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benchmark:
DIV2K:
PSNR: 35.00dB
description: "SESR, super-efficient super resolution\
\ is a network aims to generate a high-resolution image from a low-resolution input.\n\
Name was changed by ARM developers when they wrote research paper on their technique.\n\
The attached int8 fully quantized tflite model achieves 35.00dB PSNR on DIV2K dataset.\
\ \nThe model takes 1080p input (in YCbCr, i.e., takes a 1x1920x1080x1 tensor as\
\ input) and outputs 4K images (in YCbCr, i.e., 1x3840x2160x1 output).\n Compatability\
\ note:\n Please note that SESR is a high-end network operating on 1080p->4K images\
\ and runtime memory use of this network requires an end system with at least 100MB\
\ of memory available to ensure successful execution.\n We anticipate the network\
\ being used in premium devices as part of a camera imaging pipeline providing highest\
\ quality digital zoom.\n\
\ Repository for model authors: https://github.com/ARM-software/sesr"
license:
- Apache-2.0
network:
file_size_bytes: 23680
filename: SESR_1080p_to_4K_withD2S_full_int8.tflite
framework: TensorFlow Lite
hash:
algorithm: sha1
value: 5abc5f05202aa1b0b9c34c5a978b6aa0a02f7ec5
provenance: https://git.research.arm.com/golden-models/super-resolution-model-toolkit/-/tree/shuokai_tf2
quality: Optimised
network_parameters:
input_nodes:
- description: 'Low-resolution input: 1080p (in YCbCr, i.e., take a 1x1920x1080x1
tensor as input) '
example_input:
path: models/superresolution/SESR/tflite_int8/testing_input/net_input
name: net_input
shape:
- 1
- 1920
- 1080
- 1
output_nodes:
- description: 'High-resolution input: 4K images (in YCbCr, i.e., 1x3840x2160x1
output).'
name: net_output
shape:
- 1
- 3840
- 2160
- 1
test_output_path: models/superresolution/SESR/tflite_int8/testing_output/net_output
operators:
TensorFlow Lite:
- ADD
- BATCH_TO_SPACE_ND
- CONV_2D
- RELU
- TRANSPOSE
paper: https://arxiv.org/abs/2103.09404