add staging model links

Author: bhimar

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

@@ -375,9 +375,9 @@ In addition to supporting any model from HuggingFace Transfomers and MMDetection
 
 Task |  model architectures | String literal syntax
 ---|----------|----------
-Image classification<br> (multi-class and multi-label)| **[BEiT](https://ml.azure.com/registries/azureml-preview/models/microsoft-beit-base-patch16-224-pt22k-ft22k/version/2)** <br> **[ViT](https://ml.azure.com/registries/azureml-preview/models/google-vit-base-patch16-224/version/2)** <br> **[DeiT](https://ml.azure.com/registries/azureml-preview/models/facebook-deit-base-patch16-224/version/2)** <br> **[SwinV2](https://ml.azure.com/registries/azureml-preview/models/microsoft-swinv2-base-patch4-window12-192-22k/version/2)** <br> TODO update links to staging registry | `microsoft/beit-base-patch16-224-pt22k-ft22k`<br> `google/vit-base-patch16-224`<br> `facebook/deit-base-patch16-224`<br> `microsoft/swinv2-base-patch4-window12-192-22k`
-Object Detection | **[Sparse R-CNN]()** <br> **[Deformable DETR]()** <br> **[VFNet]()** <br> **[YOLOF]()** <br> **[Swin]()** <br> TODO update links to staging registry | `sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco`<br> `sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco` <br> `deformable_detr_twostage_refine_r50_16x2_50e_coco` <br> `vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco` <br> `vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco` <br> `yolof_r50_c5_8x8_1x_coco` <br> `mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco`
-Instance Segmentation | **[Swin]()** <br> TODO update links to staging registry | `mask_rcnn_swin-t-p4-w7_fpn_1x_coco`
+Image classification<br> (multi-class and multi-label)| **BEiT** <br> **ViT** <br> **DeiT** <br> **SwinV2]** | [`microsoft/beit-base-patch16-224-pt22k-ft22k`](https://ml.azure.com/registries/azureml-staging/models/microsoft-beit-base-patch16-224-pt22k-ft22k/version/3)<br> [`google/vit-base-patch16-224`](https://ml.azure.com/registries/azureml-staging/models/google-vit-base-patch16-224/version/3)<br> [`facebook/deit-base-patch16-224`](https://ml.azure.com/registries/azureml-staging/models/facebook-deit-base-patch16-224/version/3)<br> [`microsoft/swinv2-base-patch4-window12-192-22k`](https://ml.azure.com/registries/azureml-staging/models/microsoft-swinv2-base-patch4-window12-192-22k/version/3)
+Object Detection | **Sparse R-CNN** <br> **Deformable DETR** <br> **VFNet** <br> **YOLOF** <br> **Swin** | [`sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco`](https://ml.azure.com/registries/azureml-staging/models/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/version/1)<br> [`sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco`](https://ml.azure.com/registries/azureml-staging/models/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/version/1) <br> [`deformable_detr_twostage_refine_r50_16x2_50e_coco`](https://ml.azure.com/registries/azureml-staging/models/deformable_detr_twostage_refine_r50_16x2_50e_coco/version/1) <br> [`vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco`](https://ml.azure.com/registries/azureml-staging/models/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/version/1) <br> [`vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco`](https://ml.azure.com/registries/azureml-staging/models/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/version/1) <br> [`yolof_r50_c5_8x8_1x_coco`](https://ml.azure.com/registries/azureml-staging/models/yolof_r50_c5_8x8_1x_coco/version/1)
+Instance Segmentation | **Swin** | [`mask_rcnn_swin-t-p4-w7_fpn_1x_coco`](https://ml.azure.com/registries/azureml-staging/models/mask_rcnn_swin-t-p4-w7_fpn_1x_coco/version/1)
 
 We constantly update the list of curated models. You can get the most up to date list of the curated models for a given task using the Python SDK:
 ```