diff --git a/README.md b/README.md
index 154df8298fab5ecf322016157858e08cd1bccbe1..bd36047f79fb3db977e768f9231ee4b5bb6eb832 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,3 @@
----
-license: apache-2.0
----
+# WebNN Test Data
+
+This repository hosts the reusable model weights and other test data for Web Neural Network API [test cases](https://github.com/webmachinelearning/webnn-polyfill/tree/master/test) and [samples](https://github.com/webmachinelearning/webnn-samples).
diff --git a/models/deeplabv3_mnv2_nchw/README.md b/models/deeplabv3_mnv2_nchw/README.md
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@@ -0,0 +1,9 @@
+`.npy` files under this folder are downloaded from ONNX model of DeepLab V3 MoblieNet V2 generated by following commands, which is licensed under the Apache 2.0.
+
+### How to Generate DeepLab V3 MobileNet V2 ONNX model
+
+Following this [guide](../deeplabv3_mnv2_nhwc#how-to-generate-deeplab-v3-mobilenet-v2-tflite-model) to generate the DeepLab V3 MoblieNet V2 TFLite model, then use ['tf2onnx'](https://github.com/onnx/tensorflow-onnx) tool to convert TFLite model to ONNX model:
+
+```
+python3 -m tf2onnx.convert --opset 13 --tflite deeplab_mobilenetv2.tflite --output deeplab_mobilenetv2.onnx
+```
\ No newline at end of file
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diff --git a/models/deeplabv3_mnv2_nhwc/README.md b/models/deeplabv3_mnv2_nhwc/README.md
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+`.npy` files under this folder are downloaded from TFLite model of DeepLab V3 MoblieNet V2 generated by following commands, which is licensed under the Apache 2.0.
+
+### How to Generate DeepLab V3 MobileNet V2 TFLite model
+
+Download and extract the origin [DeepLab V3 MobileNet V2 model](http://download.tensorflow.org/models/deeplabv3_mnv2_pascal_trainval_2018_01_29.tar.gz)
+use following tools and commands to convert the frozen grahp to TFLite model.
+
+Use tensorflow's [`optimize_for_inference`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/optimize_for_inference.py) tool to generate a stripped frozen graph:
+
+```
+python3 -m tensorflow.python.tools.optimize_for_inference \
+--input=./frozen_inference_graph.pb \
+--output=./frozen_inference_graph_stripped.pb \
+--frozen_graph=True \
+--input_names="sub_7" \
+--output_names="ArgMax"
+```
+
+Use ['TensorFlow Lite converter'](https://www.tensorflow.org/lite/convert) tool to convert stripped frozen graph to TFLite model:
+
+```
+tflite_convert \
+--graph_def_file=frozen_inference_graph_stripped.pb \
+--output_file=deeplab_mobilenetv2.tflite \
+--output_format=TFLITE \
+--input_format=TENSORFLOW_GRAPHDEF \
+--input_arrays=sub_7 \
+--output_arrays=ArgMax
+```
\ No newline at end of file
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+`.npy` files under this folder are downloaded from onnx model of Face Landmark Detection (SimpleCNN) generated by following commands, which is licensed under the Apache 2.0.
+
+### How to Generate Face Landmark Detection ONNX Model
+
+Following this [guide](../face_landmark_nhwc/README.md#how-to-generate-face-landmark-tflite-model) to generate the Face Landmark Detection (SimpleCNN) TFLite model, use ['tflite2onnx'](https://github.com/jackwish/tflite2onnx) tool to convert tflite model to onnx model:
+
+```
+tflite2onnx face_landmark.tflite face_landmark.onnx
+```
\ No newline at end of file
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@@ -0,0 +1,15 @@
+`.npy` files under this folder are downloaded from tflite model of Face Landmark Detection (SimpleCNN) generated by following commands, which is licensed under the Apache 2.0.
+
+### How to Generate Face Landmark Detection TFLite Model
+
+Check out [yinguobing/cnn-facial-landmark](https://github.com/yinguobing/cnn-facial-landmark) for more details about this model.
