot;category_id&quot;: 5, &quot;category&quot;: &quot;deer&quot;, &quot;score&quot;: 0.189, &quot;bbox&quot;: [4.85, 4.96, 21.13, 21.04]}, {&quot;category_id&quot;: 6, &quot;category&quot;: &quot;dog&quot;, &quot;score&quot;: 0.184, &quot;bbox&quot;: [5.1, 4.94, 20.91, 21.06]}][{&quot;category_id&quot;: 10, &quot;category&quot;: &quot;truck&quot;, &quot;score&quot;: 0.405, &quot;bbox&quot;: [4.93, 4.95, 20.99, 21.04]}, {&quot;category_id&quot;: 2, &quot;category&quot;: &quot;car&quot;, &quot;score&quot;: 0.348, &quot;bbox&quot;: [4.94, 4.97, 21.0, 20.96]}, {&quot;category_id&quot;: 8, &quot;category&quot;: &quot;horse&quot;, &quot;score&quot;: 0.333, &quot;bbox&quot;: [4.96, 5.14, 21.0, 20.98]}, {&quot;category_id&quot;: 4, &quot;category&quot;: &quot;cat&quot;, &quot;score&quot;: 0.287, &quot;bbox&quot;: [5.11, 4.95, 21.01, 20.96]}, {&quot;category_id&quot;: 6, &quot;category&quot;: &quot;dog&quot;, &quot;score&quot;: 0.254, &quot;bbox&quot;: [5.09, 5.0, 21.01, 20.93]}, {&quot;category_id&quot;: 1, &quot;category&quot;: &quot;plane&quot;, &quot;score&quot;: 0.252, &quot;bbox&quot;: [5.03, 5.03, 21.0, 21.01]}, {&quot;category_id&quot;: 9, &quot;category&quot;: &quot;ship&quot;, &quot;score&quot;: 0.229, &quot;bbox&quot;: [5.04, 5.01, 21.02, 21.05]}, {&quot;category_id&quot;: 7, &quot;category&quot;: &quot;frog&quot;, &quot;score&quot;: 0.211, &quot;bbox&quot;: [5.01, 4.99, 20.99, 20.97]}, {&quot;category_id&quot;: 3, &quot;category&quot;: &quot;bird&quot;, &quot;score&quot;: 0.201, &quot;bbox&quot;: [4.93, 5.02, 21.01, 20.97]}, {&quot;category_id&quot;: 5, &quot;category&quot;: &quot;deer&quot;, &quot;score&quot;: 0.168, &quot;bbox&quot;: [4.99, 5.08, 20.99, 21.0]}]
 </pre></div>
 </div>
 </div>
@@ -935,19 +935,19 @@ <h2>Prediction<a class="headerlink" href="#prediction" title="Permalink to this
 </div>
 <div class="cell_output docutils container">
 <img alt="_images/plugins_cifar10_17_0.png" src="_images/plugins_cifar10_17_0.png" />
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;plane 40%&#39;, &#39;ship 38%&#39;, &#39;bird 32%&#39;]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;plane 40%&#39;, &#39;ship 36%&#39;, &#39;bird 34%&#39;]
 </pre></div>
 </div>
 <img alt="_images/plugins_cifar10_17_2.png" src="_images/plugins_cifar10_17_2.png" />
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;truck 47%&#39;, &#39;car 47%&#39;]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;car 47%&#39;, &#39;truck 43%&#39;]
 </pre></div>
 </div>
 <img alt="_images/plugins_cifar10_17_4.png" src="_images/plugins_cifar10_17_4.png" />
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;ship 33%&#39;, &#39;plane 32%&#39;]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;plane 35%&#39;, &#39;ship 33%&#39;, &#39;truck 31%&#39;]
 </pre></div>
 </div>
 <img alt="_images/plugins_cifar10_17_6.png" src="_images/plugins_cifar10_17_6.png" />
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;truck 37%&#39;, &#39;horse 32%&#39;, &#39;car 31%&#39;]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;truck 40%&#39;, &#39;car 35%&#39;, &#39;horse 33%&#39;]
 </pre></div>
 </div>
 </div>
@@ -994,17 +994,17 @@ <h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this
         &quot;truck&quot;
     ],
     &quot;stats&quot;: [
-        0.4159,
-        0.479,
-        0.593,
-        0.169,
-        0.393,
-        0.332,
-        0.188,
-        0.481,
-        0.455,
-        0.581,
-        0.488
+        0.4157,
+        0.472,
+        0.688,
+        0.147,
+        0.365,
+        0.388,
+        0.132,
+        0.482,
+        0.496,
+        0.502,
+        0.485
     ],
