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     <rect height="100%" width="100%" fill="rgb(166, 166, 255)" rx="2" ry="2" />
     <text alignment-baseline="central" x="4.0" y="12.0" font-size="12px" fill="rgb(0,0,0)">&lt;method 'tolist' of 'numpy.ndarray' objects&gt;</text>
 </g></svg>
-<svg class="func_g" x="1168.7609360788035" y="264" width="1" height="23"><g>
-    <title>/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/openpifpaf/annotation.py:17:__init__ 0.13% (5 5 1.8500000000000002e-05 1.8500000000000002e-05)</title>
+<svg class="func_g" x="1175.2502916358017" y="264" width="1" height="23"><g>
+    <title>/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/openpifpaf/annotation.py:17:__init__ 0.10% (5 5 1.6400000000000002e-05 1.6400000000000002e-05)</title>
     <rect height="100%" width="100%" fill="rgb(218, 218, 218)" rx="2" ry="2" />
     <text alignment-baseline="central" x="4.0" y="12.0" font-size="12px" fill="rgb(0,0,0)">__init__</text>
 </g></svg>
-<svg class="func_g" x="1168.7609360788035" y="288" width="1" height="23"><g>
-    <title>/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/openpifpaf/decoder/cifcaf.py:224:__call__ 0.88% (5 5 0.000126403 0.00033050500000000003)</title>
+<svg class="func_g" x="1175.2502916358017" y="288" width="1" height="23"><g>
+    <title>/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/openpifpaf/decoder/cifcaf.py:224:__call__ 0.64% (5 5 0.0001038 0.00028690000000000003)</title>
     <rect height="100%" width="100%" fill="rgb(232, 232, 232)" rx="2" ry="2" />
     <text alignment-baseline="central" x="4.0" y="12.0" font-size="12px" fill="rgb(0,0,0)">__call__</text>
 </g></svg>
-<svg class="func_g" x="1170.2991672556868" y="240" width="1.0870886903215675" height="23"><g>
-    <title>~:0:&lt;method 'unbind' of 'torch._C._TensorBase' objects&gt; 0.17% (2 2 2.5101000000000002e-05 2.5101000000000002e-05)</title>
+<svg class="func_g" x="1176.471763851188" y="240" width="1.063096363792659" height="23"><g>
+    <title>~:0:&lt;method 'unbind' of 'torch._C._TensorBase' objects&gt; 0.17% (2 2 2.7700000000000002e-05 2.7700000000000002e-05)</title>
     <rect height="100%" width="100%" fill="rgb(230, 230, 255)" rx="2" ry="2" />
     <text alignment-baseline="central" x="4.0" y="12.0" font-size="12px" fill="rgb(0,0,0)">&lt;method 'unbind' of 'torch._C._TensorBase' objects&gt;</text>
 </g></svg>
-<svg class="func_g" x="1170.2991672556868" y="264" width="1.0870886903215675" height="23"><g>
-    <title>/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/torch/_tensor.py:920:__iter__ 0.17% (2 2 2.5101000000000002e-05 2.5101000000000002e-05)</title>
+<svg class="func_g" x="1176.471763851188" y="264" width="1.063096363792659" height="23"><g>
+    <title>/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/torch/_tensor.py:920:__iter__ 0.17% (2 2 2.7700000000000002e-05 2.7700000000000002e-05)</title>
     <rect height="100%" width="100%" fill="rgb(211, 211, 211)" rx="2" ry="2" />
     <text alignment-baseline="central" x="4.0" y="12.0" font-size="12px" fill="rgb(0,0,0)">__iter__</text>
 </g></svg>
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-    <title>/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/openpifpaf/decoder/cifcaf.py:224:__call__ 0.06% (2 2 8.900000000000001e-06 3.6901000000000005e-05)</title>
+<svg class="func_g" x="1176.471763851188" y="288" width="1.0630963637926585" height="23"><g>
+    <title>/opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/openpifpaf/decoder/cifcaf.py:224:__call__ 0.05% (2 2 8.3e-06 3.9e-05)</title>
     <rect height="100%" width="100%" fill="rgb(232, 232, 232)" rx="2" ry="2" />
     <text alignment-baseline="central" x="4.0" y="12.0" font-size="12px" fill="rgb(0,0,0)">__call__</text>
 </g></svg>
diff --git a/dev/cli_help.html b/dev/cli_help.html
index 9683a08a1..e27982270 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;trackingpose&#39;, &#39;cifdet&#39;,
-                        &#39;posesimilarity&#39;, &#39;cifcafdense&#39;, &#39;cifcaf&#39;]. (default:
+                        Decoders to be considered: [&#39;cifdet&#39;, &#39;cifcafdense&#39;,
