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MobileViTv3 first commit
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‎.gitignore‎

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*.pyc
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__pycache__
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.DS_STORE
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.idea
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results*
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*.png
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*.jpg
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.idea
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*.pt
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*.pth
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results*
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vision_datasets/
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exp_results/
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exp_results*
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results_*
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coco_eval_results/
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*.so
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model_zoo
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model_zoo/*
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cvnets.egg-info
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cvnets.egg-info/*

‎LICENSE‎

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MICRON TECHNOLOGY, INC. SOFTWARE LICENSE AGREEMENT
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PLEASE READ THIS LICENSE AGREEMENT ("AGREEMENT") FROM MICRON TECHNOLOGY, INC.
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("MTI") CAREFULLY: BY INSTALLING, USING, OR MODIFYING THE MTI SOFTWARE AND ANY
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RELATED PRINTED MATERIALS ("SOFTWARE"), YOU ARE ACCEPTING AND AGREEING TO THE
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TERMS OF THIS AGREEMENT. IF YOU DO NOT AGREE WITH THE TERMS OF THIS AGREEMENT, DO
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NOT INSTALL, USE, OR MODIFY THE SOFTWARE.
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LICENSE:
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In consideration of your agreement to abide by the terms of this Agreement,
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and subject to these terms, MTI hereby grants to you a personal, non-exclusive
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license, under MTI’s copyrights in the Software, to install, use, and copy for
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personal use the Software solely for non-commercial uses and purposes subject to
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the terms of this Agreement. You must maintain all copyright notices on all
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copies of the Software. You agree not to use the Software for any commercial
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purpose or for any public display (commercial or noncommercial). MTI may make
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changes to the Software at any time without notice to you. In addition, MTI is
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under no obligation whatsoever to update, maintain, or provide new versions or
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other support for the Software. This license shall automatically terminate if you
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violate any of the terms of this license and may be terminated by MTI at any
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must destroy any Software in your possession whether in electronic or printed
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format. In any unmodified version of this Software, you must retain this notice
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and the following text and disclaimers. Neither the name, trademarks, service
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OWNERSHIP OF MATERIALS:
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You acknowledge and agree that the Software and any derivative works thereof
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does not grant any express or implied rights hereunder to any patents, copyrights,
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or trademarks for any commercial uses or purposes. You further acknowledge and
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agree that all right, title, and interest in and to the Software, including
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affiliated companies). This Agreement does not convey to you an interest in or to
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the Software, but only a limited right to install and use the Software in
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accordance with the terms of this Agreement. The Software is licensed to you and
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not sold.
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DISCLAIMER OF WARRANTY:
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THE SOFTWARE IS PROVIDED "AS IS" WITHOUT WARRANTY OF ANY KIND. MTI, ON BEHALF OF
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ITSELF AND ITS AFFILIATED COMPANIES, EXPRESSLY DISCLAIMS ALL WARRANTIES EXPRESS
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FURTHERMORE, MTI DOES NOT MAKE ANY REPRESENTATIONS REGARDING THE USE OR THE
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THE SOFTWARE REMAINS WITH YOU. IN NO EVENT SHALL MTI OR ITS AFFILIATED COMPANIES
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Because some jurisdictions prohibit the exclusion or limitation of liability for
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This Agreement constitutes the entire agreement between MTI and you regarding the
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installation of the Software, you agree to the terms of this Agreement. You must
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agree to the terms in order to use the Software.

‎MobileViTv3-v1/config/classification/mobilevitv3_small_multiserver.yaml‎

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common:
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run_label: "run_1"
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log_freq: 500
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auto_resume: true
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mixed_precision: true
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dataset:
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root_train: "Datasets/ILSVRC2012-raw/train"
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root_val: "Datasets/ILSVRC2012-raw/val"
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name: "imagenet"
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category: "classification"
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train_batch_size0: 32
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val_batch_size0: 32
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eval_batch_size0: 1
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workers: 6
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persistent_workers: false
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pin_memory: true
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image_augmentation:
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random_resized_crop:
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enable: true
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interpolation: "bilinear"
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random_horizontal_flip:
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enable: true
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sampler:
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name: "variable_batch_sampler"
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vbs:
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crop_size_width: 256
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crop_size_height: 256
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max_n_scales: 5
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min_crop_size_width: 160
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max_crop_size_width: 320
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min_crop_size_height: 160
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max_crop_size_height: 320
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check_scale: 32
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loss:
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category: "classification"
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classification:
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name: "label_smoothing"
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label_smoothing_factor: 0.1
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optim:
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name: "adamw"
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weight_decay: 0.01
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no_decay_bn_filter_bias: false
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adamw:
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beta1: 0.9
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beta2: 0.999
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scheduler:
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name: "cosine"
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is_iteration_based: false
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max_epochs: 300
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warmup_iterations: 3000
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warmup_init_lr: 0.0002
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cosine:
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max_lr: 0.002
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min_lr: 0.0002
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model:
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classification:
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name: "mobilevit_v3"
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classifier_dropout: 0.1
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mit:
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mode: "small_v3" #"small_v3_fast"
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ffn_dropout: 0.0
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attn_dropout: 0.0
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dropout: 0.1
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number_heads: 4
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no_fuse_local_global_features: false
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conv_kernel_size: 3
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activation:
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name: "swish"
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normalization:
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name: "batch_norm_2d"
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momentum: 0.1
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activation:
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name: "swish"
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layer:
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global_pool: "mean"
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conv_init: "kaiming_normal"
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linear_init: "trunc_normal"
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linear_init_std_dev: 0.02
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ema:
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enable: true
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momentum: 0.0005
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ddp:
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enable: true
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dist_url: "tcp://XX.XXX.XXX.XXX:XXXX" # ip address of server with rank 0
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rank: 0 # rank value unique for each server
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world_size: 6
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dist_port: 30786
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stats:
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name: [ "loss", "top1", "top5" ]
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checkpoint_metric: "top1"
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checkpoint_metric_max: true

