@@ -354,9 +354,9 @@
             <span class="bp">self</span><span class="o">.</span><span class="n">update_lr</span><span class="p">(</span><span class="n">context</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">context</span><span class="o">.</span><span class="n">epoch</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="YoloV5WarmupLRCallback"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.callbacks.YoloV5WarmupLRCallback">[docs]</a><span class="k">class</span> <span class="nc">YoloV5WarmupLRCallback</span><span class="p">(</span><span class="n">LRCallbackBase</span><span class="p">):</span>
+<div class="viewcode-block" id="YoloV5WarmupLRCallback"><a class="viewcode-back" href="../../../../super_gradients.training.utils.html#super_gradients.training.utils.callbacks.YoloV5WarmupLRCallback">[docs]</a><span class="k">class</span> <span class="nc">YoloWarmupLRCallback</span><span class="p">(</span><span class="n">LRCallbackBase</span><span class="p">):</span>
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">YoloV5WarmupLRCallback</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">Phase</span><span class="o">.</span><span class="n">TRAIN_BATCH_END</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">YoloWarmupLRCallback</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">Phase</span><span class="o">.</span><span class="n">TRAIN_BATCH_END</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 
     <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
         <span class="n">lr_warmup_epochs</span> <span class="o">=</span> <span class="n">get_param</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">training_params</span><span class="p">,</span> <span class="s1">&#39;lr_warmup_epochs&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
@@ -588,7 +588,7 @@
                           <span class="p">}</span>
 
 <span class="n">LR_WARMUP_CLS_DICT</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;linear_step&quot;</span><span class="p">:</span> <span class="n">WarmupLRCallback</span><span class="p">,</span>
-                      <span class="s2">&quot;yolov5_warmup&quot;</span><span class="p">:</span> <span class="n">YoloV5WarmupLRCallback</span><span class="p">}</span>
+                      <span class="s2">&quot;yolov5_warmup&quot;</span><span class="p">:</span> <span class="n">YoloWarmupLRCallback</span><span class="p">}</span>
 </pre></div>
 
            </div>

@@ -103,7 +103,7 @@
 <span class="sd">        :param model: Union[SgModule, nn.Module], the training model to construct the EMA model by</span>
 <span class="sd">                    IMPORTANT: WHEN THE APPLICATION OF EMA ONLY ON A SUBSET OF ATTRIBUTES IS DESIRED, WRAP THE NN.MODULE</span>
 <span class="sd">                    AS SgModule AND OVERWRITE get_include_attributes() AND get_exclude_attributes() AS DESIRED (SEE</span>
-<span class="sd">                    YoLoV5Base IMPLEMENTATION IN super_gradients.trainer.models.yolov5_base.py AS AN EXAMPLE).</span>
+<span class="sd">                    YoLoBase IMPLEMENTATION IN super_gradients.trainer.models.yolov5_base.py AS AN EXAMPLE).</span>
 <span class="sd">        :param decay: the maximum decay value. as the training process advances, the decay will climb towards this value</span>
 <span class="sd">                      until the EMA_t+1 = EMA_t * decay + TRAINING_MODEL * (1- decay)</span>
 <span class="sd">        :param beta: the exponent coefficient. The higher the beta, the sooner in the training the decay will saturate to</span>

@@ -3008,7 +3008,7 @@
         <li><a href="super_gradients.training.losses.html#super_gradients.training.losses.yolo_v5_loss.YoLoV5DetectionLoss">(class in super_gradients.training.losses.yolo_v5_loss)</a>
 </li>
       </ul></li>
-      <li><a href="super_gradients.training.utils.html#super_gradients.training.utils.callbacks.YoloV5WarmupLRCallback">YoloV5WarmupLRCallback (class in super_gradients.training.utils.callbacks)</a>
+      <li><a href="super_gradients.training.utils.html#super_gradients.training.utils.callbacks.YoloV5WarmupLRCallback">YoloWarmupLRCallback (class in super_gradients.training.utils.callbacks)</a>
 </li>
   </ul></td>
 </tr></table>

@@ -272,7 +272,7 @@ LR climbs from warmup_initial_lr with even steps to initial lr. When warmup_init
 
 <dl class="py class">
 <dt class="sig sig-object py" id="super_gradients.training.utils.callbacks.YoloV5WarmupLRCallback">
-<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.utils.callbacks.</span></span><span class="sig-name descname"><span class="pre">YoloV5WarmupLRCallback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/sup
+<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">super_gradients.training.utils.callbacks.</span></span><span class="sig-name descname"><span class="pre">YoloWarmupLRCallback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/super
 <dd><p>Bases: <a class="reference internal" href="#super_gradients.training.utils.callbacks.LRCallbackBase" title="super_gradients.training.utils.callbacks.LRCallbackBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">super_gradients.training.utils.callbacks.LRCallbackBase</span></code></a></p>
 </dd></dl>