+                        &#39;trackingpose&#39;, &#39;posesimilarity&#39;, &#39;cifcaf&#39;]. (default:
                         None)
   --seed-threshold SEED_THRESHOLD
                         minimum threshold for seeds (default: 0.2)
@@ -629,6 +629,9 @@ <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)
 
+CifCafDense decoder:
+  --dense-connections [DENSE_CONNECTIONS]
+
 trackingpose decoder:
   --trackingpose-track-recovery
   --trackingpose-single-seed
@@ -637,9 +640,6 @@ <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
 
-CifCafDense decoder:
-  --dense-connections [DENSE_CONNECTIONS]
-
 CifCaf decoder:
   --force-complete-pose
   --force-complete-caf-th FORCE_COMPLETE_CAF_TH
@@ -670,6 +670,35 @@ <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)
 
+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)
+
+SqueezeNet:
+  --squeezenet-no-pretrain
+                        use randomly initialized models (default: True)
+
 MobileNetV2:
   --mobilenetv2-no-pretrain
                         use randomly initialized models (default: True)
@@ -713,35 +742,6 @@ <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)
-
-SqueezeNet:
-  --squeezenet-no-pretrain
-                        use randomly initialized models (default: True)
-
 CompositeField4:
   --cf4-dropout CF4_DROPOUT
                         [experimental] zeroing probability of feature in head
@@ -912,6 +912,22 @@ <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)
+
+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:
@@ -968,22 +984,6 @@ <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)
-
 CompositeField4:
   --cf4-dropout CF4_DROPOUT
                         [experimental] zeroing probability of feature in head
@@ -1037,9 +1037,9 @@ <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;posesimilarity&#39;, &#39;cifcafdense&#39;, &#39;cifcaf&#39;,
-                        &#39;trackingpose&#39;]. (default: None)
+                        Decoders to be considered: [&#39;trackingpose&#39;,
+                        &#39;cifcafdense&#39;, &#39;cifcaf&#39;, &#39;posesimilarity&#39;, &#39;cifdet&#39;].
+                        (default: None)
   --seed-threshold SEED_THRESHOLD
                         minimum threshold for seeds (default: 0.2)
   --instance-threshold INSTANCE_THRESHOLD
@@ -1066,9 +1066,9 @@ <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)
 
-PoseSimilarity:
-  --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]
@@ -1098,9 +1098,9 @@ <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
+PoseSimilarity:
+  --posesimilarity-distance {crafted,euclidean,euclidean4,oks}
+  --posesimilarity-oks-inflate POSESIMILARITY_OKS_INFLATE
 
 logger:
   -q, --quiet           only show warning messages or above (default: False)
@@ -1233,31 +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)
 
-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)
-
 SqueezeNet:
   --squeezenet-no-pretrain
                         use randomly initialized models (default: True)
@@ -1305,6 +1280,31 @@ <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
@@ -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;cifcaf&#39;,
-                        &#39;posesimilarity&#39;, &#39;cifdet&#39;, &#39;trackingpose&#39;,
-                        &#39;cifcafdense&#39;]. (default: None)
+                        Decoders to be considered: [&#39;cifdet&#39;, &#39;trackingpose&#39;,
+                        &#39;cifcafdense&#39;, &#39;cifcaf&#39;, &#39;posesimilarity&#39;]. (default:
+                        None)
   --seed-threshold SEED_THRESHOLD
                         minimum threshold for seeds (default: 0.2)
   --instance-threshold INSTANCE_THRESHOLD
@@ -2179,6 +2179,13 @@ <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
@@ -2208,18 +2215,27 @@ <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)
+
+MobileNetV3:
+  --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:
@@ -2276,22 +2292,6 @@ <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)
-
 CompositeField4:
   --cf4-dropout CF4_DROPOUT
                         [experimental] zeroing probability of feature in head
@@ -2426,6 +2426,15 @@ <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]