+
+This model is converted from a pre-trained [Simple CNN](https://drive.google.com/file/d/1Nvzu5A9CjP70sDhiRbMzuIwFLnrq2Qpw/view?usp=sharing) model. You can use the following commands to convert your own model.
+
+```
+tflite_convert \
+--output_file=${out_dir}/face_landmark.tflite \
+--graph_def_file=${download_model_dir}/SimpleCNN.pb \
+--input_arrays=input_to_float \
+--output_arrays=logits/BiasAdd
+```
\ No newline at end of file
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diff --git a/models/facenet_nchw/README.md b/models/facenet_nchw/README.md
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+`.npy` files under this folder are downloaded from ONNX model of FaceNet generated by following commands, which is licensed under the Apache 2.0.
+
+### How to Generate FaceNet ONNX Model
+
+Following this [guide](../facenet_nhwc/README.md#how-to-generate-facenet-tflite-model) to generate the FaceNet TFLite model, use ['tflite2onnx'](https://github.com/onnx/tensorflow-onnx) tool to convert TFLite model to ONNX model:
+
+```
+python -m tf2onnx.convert --tflite facenet.tflite --output facenet.onnx --inputs-as-nchw input_1 --inputs input_1
+```
\ No newline at end of file
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diff --git a/models/facenet_nhwc/README.md b/models/facenet_nhwc/README.md
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+`.npy` files under this folder are downloaded from TFLite model of FaceNet generated by following commands, which is licensed under the Apache 2.0.
+
+### How to Generate FaceNet TFLite Model
+
+Following this [guide](https://medium.com/@estebanuri/converting-sandbergs-facenet-pre-trained-model-to-tensorflow-lite-using-an-unorthodox-way-7ee3a6ed02a3) to convert [Sandberg's FaceNet pre-trained model](https://github.com/davidsandberg/facenet) to TensorFlow Lite.
\ No newline at end of file
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+`.npy` files under this folder are trained from project of [xiph/rnnoise](https://github.com/xiph/rnnoise), which is licensed under the BSD 3-Clause "New" or "Revised" License.  
+Refer to [issue79](https://github.com/xiph/rnnoise/issues/79) we can load the C array weights back to Keras and it can be further used to extract a TensorFlow model.
\ No newline at end of file
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+`.npy` files under `weights` folder are downloaded from model of [SqueezeNet 1.0](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/squeezenet_2018_04_27.tgz), `.npy` files under `test_data_set` folder are created by Intel, which are all licensed under the Apache 2.0.
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diff --git a/models/ssd_mobilenetv1_nchw/README.md b/models/ssd_mobilenetv1_nchw/README.md
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+`.npy` files under this folder are downloaded from model of SSD MobileNet V1 generated by following commands, which is licensed under the Apache 2.0.
+
+### How to Generate SSD MobileNet V1 ONNX Model
+
+Following this [guide](../ssd_mobilenetv1_nhwc#how-to-generate-ssd-mobilenet-v1-tflite-model) to generate the SSD MobileNet V1 TFLite model, use ['tflite2onnx'](https://github.com/jackwish/tflite2onnx) tool to convert tflite model to onnx model:
+
+```
+tflite2onnx ssd_mobilenet_v1_coco.tflite ssd_mobilenet_v1_coco.onnx
+```
\ No newline at end of file
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@@ -0,0 +1,27 @@
+`.npy` files under this folder are downloaded from model of SSD MobileNet V1 generated by following commands, which is licensed under the Apache 2.0.
