Update READMEs with accuracy numbers of 4b quant proxylessnas w/ Hada…

…mard classifier
Xilinx · Nov 1, 2019 · 9eba457 · 9eba457
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diff --git a/README.md b/README.md
@@ -124,6 +124,7 @@ Below a list of relevant pretrained models available, currently for evaluation o
 | Name         | Scaling Type               | First layer weights | Weights | Activations | Avg pool | Top1  | Top5  | Pretrained model                                                                                | Retrained from                                                |
 |--------------|----------------------------|---------------------|---------|-------------|----------|-------|-------|-------------------------------------------------------------------------------------------------|---------------------------------------------------------------|
 | MobileNet V1 | Floating-point per channel | 8 bit               | 4 bit   | 4 bit       | 4 bit    | 71.14 | 90.10 | [Download](https://github.com/Xilinx/brevitas/releases/download/quant_mobilenetv1_4b-r1/quant_mobilenet_v1_4b-0100a667.pth) | [link](https://github.com/osmr/imgclsmob/tree/master/pytorch) |
+| ProxylessNAS Mobile14 w/ Hadamard classifier | Floating-point per channel | 8 bit               | 4 bit  | 4 bit       | 4 bit    | 73.52 | 91.46 | [Download](https://github.com/Xilinx/brevitas/releases/download/quant_proxylessnas_mobile14_hadamard_4b-r0/quant_proxylessnas_mobile14_hadamard_4b-4acbfa9f.pth) | [link](https://github.com/osmr/imgclsmob/tree/master/pytorch) |
 | ProxylessNAS Mobile14 | Floating-point per channel | 8 bit               | 4 bit, 5 bit  | 4 bit, 5 bit       | 4 bit    | 75.01 | 92.33 | [Download](https://github.com/Xilinx/brevitas/releases/download/quant_proxylessnas_mobile14_4b5b-r0/quant_proxylessnas_mobile14_4b5b-2bdf7f8d.pth) | [link](https://github.com/osmr/imgclsmob/tree/master/pytorch) |
 
 

diff --git a/examples/README.md b/examples/README.md
@@ -8,6 +8,7 @@ Below in the table is a list of example pretrained models made available for ref
 | Name         | Cfg                   | Scaling Type               | First layer weights | Weights | Activations | Avg pool | Top1  | Top5  | Pretrained model                                                                                | Retrained from                                                |
 |--------------|-----------------------|----------------------------|---------------------|---------|-------------|----------|-------|-------|-------------------------------------------------------------------------------------------------|---------------------------------------------------------------|
 | MobileNet V1 | quant_mobilenet_v1_4b | Floating-point per channel | 8 bit               | 4 bit   | 4 bit       | 4 bit    | 71.14 | 90.10 | [Download](https://github.com/Xilinx/brevitas/releases/download/quant_mobilenetv1_4b-r1/quant_mobilenet_v1_4b-0100a667.pth) | [link](https://github.com/osmr/imgclsmob/tree/master/pytorch) |
+| ProxylessNAS Mobile14 w/ Hadamard classifier | quant_proxylessnas_mobile14_hadamard_4b  | Floating-point per channel | 8 bit               | 4 bit  | 4 bit       | 4 bit    | 73.52 | 91.46 | [Download](https://github.com/Xilinx/brevitas/releases/download/quant_proxylessnas_mobile14_hadamard_4b-r0/quant_proxylessnas_mobile14_hadamard_4b-4acbfa9f.pth) | [link](https://github.com/osmr/imgclsmob/tree/master/pytorch) |
 | ProxylessNAS Mobile14 | quant_proxylessnas_mobile14_4b5b | Floating-point per channel | 8 bit               | 4 bit, 5 bit  | 4 bit, 5 bit       | 4 bit    | 75.01 | 92.33 | [Download](https://github.com/Xilinx/brevitas/releases/download/quant_proxylessnas_mobile14_4b5b-r0/quant_proxylessnas_mobile14_4b5b-2bdf7f8d.pth) | [link](https://github.com/osmr/imgclsmob/tree/master/pytorch) |