+                                         [--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]
                                          [--squeezenet-no-pretrain]
                                          [--mobilenetv2-no-pretrain]
                                          [--mobilenetv3-no-pretrain]
@@ -2443,15 +2452,6 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
                                          [--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]
@@ -2480,6 +2480,31 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
   --input-width INPUT_WIDTH
   --input-height INPUT_HEIGHT
 
+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)
+
 SqueezeNet:
   --squeezenet-no-pretrain
                         use randomly initialized models (default: True)
@@ -2527,31 +2552,6 @@ <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
@@ -2618,15 +2618,6 @@ <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]
-                                           [--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]
                                            [--squeezenet-no-pretrain]
                                            [--mobilenetv2-no-pretrain]
                                            [--mobilenetv3-no-pretrain]
@@ -2644,6 +2635,15 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
                                            [--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]
@@ -2666,31 +2666,6 @@ <h1>Command Line<a class="headerlink" href="#command-line" title="Permalink to t
   --input-height INPUT_HEIGHT
   --minimum-deployment-target {iOS13,iOS14}
 
-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)
-
 SqueezeNet:
   --squeezenet-no-pretrain
                         use randomly initialized models (default: True)
@@ -2738,6 +2713,31 @@ <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 01e0e2a47..9a7b45d02 100644
--- a/dev/dev.html
+++ b/dev/dev.html
@@ -829,7 +829,7 @@ <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+22.g92e246e
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>OpenPifPaf 0.14.0+23.g6effda5
 </pre></div>
 </div>
 </div>
@@ -852,9 +852,7 @@ <h2>Build Guide<a class="headerlink" href="#build-guide" title="Permalink to thi
   libswscale      7.  1.100 /  7.  1.100
   libswresample   4. 10.100 /  4. 10.100
   libpostproc    57.  1.100 / 57.  1.100
-</pre></div>
-</div>
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Codecs:
+Codecs:
  D..... = Decoding supported
  .E.... = Encoding supported
  ..V... = Video codec
diff --git a/dev/performance.html b/dev/performance.html
index 1a93342b2..88e85829c 100644
--- a/dev/performance.html
+++ b/dev/performance.html
@@ -794,18 +794,18 @@ <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: 502.5ms
-DEBUG:openpifpaf.decoder.decoder:parallel execution with worker &lt;openpifpaf.decoder.decoder.DummyPool object at 0x7f5f79213df0&gt;
+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.multi:task 0
-DEBUG:openpifpaf.decoder.cifcaf:cpp annotations = 5 (12.7ms)
+DEBUG:openpifpaf.decoder.cifcaf:cpp annotations = 5 (14.5ms)
 INFO:openpifpaf.decoder.cifcaf:annotations 5: [16, 14, 13, 12, 12]
 INFO:openpifpaf.profiler:writing profile file profile_decoder.prof
-         386 function calls in 0.014 seconds
+         386 function calls in 0.016 seconds
 
    Ordered by: internal time
 
    ncalls  tottime  percall  cumtime  percall filename:lineno(function)
-        1    0.013    0.013    0.014    0.014 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/openpifpaf/decoder/cifcaf.py:224(__call__)
+        1    0.015    0.015    0.016    0.016 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/openpifpaf/decoder/cifcaf.py:224(__call__)
         5    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/openpifpaf/annotation.py:17(__init__)
        15    0.000    0.000    0.000    0.000 {method &#39;numpy&#39; of &#39;torch._C._TensorBase&#39; objects}
        10    0.000    0.000    0.000    0.000 {method &#39;reduce&#39; of &#39;numpy.ufunc&#39; objects}
@@ -813,92 +813,92 @@ <h2>Profile Decoder<a class="headerlink" href="#profile-decoder" title="Permalin
