Update README.md

README.md

@@ -78,7 +78,7 @@ You will get the results:
  Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83868
 ```
 
-To measure accuracy, download [COCO-annotations for Pycocotools](https://images.cocodataset.org/annotations/annotations_trainval2017.zip).
+To measure accuracy, download [COCO-annotations for Pycocotools](https://images.cocodataset.org/annotations/annotations_trainval2017.zip) to the `./coco/annotations/instances_val2017.json`
 
 ## Training
 
@@ -149,6 +149,7 @@ python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inferen
 
 ## Export
 
+**Pytorch to CoreML (and inference on MacOS/iOS)** <a href="https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7CoreML.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
 
 **Pytorch to ONNX with NMS (and inference)** <a href="https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7onnx.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
 ```shell