WebNN use cases, models and ops

Use cases and networks

Use case	Network
Image classification	MobileNet, SqueezeNet, ResNet, Inception
Object/Person detection	TinyYOLO, SSD
Semantic Segmentation	DeepLab
Skeleton Detection	PoseNet

Network/Models and ops

ONNX Op	MobileNetV2 (ONNX)	MobileNetV2 (TFLite)	SqueezeNet1.1 (ONNX) [4]	SqueezeNet (TFLite)	ResNet50V2 (ONNX)	InceptionV4 (TFLite)	TinyYOLOV2 (ONNX) [6]	SSD MobileNetV1 (TFLite) [7]	PoseNet (TFLite)	DeepLabV3 (TFLite)
Add	✔️	✔️			✔️					✔️
AveragePool	✔️ [1]	✔️	✔️	✔️	✔️	✔️				✔️
BatchNormalization	✔️				✔️		✔️
Clip		✔️ [2]						✔️ [2]	✔️ [2]	✔️ [2]
Concat			✔️	✔️	✔️	✔️		✔️	✔️	✔️
Conv	✔️	✔️ [3]	✔️	✔️	✔️	✔️	✔️	✔️ [3]	✔️ [3]	✔️ [3]
Gemm					✔️	✔️ [5]
LeakyRelu							✔️
MaxPool			✔️	✔️	✔️	✔️	✔️
Relu	✔️		✔️	✔️	✔️	✔️
Reshape	✔️	✔️	✔️	✔️	✔️			✔️
Resize										✔️ [8]
Sigmoid								✔️
Softmax	✔️	✔️	✔️	✔️	✔️	✔️

Notes:

1. GlobalAveragePool op of ONNX
2. RELU6 op of TFLite
3. CONV_2D and DEPTHWISE_CONV_2D op of TFLite
4. Ignore Dropout op of ONNX
5. FULLY_CONNECTED op of TFLite
6. Ignore ImageScalar op of ONNX
7. Ignore TFLite_Detection_PostProcess op of TFLite
8. RESIZE_BILINEAR op of TFLite

Covered TFLite ops

TFLite Op	ONNX Op
ADD	Add
AVERAGE_POOL_2D	AveragePool
CONCATENATION	Concat
CONV_2D	Conv
DEPTHWISE_CONV_2D	Conv
FULLY_CONNECTED	Gemm
LEAKY_RELU	LeakyRelu
LOGISTIC	Sigmoid
MAX_POOL_2D	MaxPool
RELU	Relu
RELU6	Clip
RESHAPE	Reshape
RESIZE_BILINEAR	Resize
SOFTMAX	Softmax