         2    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/logging/__init__.py:283(__init__)
         1    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/openpifpaf/visualizer/cif.py:47(predicted)
         2    0.000    0.000    0.000    0.000 {method &#39;flush&#39; of &#39;_io.TextIOWrapper&#39; objects}
-       10    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/numpy/core/fromnumeric.py:69(_wrapreduction)
-        1    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/openpifpaf/decoder/cifcaf.py:276(&lt;listcomp&gt;)
         1    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/openpifpaf/visualizer/caf.py:47(predicted)
         2    0.000    0.000    0.000    0.000 {method &#39;unbind&#39; of &#39;torch._C._TensorBase&#39; objects}
+       10    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/numpy/core/fromnumeric.py:69(_wrapreduction)
+        1    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/openpifpaf/decoder/cifcaf.py:276(&lt;listcomp&gt;)
        15    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/torch/_tensor.py:966(__array__)
-       10    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/numpy/core/fromnumeric.py:2188(sum)
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         2    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/logging/__init__.py:359(getMessage)
+       10    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/numpy/core/fromnumeric.py:2188(sum)
         5    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/openpifpaf/visualizer/base.py:114(indices)
+        1    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/site-packages/torch/_tensor.py:904(__len__)
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        10    0.000    0.000    0.000    0.000 &lt;__array_function__ internals&gt;:177(sum)
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         2    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/logging/__init__.py:1680(callHandlers)
-       24    0.000    0.000    0.000    0.000 {built-in method builtins.len}
-        2    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/logging/__init__.py:1088(emit)
+        2    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/logging/__init__.py:1600(_log)
        10    0.000    0.000    0.000    0.000 {built-in method numpy.core._multiarray_umath.implement_array_function}
+       24    0.000    0.000    0.000    0.000 {built-in method builtins.len}
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-        2    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/logging/__init__.py:1600(_log)
+        2    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/logging/__init__.py:1088(emit)
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+        2    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/logging/__init__.py:228(_releaseLock)
+        2    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/logging/__init__.py:1077(flush)
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-       16    0.000    0.000    0.000    0.000 {built-in method torch._C._has_torch_function_unary}
-        2    0.000    0.000    0.000    0.000 /opt/hostedtoolcache/Python/3.10.13/x64/lib/python3.10/logging/__init__.py:431(_format)
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 INFO:openpifpaf.network.trainer:training state written: cifar10_tutorial.pkl.optim.epoch001
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 INFO:openpifpaf.network.trainer:restoring params from before ema
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+INFO:openpifpaf.network.trainer:{&#39;type&#39;: &#39;train&#39;, &#39;epoch&#39;: 2, &#39;batch&#39;: 3000, &#39;n_batches&#39;: 3125, &#39;time&#39;: 0.015, &#39;data_time&#39;: 0.006, &#39;lr&#39;: 3e-06, &#39;loss&#39;: -10.834, &#39;head_losses&#39;: [-10.647, -0.188, 0.0]}
+INFO:openpifpaf.network.trainer:{&#39;type&#39;: &#39;train&#39;, &#39;epoch&#39;: 2, &#39;batch&#39;: 3050, &#39;n_batches&#39;: 3125, &#39;time&#39;: 0.02, &#39;data_time&#39;: 0.001, &#39;lr&#39;: 3e-06, &#39;loss&#39;: -10.812, &#39;head_losses&#39;: [-10.703, -0.109, 0.0]}
+INFO:openpifpaf.network.trainer:{&#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]}
