Update README.md (#2)

- Update README.md (e6802a40694d0dfdf6476717bed465fe9b689231)


Co-authored-by: bob chesebrough <[email protected]>

README.md

@@ -42,11 +42,12 @@ Swin Transformer achieves strong performance on COCO object detection (58.7 box
 
 # Videos
 
-![MiDaS Depth Estimation | Intel Technology](https://cdn-uploads.huggingface.co/production/uploads/641bd18baebaa27e0753f2c9/u-KwRFIQhMWiFraSTTBkc.png)
 
+[![MiDaS Depth Estimation - Intel Technology](https://img.youtube.com/vi/UjaeNNFf9sE/0.jpg)](https://www.youtube.com/watch?v=UjaeNNFf9sE)
 MiDaS Depth Estimation is a machine learning model from Intel Labs for monocular depth estimation. It was trained on up to 12 datasets and covers both in-and outdoor scenes. Multiple different MiDaS models are available, ranging from high quality depth estimation to lightweight models for mobile downstream tasks (https://github.com/isl-org/MiDaS).
 
 
+
 ## Model description
 
 This Midas 3.1 DPT model uses the [SwinV2 Philosophy]( https://huggingface.co/docs/transformers/en/model_doc/swinv2) model as backbone and uses a different approach to Vision that Beit, where Swin backbones focus more on using a hierarchical approach.
@@ -120,6 +121,7 @@ depth
 or one can use the pipeline API:
 from transformers import pipeline
 
+```python
 pipe = pipeline(task="depth-estimation", model="Intel/dpt-swinv2-large-384")
 result = pipe("http://images.cocodataset.org/val2017/000000181816.jpg")
 result["depth"]