fixed incorrect path in GETTING_STARTED.md
Summary:
updated path from "MODEL_ZOO.md" to "DENSEPOSE_IUV.md#ModelZoo" and "DENSEPOSE_CSE.md#ModelZoo".
Because the file "MODEL_ZOO.md" does not exist in that path now.
Pull Request resolved:
## Inference with Pre-trained Models
-1. Pick a model and its config file from [Model Zoo](MODEL_ZOO.md), for example [densepose_rcnn_R_50_FPN_s1x.yaml](../configs/densepose_rcnn_R_50_FPN_s1x.yaml)
+1. Pick a model and its config file from [Model Zoo(IUV)](DENSEPOSE_IUV.md#ModelZoo), [Model Zoo(CSE)](DENSEPOSE_CSE.md#ModelZoo), for example [densepose_rcnn_R_50_FPN_s1x.yaml](../configs/densepose_rcnn_R_50_FPN_s1x.yaml)
2. Run the [Apply Net](TOOL_APPLY_NET.md) tool to visualize the results or save the to disk. For example, to use contour visualization for DensePose, one can run:
```bash
python apply_net.py show configs/densepose_rcnn_R_50_FPN_s1x.yaml densepose_rcnn_R_50_FPN_s1x.pkl image.jpg dp_contour,bbox --output image_densepose_contour.png
@@ -22,7 +22,7 @@ datasets/coco/
</pre>
To train a model one can use the [train_net.py](../train_net.py) script.
-This script was used to train all DensePose models in [Model Zoo](MODEL_ZOO.md).
+This script was used to train all DensePose models in [Model Zoo(IUV)](DENSEPOSE_IUV.md#ModelZoo), [Model Zoo(CSE)](DENSEPOSE_CSE.md#ModelZoo).
For example, to launch end-to-end DensePose-RCNN training with ResNet-50 FPN backbone
on 8 GPUs following the s1x schedule, one can run
```bash