 INFO:openpifpaf.network.trainer:applying ema
-INFO:openpifpaf.network.trainer:{&#39;type&#39;: &#39;train-epoch&#39;, &#39;epoch&#39;: 3, &#39;loss&#39;: -10.69299, &#39;head_losses&#39;: [-10.53501, -0.15797, 0.0], &#39;time&#39;: 76.8, &#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;: -10.77339, &#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: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;: -10.74472, &#39;head_losses&#39;: [-10.56948, -0.17524, 0.0], &#39;time&#39;: 12.3}
+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}
 </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 16:16:40,530&#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;: -10.527, &#39;head_losses&#39;: [-10.342, -0.185, 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;2023-09-28 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]}
 </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">
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+<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]}]
 </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 41%&#39;, &#39;ship 37%&#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 38%&#39;, &#39;bird 32%&#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;car 47%&#39;, &#39;truck 44%&#39;, &#39;ship 30%&#39;]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;truck 47%&#39;, &#39;car 47%&#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;truck 33%&#39;, &#39;plane 32%&#39;, &#39;ship 32%&#39;, &#39;car 32%&#39;]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&#39;ship 33%&#39;, &#39;plane 32%&#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;car 33%&#39;, &#39;ship 30%&#39;]
+<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;]
 </pre></div>
 </div>
 </div>
@@ -994,17 +994,17 @@ <h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this
         &quot;truck&quot;
     ],
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-        0.113,
-        0.276,
-        0.404,
-        0.283,
-        0.359,
-        0.442,
-        0.49,
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+        0.593,
+        0.169,
+        0.393,
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+        0.188,
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+        0.488
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@@ -1015,9 +1015,9 @@ <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+22.g92e246e&quot;,
+    &quot;version&quot;: &quot;0.14.0+23.g6effda5&quot;,
     &quot;dataset&quot;: &quot;cifar10&quot;,
-    &quot;total_time&quot;: 33.49907245299994,
+    &quot;total_time&quot;: 32.99185662399998,
     &quot;checkpoint&quot;: &quot;cifar10_tutorial.pkl.epoch003&quot;,
     &quot;count_ops&quot;: [
         421736880.0,
@@ -1025,8 +1025,8 @@ <h2>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this
     ],
     &quot;file_size&quot;: 438179,
     &quot;n_images&quot;: 10000,
-    &quot;decoder_time&quot;: 8.541962761009358,
-    &quot;nn_time&quot;: 14.321056258003296
+    &quot;decoder_time&quot;: 8.64063907301238,
+    &quot;nn_time&quot;: 8.361834807997298
 }
 </pre></div>
 </div>
diff --git a/dev/plugins_nuscenes.html b/dev/plugins_nuscenes.html
index e0681a1d1..9f5da0d9d 100644
--- a/dev/plugins_nuscenes.html
+++ b/dev/plugins_nuscenes.html
@@ -544,7 +544,7 @@ <h2>Prediction<a class="headerlink" href="#prediction" title="Permalink to this
 INFO:__main__:Running Python 3.10.13
 INFO:__main__:Running PyTorch 2.0.1+cpu
 INFO:openpifpaf.predictor:neural network device: cpu (CUDA available: False, count: 0)
-INFO:openpifpaf.decoder.cifdet:annotations 3, decoder = 18.6ms
+INFO:openpifpaf.decoder.cifdet:annotations 3, decoder = 16.8ms
 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 f25245bee..3c677835f 100644
--- a/dev/plugins_overview.html
+++ b/dev/plugins_overview.html
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- &#39;resnet18&#39;: &lt;object at 0x7fb137312c70&gt;,
+ &#39;resnet18&#39;: &lt;object at 0x7f4d5a682c70&gt;,
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- &#39;shufflenetv2x1&#39;: &lt;object at 0x7fb137312c70&gt;,
- &#39;shufflenetv2x2&#39;: &lt;object at 0x7fb137312c70&gt;,