+
+### How to Generate SSD MobileNet V1 TFLite Model
+
+Download and extract the origin [SSD MobileNet V1 model](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz), use following converter command to remove the preprocess and postprocess graphs by using tensorflow's [`optimize_for_inference`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/optimize_for_inference.py) tool:
+
+```
+python3 -m tensorflow.python.tools.optimize_for_inference \
+--input=./frozen_inference_graph.pb \
+--output=./frozen_inference_graph_stripped.pb --frozen_graph=True \
+--input_names=Preprocessor/sub \
+--output_names='concat,concat_1' \
+--alsologtostderr
+```
+
+Use ['TensorFlow Lite converter'](https://www.tensorflow.org/lite/convert) tool to convert frozen graph to tflite model:
+
+```
+tflite_convert \
+--graph_def_file=./frozen_inference_graph_stripped.pb \
+--output_file=./ssd_mobilenet_v1_coco.tflite \
+--input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE \
+--input_shapes=1,300,300,3 --input_arrays=Preprocessor/sub \
+--enable_v1_converter \
+--output_arrays='concat,concat_1' \
+--inference_type=FLOAT --logtostderr
+```
\ No newline at end of file
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diff --git a/models/ssd_mobilenetv2_face_nchw/README.md b/models/ssd_mobilenetv2_face_nchw/README.md
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+`.npy` files under this folder are downloaded from onnx model of SSD MobileNet V2 Face generated by following commands, which is licensed under the Apache 2.0.
+
+### How to Generate SSD MobileNet V2 Face ONNX Model
+
+Following this [guide](../ssd_mobilenetv2_face_nhwc/README.md#how-to-generate-ssd-mobilenet-v2-face-tflite-model) to generate the SSD MobileNet V2 Face TFLite model, use ['tflite2onnx'](https://github.com/jackwish/tflite2onnx) tool to convert tflite model to onnx model:
+
+```
+tflite2onnx ssd_mobilenetv2_face.tflite ssd_mobilenetv2_face.onnx
+```
\ No newline at end of file
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diff --git a/models/ssd_mobilenetv2_face_nhwc/README.md b/models/ssd_mobilenetv2_face_nhwc/README.md
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+`.npy` files under this folder are downloaded from tflite model of SSD MobileNet V2 Face generated by following commands, which is licensed under the Apache 2.0.
+
+### How to Generate SSD MobileNet V2 Face TFLite Model
+
+SSD MobileNet V2 Face Detection model is trained by Tensorflow Object Detection API with WIDER_FACE dataset. Please go [here](https://github.com/Wenzhao-Xiang/face-detection-ssd-mobilenet) for more training details.
+
+After getting the `frozen_inference_graph.pb`, you can use the following commands by using tensorflow's [`optimize_for_inference`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/optimize_for_inference.py) tool to convert it to frozen graph.
+
+
+```
+python3 -m tensorflow.python.tools.optimize_for_inference \
+--input=${download_model_dir}/frozen_inference_graph.pb \
+--output=${out_dir}/frozen_inference_graph_stripped.pb --frozen_graph=True \
+--input_names=Preprocessor/sub \
+--output_names=\
+"BoxPredictor_0/BoxEncodingPredictor/BiasAdd,BoxPredictor_0/ClassPredictor/BiasAdd,\
+BoxPredictor_1/BoxEncodingPredictor/BiasAdd,BoxPredictor_1/ClassPredictor/BiasAdd,\
+BoxPredictor_2/BoxEncodingPredictor/BiasAdd,BoxPredictor_2/ClassPredictor/BiasAdd,\
+BoxPredictor_3/BoxEncodingPredictor/BiasAdd,BoxPredictor_3/ClassPredictor/BiasAdd,\
+BoxPredictor_4/BoxEncodingPredictor/BiasAdd,BoxPredictor_4/ClassPredictor/BiasAdd,\
+BoxPredictor_5/BoxEncodingPredictor/BiasAdd,BoxPredictor_5/ClassPredictor/BiasAdd" \
+--alsologtostderr
+```
+
