Merge pull request #202097 from daholste/daholste/remove_dupe_model_name

Remove duplicate maskrcnn_resnet50_fpn in model name list

Author: PRMerger17

articles/machine-learning/how-to-auto-train-image-models.md

@@ -267,7 +267,7 @@ Task |  Model algorithms | String literal syntax<br> ***`default_model`\**** den
 ---|----------|----------
 Image classification<br> (multi-class and multi-label)| **MobileNet**: Light-weighted models for mobile applications <br> **ResNet**: Residual networks<br> **ResNeSt**: Split attention networks<br> **SE-ResNeXt50**: Squeeze-and-Excitation networks<br> **ViT**: Vision transformer networks| `mobilenetv2`   <br>`resnet18` <br>`resnet34` <br> `resnet50`  <br> `resnet101` <br> `resnet152`    <br> `resnest50` <br> `resnest101`  <br> `seresnext`  <br> `vits16r224` (small) <br> ***`vitb16r224`\**** (base) <br>`vitl16r224` (large)|
 Object detection | **YOLOv5**: One stage object detection model   <br>  **Faster RCNN ResNet FPN**: Two stage object detection models  <br> **RetinaNet ResNet FPN**: address class imbalance with Focal Loss <br> <br>*Note: Refer to [`model_size` hyperparameter](reference-automl-images-hyperparameters.md#model-specific-hyperparameters) for YOLOv5 model sizes.*| ***`yolov5`\**** <br> `fasterrcnn_resnet18_fpn` <br> `fasterrcnn_resnet34_fpn` <br> `fasterrcnn_resnet50_fpn` <br> `fasterrcnn_resnet101_fpn` <br> `fasterrcnn_resnet152_fpn` <br> `retinanet_resnet50_fpn` 
-Instance segmentation | **MaskRCNN ResNet FPN**| `maskrcnn_resnet18_fpn` <br> `maskrcnn_resnet34_fpn` <br> ***`maskrcnn_resnet50_fpn`\****  <br> `maskrcnn_resnet101_fpn` <br> `maskrcnn_resnet152_fpn` <br>`maskrcnn_resnet50_fpn`
+Instance segmentation | **MaskRCNN ResNet FPN**| `maskrcnn_resnet18_fpn` <br> `maskrcnn_resnet34_fpn` <br> ***`maskrcnn_resnet50_fpn`\****  <br> `maskrcnn_resnet101_fpn` <br> `maskrcnn_resnet152_fpn`
 
 
 In addition to controlling the model algorithm, you can also tune hyperparameters used for model training. While many of the hyperparameters exposed are model-agnostic, there are instances where hyperparameters are task-specific or model-specific. [Learn more about the available hyperparameters for these instances](reference-automl-images-hyperparameters.md).