+ &#39;resnet101&#39;: &lt;object at 0x7f4d5a682c70&gt;,
+ &#39;resnet152&#39;: &lt;object at 0x7f4d5a682c70&gt;,
+ &#39;shufflenetv2x1&#39;: &lt;object at 0x7f4d5a682c70&gt;,
+ &#39;shufflenetv2x2&#39;: &lt;object at 0x7f4d5a682c70&gt;,
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  &#39;shufflenetv2k16-withdense&#39;: &#39;http://github.com/vita-epfl/openpifpaf-torchhub/releases/download/v0.12b4/shufflenetv2k16-210221-131426-cocokp-o10s-627d901e.pkl&#39;,
  &#39;shufflenetv2k30&#39;: &#39;http://github.com/openpifpaf/torchhub/releases/download/v0.13/shufflenetv2k30-210821-003923-cocokp-slurm726072-edge513-o10s-5fe1c400.pkl&#39;,
- &#39;shufflenetv2k44&#39;: &lt;object at 0x7fb137312c70&gt;,
+ &#39;shufflenetv2k44&#39;: &lt;object at 0x7f4d5a682c70&gt;,
  &#39;swin_s&#39;: &#39;http://github.com/dmizr/openpifpaf/releases/download/v0.12.14/swin_s_fpn_lvl_3_lr_5e-5_resumed-d286d41a.pkl&#39;,
  &#39;swin_b&#39;: &#39;http://github.com/dmizr/openpifpaf/releases/download/v0.12.14/swin_b_fpn_lvl_3_lr_5e-5_resumed-fa951ce0.pkl&#39;,
  &#39;swin_t_input_upsample&#39;: &#39;http://github.com/dmizr/openpifpaf/releases/download/v0.12.14/swin_t_input_upsample_no_fpn_lr_5e-5_resumed-e0681112.pkl&#39;,
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- &#39;tshufflenetv2k16&#39;: &lt;object at 0x7fb137312c70&gt;,
+ &#39;tshufflenetv2k16&#39;: &lt;object at 0x7f4d5a682c70&gt;,
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diff --git a/dev/predict_api.html b/dev/predict_api.html
index 02b8c2c5f..a5594041d 100644
--- a/dev/predict_api.html
+++ b/dev/predict_api.html
@@ -538,7 +538,7 @@ <h1>Prediction API<a class="headerlink" href="#prediction-api" title="Permalink
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>OpenPifPaf version 0.14.0+22.g92e246e
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>OpenPifPaf version 0.14.0+23.g6effda5
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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 0x7f78c3e898a0&gt;
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diff --git a/dev/predict_cli.html b/dev/predict_cli.html
index 3f719cbc3..6f6a6cfa5 100644
--- a/dev/predict_cli.html
+++ b/dev/predict_cli.html
@@ -1194,6 +1194,8 @@ <h2>Debug<a class="headerlink" href="#debug" title="Permalink to this headline">
   )
 )
 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
@@ -1201,8 +1203,6 @@ <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
-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
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 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: 535.4ms
-DEBUG:openpifpaf.decoder.decoder:parallel execution with worker &lt;openpifpaf.decoder.decoder.DummyPool object at 0x7fbdc6603e50&gt;
+DEBUG:openpifpaf.decoder.decoder:nn processing time: 474.6ms
+DEBUG:openpifpaf.decoder.decoder:parallel execution with worker &lt;openpifpaf.decoder.decoder.DummyPool object at 0x7f1fe816be80&gt;
 DEBUG:openpifpaf.decoder.multi:task 0
-DEBUG:openpifpaf.decoder.cifcaf:cpp annotations = 5 (12.0ms)
+DEBUG:openpifpaf.decoder.cifcaf:cpp annotations = 5 (12.6ms)
 INFO:openpifpaf.decoder.cifcaf:annotations 5: [16, 14, 13, 12, 12]
-DEBUG:openpifpaf.decoder.decoder:time: nn = 535.7ms, dec = 13.4ms
+DEBUG:openpifpaf.decoder.decoder:time: nn = 474.9ms, dec = 13.9ms
 INFO:openpifpaf.predictor:batch 0: coco/000000081988.jpg
 DEBUG:__main__:json output = coco/000000081988.jpg.predictions.json
 DEBUG:__main__:image output = coco/000000081988.jpg.predictions.jpeg
diff --git a/dev/searchindex.js b/dev/searchindex.js
index 81f550a0e..b4b8398cb 100644
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diff --git a/dev/train.html b/dev/train.html
index 7bfdb1c79..3ba7e3038 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-705b4af8-1605-4285-b1fe-17d3850f1fbd">
-<span class="eqno">(1)<a class="headerlink" href="#equation-705b4af8-1605-4285-b1fe-17d3850f1fbd" title="Permalink to this equation">#</a></span>\[\begin{align}
+<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>\[\begin{align}
     \lambda \cdot c &amp;\leq \sqrt{\epsilon}
 \end{align}\]</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).