     &quot;args&quot;: [
         &quot;/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/openpifpaf/eval.py&quot;,
@@ -1015,18 +1015,18 @@ <h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this
         &quot;--instance-threshold=0.1&quot;,
         &quot;--quiet&quot;
     ],
-    &quot;version&quot;: &quot;0.14.0+23.g6effda5&quot;,
+    &quot;version&quot;: &quot;0.14.1+1.ga2bc4dc&quot;,
     &quot;dataset&quot;: &quot;cifar10&quot;,
-    &quot;total_time&quot;: 32.99185662399998,
+    &quot;total_time&quot;: 56.3783854830001,
     &quot;checkpoint&quot;: &quot;cifar10_tutorial.pkl.epoch003&quot;,
     &quot;count_ops&quot;: [
         421736880.0,
         105180.0
     ],
-    &quot;file_size&quot;: 438179,
+    &quot;file_size&quot;: 438766,
     &quot;n_images&quot;: 10000,
-    &quot;decoder_time&quot;: 8.64063907301238,
-    &quot;nn_time&quot;: 8.361834807997298
+    &quot;decoder_time&quot;: 14.016099843001712,
+    &quot;nn_time&quot;: 24.6159981660046
 }
 </pre></div>
 </div>
diff --git a/dev/plugins_crowdpose.html b/dev/plugins_crowdpose.html
index e2c6ea53c..a47bdb794 100644
--- a/dev/plugins_crowdpose.html
+++ b/dev/plugins_crowdpose.html
@@ -609,7 +609,7 @@ <h2>Prediction<a class="headerlink" href="#prediction" title="Permalink to this
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>INFO:__main__:neural network device: cpu (CUDA available: False, count: 0)
 INFO:__main__:Running Python 3.10.13
-INFO:__main__:Running PyTorch 2.0.1+cpu
+INFO:__main__:Running PyTorch 2.1.0+cpu
 INFO:openpifpaf.decoder.factory:No specific decoder requested. Using the first one from:
   --decoder=cifcaf:0
   --decoder=posesimilarity:0
diff --git a/dev/plugins_nuscenes.html b/dev/plugins_nuscenes.html
index 9f5da0d9d..2a0b5b2e8 100644
--- a/dev/plugins_nuscenes.html
+++ b/dev/plugins_nuscenes.html
@@ -542,9 +542,9 @@ <h2>Prediction<a class="headerlink" href="#prediction" title="Permalink to this
 <div class="cell_output docutils container">
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>INFO:__main__:neural network device: cpu (CUDA available: False, count: 0)
 INFO:__main__:Running Python 3.10.13
-INFO:__main__:Running PyTorch 2.0.1+cpu
+INFO:__main__:Running PyTorch 2.1.0+cpu
 INFO:openpifpaf.predictor:neural network device: cpu (CUDA available: False, count: 0)
-INFO:openpifpaf.decoder.cifdet:annotations 3, decoder = 16.8ms
+INFO:openpifpaf.decoder.cifdet:annotations 3, decoder = 27.9ms
 INFO:openpifpaf.predictor:batch 0: nuscenes/n013-2018-08-28-10-41-15+0800__CAM_FRONT__1535424448912407.jpg
 /home/runner/work/openpifpaf/openpifpaf/src/openpifpaf/csrc/src/cif_hr.cpp:102: UserInfo: resizing cifhr buffer
 /home/runner/work/openpifpaf/openpifpaf/src/openpifpaf/csrc/src/occupancy.cpp:53: UserInfo: resizing occupancy buffer
diff --git a/dev/plugins_overview.html b/dev/plugins_overview.html
index 3c677835f..97b9022f7 100644
--- a/dev/plugins_overview.html
+++ b/dev/plugins_overview.html
@@ -712,16 +712,16 @@ <h2>Registrations<a class="headerlink" href="#registrations" title="Permalink to
  &#39;mobilenetv2&#39;: &#39;http://github.com/vita-epfl/openpifpaf-torchhub/releases/download/v0.12a5/mobilenetv2-201112-193315-cocokp-1728a9f5.pkl&#39;,
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- &#39;resnet18&#39;: &lt;object at 0x7f4d5a682c70&gt;,
+ &#39;resnet18&#39;: &lt;object at 0x7f21fb170590&gt;,
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+ &#39;shufflenetv2k44&#39;: &lt;object at 0x7f21fb170590&gt;,
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diff --git a/dev/plugins_wholebody.html b/dev/plugins_wholebody.html
index e739af5bc..15338dcbf 100644