+Use ['TensorFlow Lite converter'](https://www.tensorflow.org/lite/convert) tool to convert frozen graph to tflite model:
+
+```
+tflite_convert \
+--graph_def_file=${out_dir}/frozen_inference_graph_stripped.pb \
+--output_file=${out_dir}/ssd_mobilenetv2_face.tflite \
+--input_format=TENSORFLOW_GRAPHDEF --output_format=TFLITE \
+--input_shapes=1,300,300,3 --input_arrays=Preprocessor/sub \
+--output_arrays=\
+"BoxPredictor_0/BoxEncodingPredictor/BiasAdd,BoxPredictor_0/ClassPredictor/BiasAdd,\
+BoxPredictor_1/BoxEncodingPredictor/BiasAdd,BoxPredictor_1/ClassPredictor/BiasAdd,\
+BoxPredictor_2/BoxEncodingPredictor/BiasAdd,BoxPredictor_2/ClassPredictor/BiasAdd,\
+BoxPredictor_3/BoxEncodingPredictor/BiasAdd,BoxPredictor_3/ClassPredictor/BiasAdd,\
+BoxPredictor_4/BoxEncodingPredictor/BiasAdd,BoxPredictor_4/ClassPredictor/BiasAdd,\
+BoxPredictor_5/BoxEncodingPredictor/BiasAdd,BoxPredictor_5/ClassPredictor/BiasAdd" \
+--inference_type=FLOAT --logtostderr
+```
\ No newline at end of file
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diff --git a/models/super_resolution_nchw/README.md b/models/super_resolution_nchw/README.md
new file mode 100644
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+`.npy` files under this folder are downloaded from model of [sub_pixel_cnn_2016](https://github.com/onnx/models/blob/main/vision/super_resolution/sub_pixel_cnn_2016/model/super-resolution-10.tar.gz),
+which is licensed under the Apache 2.0.
+
+### How to Generate Super Resolution (sub_pixel_cnn_2016) Model
+Download and extract [Super Resolution (with sample test data)](https://github.com/onnx/models/blob/main/vision/super_resolution/sub_pixel_cnn_2016/model/super-resolution-10.tar.gz), then use https://netron.app/ to open `.ONNX`, Navigate to each node "layer" per W (=Weight) and B (=Biases), click "+", then click save.
+
+Convert to `.npy` formatted files by:
+```sh
+python3 gen_nhwc_test_data.py -d path\to\super_resolution -n super_resolution
+```
\ No newline at end of file
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\ No newline at end of file
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diff --git a/models/tiny_yolov2_nhwc/README.md b/models/tiny_yolov2_nhwc/README.md
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+`.npy` files under this folder are downloaded from model of Tiny YOLO v2, which is converted from [YAD2K project](https://github.com/allanzelener/YAD2K) and  licensed under the MIT.
+
+The test data are generated by running TinyYolov2 model based on [YAD2K project](https://github.com/BruceDai/YAD2K/tree/gen_test_data) with [yolov2-tiny-voc.cfg](https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2-tiny-voc.cfg) and [yolov2-tiny-voc.weights](http://pjreddie.com/media/files/yolov2-tiny-voc.weights).
+
+Details of test data: 
+
+1. `input_0.npy` and `output_0.npy` of [test_data_set/0](test_data_set/0) is generated by running TinyYolov2 model with [dog.jpg](https://github.com/allanzelener/YAD2K/blob/master/images/dog.jpg) 
+2. `input_0.npy` and `output_0.npy` of [test_data_set/1](test_data_set/1) is generated by running TinyYolov2 model with [horses.jpg](https://github.com/allanzelener/YAD2K/blob/master/images/horses.jpg) 
+3. `input_0.npy` and `output_0.npy` of [test_data_set/2](test_data_set/2) is generated by running TinyYolov2 model with [person.jpg](https://github.com/allanzelener/YAD2K/blob/master/images/person.jpg) 
+
+Commands to generate test data:
+
+```sh
+$ git clone https://github.com/BruceDai/YAD2K && cd YAD2K
+$ git checkout gen_test_data
+$ python3 test_yolo.py model_data/yolov2-tiny-voc.h5 -a model_data/yolov2-tiny-voc_anchors.txt -c model_data/pascal_classes.txt
+```
\ No newline at end of file
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