‎MobileViTv3-v1/config/classification/mobilevitv3_small_oneserver.yaml‎

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common:
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run_label: "run_1"
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log_freq: 500
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auto_resume: true
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mixed_precision: true
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dataset:
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root_train: "Datasets/ILSVRC2012-raw/train"
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root_val: "Datasets/ILSVRC2012-raw/val"
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name: "imagenet"
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category: "classification"
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train_batch_size0: 32
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val_batch_size0: 32
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eval_batch_size0: 1
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workers: 6
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persistent_workers: false
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pin_memory: true
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image_augmentation:
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random_resized_crop:
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enable: true
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interpolation: "bilinear"
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random_horizontal_flip:
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enable: true
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sampler:
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name: "variable_batch_sampler"
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vbs:
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crop_size_width: 256
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crop_size_height: 256
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max_n_scales: 5
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min_crop_size_width: 160
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max_crop_size_width: 320
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min_crop_size_height: 160
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max_crop_size_height: 320
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check_scale: 32
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loss:
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category: "classification"
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classification:
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name: "label_smoothing"
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label_smoothing_factor: 0.1
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optim:
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name: "adamw"
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weight_decay: 0.01
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no_decay_bn_filter_bias: false
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adamw:
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beta1: 0.9
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beta2: 0.999
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scheduler:
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name: "cosine"
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is_iteration_based: false
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max_epochs: 300
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warmup_iterations: 3000
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warmup_init_lr: 0.0002
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cosine:
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max_lr: 0.002
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min_lr: 0.0002
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model:
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classification:
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name: "mobilevit_v3"
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classifier_dropout: 0.1
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mit:
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mode: "small_v3"
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ffn_dropout: 0.0
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attn_dropout: 0.0
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dropout: 0.1
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number_heads: 4
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no_fuse_local_global_features: false
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conv_kernel_size: 3
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activation:
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name: "swish"
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normalization:
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name: "batch_norm_2d"
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momentum: 0.1
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activation:
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name: "swish"
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layer:
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global_pool: "mean"
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conv_init: "kaiming_normal"
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linear_init: "trunc_normal"
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linear_init_std_dev: 0.02
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ema:
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enable: true
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momentum: 0.0005
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ddp:
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enable: true
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rank: 0
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world_size: -1
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dist_port: 30786
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stats:
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name: [ "loss", "top1", "top5" ]
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checkpoint_metric: "top1"
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checkpoint_metric_max: true

‎MobileViTv3-v1/config/classification/mobilevitv3_x_small_multiserver.yaml‎

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common:
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run_label: "run_1"
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log_freq: 500
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auto_resume: true
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mixed_precision: true
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dataset:
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root_train: "/media/Datasets/ILSVRC2012-raw/train"
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root_val: "/media/Datasets/ILSVRC2012-raw/val"
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name: "imagenet"
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category: "classification"
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train_batch_size0: 32
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val_batch_size0: 32
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eval_batch_size0: 1
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workers: 6
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persistent_workers: false
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pin_memory: true
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image_augmentation:
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random_resized_crop:
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enable: true
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interpolation: "bilinear"
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random_horizontal_flip:
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enable: true
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sampler:
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name: "variable_batch_sampler"
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vbs:
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crop_size_width: 256
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crop_size_height: 256
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max_n_scales: 5
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min_crop_size_width: 160
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max_crop_size_width: 320
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min_crop_size_height: 160
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max_crop_size_height: 320
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check_scale: 32
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loss:
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category: "classification"
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classification:
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name: "label_smoothing"
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label_smoothing_factor: 0.1
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optim:
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name: "adamw"
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weight_decay: 1.e-2
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no_decay_bn_filter_bias: false
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adamw:
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beta1: 0.9
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beta2: 0.999
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scheduler:
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name: "cosine"
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is_iteration_based: false
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max_epochs: 300
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warmup_iterations: 3000
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warmup_init_lr: 0.0002
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cosine:
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max_lr: 0.002
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min_lr: 0.0002
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model:
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classification:
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name: "mobilevit_v3"
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classifier_dropout: 0.1
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mit:
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mode: "x_small_v3"
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ffn_dropout: 0.0
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attn_dropout: 0.0
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dropout: 0.1
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number_heads: 4
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no_fuse_local_global_features: false
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conv_kernel_size: 3
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activation:
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name: "swish"
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normalization:
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name: "batch_norm_2d"
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momentum: 0.1
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activation:
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name: "swish"
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layer:
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global_pool: "mean"
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conv_init: "kaiming_normal"
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linear_init: "trunc_normal"
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linear_init_std_dev: 0.02
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ema:
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enable: true
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momentum: 0.0005
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ddp:
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enable: true
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dist_url: "tcp://XX.XXX.XXX.XXX:XXXX" # ip address of server with rank 0
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rank: 0 # rank value unique for each server
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world_size: 6
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dist_port: 30786
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stats:
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name: [ "loss", "top1", "top5" ]
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checkpoint_metric: "top1"
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checkpoint_metric_max: true

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