--- a/dev/plugins_wholebody.html
+++ b/dev/plugins_wholebody.html
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diff --git a/dev/predict_api.html b/dev/predict_api.html
index a5594041d..d8148e91f 100644
--- a/dev/predict_api.html
+++ b/dev/predict_api.html
@@ -538,8 +538,8 @@ <h1>Prediction API<a class="headerlink" href="#prediction-api" title="Permalink
 </div>
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 <div class="cell_output docutils container">
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+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>OpenPifPaf version 0.14.1+1.ga2bc4dc
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@@ -594,7 +594,7 @@ <h2>Use the Predictor API<a class="headerlink" href="#use-the-predictor-api" tit
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;openpifpaf.annotation.Annotation at 0x7fa6c2f85840&gt;
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diff --git a/dev/predict_cli.html b/dev/predict_cli.html
index 6f6a6cfa5..e314991b7 100644
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+++ b/dev/predict_cli.html
@@ -539,7 +539,7 @@ <h2>Images<a class="headerlink" href="#images" title="Permalink to this headline
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 DEBUG:openpifpaf.network.factory:Shell(
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@@ -1194,8 +1194,6 @@ <h2>Debug<a class="headerlink" href="#debug" title="Permalink to this headline">
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 DEBUG:openpifpaf.show.painters:color connections = False, lw = 6, marker = 3
@@ -1203,6 +1201,8 @@ <h2>Debug<a class="headerlink" href="#debug" title="Permalink to this headline">
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 DEBUG:openpifpaf.show.painters:color connections = True, lw = 2, marker = 6
 DEBUG:openpifpaf.show.painters:color connections = False, lw = 6, marker = 3
+DEBUG:openpifpaf.signal:subscribe to eval_reset
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@@ -1219,12 +1219,12 @@ <h2>Debug<a class="headerlink" href="#debug" title="Permalink to this headline">
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index b4b8398cb..30507edd1 100644
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diff --git a/dev/train.html b/dev/train.html
index 3ba7e3038..807efe174 100644
--- a/dev/train.html
+++ b/dev/train.html
@@ -773,8 +773,8 @@ <h2>ShuffleNet<a class="headerlink" href="#shufflenet" title="Permalink to this
 We found that for stable training, the learning rate <span class="math notranslate nohighlight">\(\lambda\)</span>,
 the <code class="docutils literal notranslate"><span class="pre">clip-grad-value</span></code> <span class="math notranslate nohighlight">\(c\)</span> and the <span class="math notranslate nohighlight">\(\epsilon\)</span> from the denominator of the
 BatchNorm should obey:</p>
-<div class="amsmath math notranslate nohighlight" id="equation-3eda8100-0d14-4996-b319-45741717ab27">
-<span class="eqno">(1)<a class="headerlink" href="#equation-3eda8100-0d14-4996-b319-45741717ab27" title="Permalink to this equation">#</a></span>\[\begin{align}
+<div class="amsmath math notranslate nohighlight" id="equation-cf48084f-7799-4b29-a97c-1a594aadf6b1">
+<span class="eqno">(1)<a class="headerlink" href="#equation-cf48084f-7799-4b29-a97c-1a594aadf6b1" title="Permalink to this equation">#</a></span>\[\begin{align}
     \lambda \cdot c &amp;\leq \sqrt{\epsilon}
 \end{align}\]</div>
 <p>where the default <span class="math notranslate nohighlight">\(\epsilon\)</span> is <span class="math notranslate nohighlight">\(10^{-4}\)</span> (different from the PyTorch default).