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src/brevitas_examples/super_resolution/README.md

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 # Integer-Quantized Super Resolution Experiments with Brevitas
 
-This directory contains training scripts to demonstrate how to train integer-quantized super resolution models using [Brevitas](https://github.com/Xilinx/brevitas).
-Code is also provided to demonstrate accumulator-aware quantization (A2Q) as proposed in our paper "[Quantized Neural Networks for Low-Precision Accumulation with Guaranteed Overflow Avoidance](https://arxiv.org/abs/2301.13376)".
+This directory contains scripts demonstrating how to train integer-quantized super resolution models using [Brevitas](https://github.com/Xilinx/brevitas).
+Code is also provided to demonstrate accumulator-aware quantization (A2Q) as proposed in our ICCV 2023 paper "[A2Q: Accumulator-Aware Quantization with Guaranteed Overflow Avoidance](https://arxiv.org/abs/2308.13504)".
 
 ## Experiments
 
 All models are trained on the BSD300 dataset to upsample images by 2x.
-Target images are center cropped to 512x512.
+Target images are cropped to 512x512.
+During training random cropping is applied, along with random vertical and horizontal flips.
+During inference center cropping is applied.
 Inputs are then downscaled by 2x and then used to train the model directly in the RGB space.
 Note that this is a difference from many academic works that train only on the Y-channel in YCbCr format.
 
 | Model Name                  | Upscale Factor | Weight quantization | Activation quantization | Peak Signal-to-Noise Ratio |
 |-----------------------------|----------------|---------------------|-------------------------|----------------------------|
-| [float_espcn_x2](https://github.com/Xilinx/brevitas/releases/download/super_res_r0/float_espcn_x2-2f3821e3.pth) | x2             | float32             | float32                 | 30.37                      |
-| [quant_espcn_x2_w8a8_base](https://github.com/Xilinx/brevitas/releases/download/super_res_r0/quant_espcn_x2_w8a8_base-7d54e29c.pth)    | x2             | int8                | (u)int8                 | 30.16                      |
-| [quant_espcn_x2_w8a8_a2q_32b](https://github.com/Xilinx/brevitas/releases/download/super_res_r0/quant_espcn_x2_w8a8_a2q_32b-0b1f361d.pth) | x2             | int8                | (u)int8                 | 30.80                      |
-| [quant_espcn_x2_w8a8_a2q_16b](https://github.com/Xilinx/brevitas/releases/download/super_res_r0/quant_espcn_x2_w8a8_a2q_16b-3c4acd35.pth) | x2             | int8                | (u)int8                 | 29.38                      |
 | bicubic_interp              | x2             | N/A                 | N/A                     | 28.71                      |
+| [float_espcn_x2]()          | x2             | float32             | float32                 | 31.03                      |
+||
+| [quant_espcn_x2_w8a8_base]()    | x2             | int8                | (u)int8                 | 30.96                  |
+| [quant_espcn_x2_w8a8_a2q_32b]() | x2             | int8                | (u)int8                 | 30.79                  |
+| [quant_espcn_x2_w8a8_a2q_16b]() | x2             | int8                | (u)int8                 | 30.56                  |
+||
+| [quant_espcn_x2_w4a4_base]()    | x2             | int4                | (u)int4                 | 30.30                  |
+| [quant_espcn_x2_w4a4_a2q_32b]() | x2             | int4                | (u)int4                 | 30.27                  |
+| [quant_espcn_x2_w4a4_a2q_14b]() | x2             | int4                | (u)int4                 | 30.24                  |
 
 
 ## Train