Gave direct link to implementation readmes

vision/classification_and_detection/README.md

@@ -2,7 +2,20 @@
 
 This is the reference implementation for MLPerf Inference Classification and Object Detection benchmarks
 
-Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker. 
+## Automated Run Commands
+Please see the [new docs site](https://docs.mlcommons.org/inference/benchmarks) for an automated way to run this benchmark across different available implementations and do an end-to-end submission with or without docker.
+
+### ResNet50
+- [ResNet50 MLCommons Python Reference Implementation](https://docs.mlcommons.org/inference/benchmarks/image_classification/resnet50/#__tabbed_1_1) (Onnxruntime, Tensorflow)
+- [ResNet50 Nvidia Implementation](https://docs.mlcommons.org/inference/benchmarks/image_classification/resnet50/#__tabbed_1_2) (TensorRT)
+- [ResNet50 Intel Implementation](https://docs.mlcommons.org/inference/benchmarks/image_classification/resnet50/#__tabbed_1_3) (PyTorch)
+- [ResNet50 MLCommons C++ Implementation](https://docs.mlcommons.org/inference/benchmarks/image_classification/resnet50/#__tabbed_1_5) (Onnxruntime)
+
+### Retinanet
+- [Retinanet MLCommons Python Reference Implementation](https://docs.mlcommons.org/inference/benchmarks/object_detection/retinanet/#__tabbed_1_1) (PyTorch, Onnxruntime)
+- [Retinanet Nvidia Implementation](https://docs.mlcommons.org/inference/benchmarks/object_detection/retinanet/#__tabbed_1_2) (TensorRT)
+- [Retinanet Intel Implementation](https://docs.mlcommons.org/inference/benchmarks/object_detection/retinanet/#__tabbed_1_3) (PyTorch)
+- [Retinanet MLCommons C++ Implementation](https://docs.mlcommons.org/inference/benchmarks/object_detection/retinanet/#__tabbed_1_5) (Onnxruntime)
 
 You can find a short tutorial on how to use this benchmark [here](https://github.com/mlperf/inference/blob/master/vision/classification_and_detection/GettingStarted.ipynb).