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felixdittrich92 committed Mar 15, 2024
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2 changes: 1 addition & 1 deletion latest/_modules/doctr/models/classification/zoo.html
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Expand Up @@ -290,7 +290,7 @@ <h1>Source code for doctr.models.classification.zoo</h1><div class="highlight"><
<span class="n">_model</span> <span class="o">=</span> <span class="n">classification</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="n">arch</span><span class="p">](</span><span class="n">pretrained</span><span class="o">=</span><span class="n">pretrained</span><span class="p">)</span>
<span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="n">_model</span><span class="o">.</span><span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">])</span>
<span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;std&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;std&quot;</span><span class="p">,</span> <span class="n">_model</span><span class="o">.</span><span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;std&quot;</span><span class="p">])</span>
<span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;batch_size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;batch_size&quot;</span><span class="p">,</span> <span class="mi">64</span><span class="p">)</span>
<span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;batch_size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;batch_size&quot;</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span>
<span class="n">input_shape</span> <span class="o">=</span> <span class="n">_model</span><span class="o">.</span><span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;input_shape&quot;</span><span class="p">][:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="n">is_tf_available</span><span class="p">()</span> <span class="k">else</span> <span class="n">_model</span><span class="o">.</span><span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;input_shape&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">:]</span>
<span class="n">predictor</span> <span class="o">=</span> <span class="n">CropOrientationPredictor</span><span class="p">(</span>
<span class="n">PreProcessor</span><span class="p">(</span><span class="n">input_shape</span><span class="p">,</span> <span class="n">preserve_aspect_ratio</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">symmetric_pad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">),</span> <span class="n">_model</span>
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2 changes: 1 addition & 1 deletion latest/_modules/doctr/models/detection/zoo.html
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Expand Up @@ -310,7 +310,7 @@ <h1>Source code for doctr.models.detection.zoo</h1><div class="highlight"><pre>

<span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="n">_model</span><span class="o">.</span><span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">])</span>
<span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;std&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;std&quot;</span><span class="p">,</span> <span class="n">_model</span><span class="o">.</span><span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;std&quot;</span><span class="p">])</span>
<span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;batch_size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;batch_size&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;batch_size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;batch_size&quot;</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">predictor</span> <span class="o">=</span> <span class="n">DetectionPredictor</span><span class="p">(</span>
<span class="n">PreProcessor</span><span class="p">(</span><span class="n">_model</span><span class="o">.</span><span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;input_shape&quot;</span><span class="p">][:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="n">is_tf_available</span><span class="p">()</span> <span class="k">else</span> <span class="n">_model</span><span class="o">.</span><span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;input_shape&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">:],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">),</span>
<span class="n">_model</span><span class="p">,</span>
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2 changes: 1 addition & 1 deletion latest/_modules/doctr/models/recognition/zoo.html
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Expand Up @@ -293,7 +293,7 @@ <h1>Source code for doctr.models.recognition.zoo</h1><div class="highlight"><pre

<span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="n">_model</span><span class="o">.</span><span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">])</span>
<span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;std&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;std&quot;</span><span class="p">,</span> <span class="n">_model</span><span class="o">.</span><span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;std&quot;</span><span class="p">])</span>
<span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;batch_size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;batch_size&quot;</span><span class="p">,</span> <span class="mi">32</span><span class="p">)</span>
<span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;batch_size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;batch_size&quot;</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span>
<span class="n">input_shape</span> <span class="o">=</span> <span class="n">_model</span><span class="o">.</span><span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;input_shape&quot;</span><span class="p">][:</span><span class="mi">2</span><span class="p">]</span> <span class="k">if</span> <span class="n">is_tf_available</span><span class="p">()</span> <span class="k">else</span> <span class="n">_model</span><span class="o">.</span><span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;input_shape&quot;</span><span class="p">][</span><span class="o">-</span><span class="mi">2</span><span class="p">:]</span>
<span class="n">predictor</span> <span class="o">=</span> <span class="n">RecognitionPredictor</span><span class="p">(</span><span class="n">PreProcessor</span><span class="p">(</span><span class="n">input_shape</span><span class="p">,</span> <span class="n">preserve_aspect_ratio</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">),</span> <span class="n">_model</span><span class="p">)</span>

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10 changes: 10 additions & 0 deletions latest/_sources/using_doctr/using_models.rst.txt
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Expand Up @@ -298,6 +298,16 @@ For instance, this snippet instantiates an end-to-end ocr_predictor working with
from doctr.model import ocr_predictor
model = ocr_predictor('linknet_resnet18', pretrained=True, assume_straight_pages=False, preserve_aspect_ratio=True)
Additionally, you can change the batch size of the underlying detection and recognition predictors to optimize the performance depending on your hardware:

* `det_bs`: batch size for the detection model (default: 2)
* `reco_bs`: batch size for the recognition model (default: 128)

.. code:: python3
from doctr.model import ocr_predictor
model = ocr_predictor(pretrained=True, det_bs=4, reco_bs=1024)
To modify the output structure you can pass the following arguments to the predictor which will be handled by the underlying `DocumentBuilder`:

* `resolve_lines`: whether words should be automatically grouped into lines (default: True)
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2 changes: 1 addition & 1 deletion latest/searchindex.js
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9 changes: 9 additions & 0 deletions latest/using_doctr/using_models.html
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Expand Up @@ -913,6 +913,15 @@ <h3>Two-stage approaches<a class="headerlink" href="#two-stage-approaches" title
<span class="n">model</span> <span class="o">=</span> <span class="n">ocr_predictor</span><span class="p">(</span><span class="s1">&#39;linknet_resnet18&#39;</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">assume_straight_pages</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">preserve_aspect_ratio</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<p>Additionally, you can change the batch size of the underlying detection and recognition predictors to optimize the performance depending on your hardware:</p>
<ul class="simple">
<li><p><cite>det_bs</cite>: batch size for the detection model (default: 2)</p></li>
<li><p><cite>reco_bs</cite>: batch size for the recognition model (default: 128)</p></li>
</ul>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">doctr.model</span> <span class="kn">import</span> <span class="n">ocr_predictor</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">ocr_predictor</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">det_bs</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">reco_bs</span><span class="o">=</span><span class="mi">1024</span><span class="p">)</span>
</pre></div>
</div>
<p>To modify the output structure you can pass the following arguments to the predictor which will be handled by the underlying <cite>DocumentBuilder</cite>:</p>
<ul class="simple">
<li><p><cite>resolve_lines</cite>: whether words should be automatically grouped into lines (default: True)</p></li>
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2 changes: 1 addition & 1 deletion v0.1.0/_modules/doctr/models/classification/zoo.html
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Expand Up @@ -290,7 +290,7 @@ <h1>Source code for doctr.models.classification.zoo</h1><div class="highlight"><
<span class="n">_model</span> <span class="o">=</span> <span class="n">classification</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="n">arch</span><span class="p">](</span><span class="n">pretrained</span><span class="o">=</span><span class="n">pretrained</span><span class="p">)</span>
<span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="n">_model</span><span class="o">.</span><span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">])</span>
<span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;std&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;std&quot;</span><span class="p">,</span> <span class="n">_model</span><span class="o">.</span><span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;std&quot;</span><span class="p">])</span>
<span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;batch_size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;batch_size&quot;</span><span class="p">,</span> <span class="mi">64</span><span class="p">)</span>
<span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;batch_size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;batch_size&quot;</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span>
<span class="n">input_shape</span> <span class="o">=</span> <span class="n">_model</span><span class="o">.</span><span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;input_shape&quot;</span><span class="p">][:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="n">is_tf_available</span><span class="p">()</span> <span class="k">else</span> <span class="n">_model</span><span class="o">.</span><span class="n">cfg</span><span class="p">[</span><span class="s2">&quot;input_shape&quot;</span><span class="p">][</span><span class="mi">1</span><span class="p">:]</span>
<span class="n">predictor</span> <span class="o">=</span> <span class="n">CropOrientationPredictor</span><span class="p">(</span>
<span class="n">PreProcessor</span><span class="p">(</span><span class="n">input_shape</span><span class="p">,</span> <span class="n">preserve_aspect_ratio</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">symmetric_pad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">),</span> <span class="n">_model</span>
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10 changes: 10 additions & 0 deletions v0.1.0/_sources/using_doctr/using_models.rst.txt
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Expand Up @@ -298,6 +298,16 @@ For instance, this snippet instantiates an end-to-end ocr_predictor working with
from doctr.model import ocr_predictor
model = ocr_predictor('linknet_resnet18', pretrained=True, assume_straight_pages=False, preserve_aspect_ratio=True)
Additionally, you can change the batch size of the underlying detection and recognition predictors to optimize the performance depending on your hardware:

* `det_bs`: batch size for the detection model (default: 2)
* `reco_bs`: batch size for the recognition model (default: 128)

.. code:: python3
from doctr.model import ocr_predictor
model = ocr_predictor(pretrained=True, det_bs=4, reco_bs=1024)
To modify the output structure you can pass the following arguments to the predictor which will be handled by the underlying `DocumentBuilder`:

* `resolve_lines`: whether words should be automatically grouped into lines (default: True)
Expand Down
2 changes: 1 addition & 1 deletion v0.1.0/searchindex.js
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9 changes: 9 additions & 0 deletions v0.1.0/using_doctr/using_models.html
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Expand Up @@ -913,6 +913,15 @@ <h3>Two-stage approaches<a class="headerlink" href="#two-stage-approaches" title
<span class="n">model</span> <span class="o">=</span> <span class="n">ocr_predictor</span><span class="p">(</span><span class="s1">&#39;linknet_resnet18&#39;</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">assume_straight_pages</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">preserve_aspect_ratio</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<p>Additionally, you can change the batch size of the underlying detection and recognition predictors to optimize the performance depending on your hardware:</p>
<ul class="simple">
<li><p><cite>det_bs</cite>: batch size for the detection model (default: 2)</p></li>
<li><p><cite>reco_bs</cite>: batch size for the recognition model (default: 128)</p></li>
</ul>
<div class="highlight-python3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">doctr.model</span> <span class="kn">import</span> <span class="n">ocr_predictor</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">ocr_predictor</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">det_bs</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">reco_bs</span><span class="o">=</span><span class="mi">1024</span><span class="p">)</span>
</pre></div>
</div>
<p>To modify the output structure you can pass the following arguments to the predictor which will be handled by the underlying <cite>DocumentBuilder</cite>:</p>
<ul class="simple">
<li><p><cite>resolve_lines</cite>: whether words should be automatically grouped into lines (default: True)</p></li>
Expand Down
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