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#!/bin/bash
DATASET_ROOT=/dataset/imageNet100_sicy/train/ #/raid/common/imagenet-raw/
## train ViT-large for 250 epochs
OUTPUT_ROOT=./exps/vit_large_250ep
NPROC_PER_NODE=40 # GPU numbers
BATCH_SIZE_PER_GPU=16
DEBUG=false # debug = true, then we only load subset of the whole training dataset
python -m torch.distributed.launch --nproc_per_node=$NPROC_PER_NODE main.py \
--data_path $DATASET_ROOT \
--output_dir $OUTPUT_ROOT \
--arch vit_large \
--instance_queue_size 65536 \
--local_group_queue_size 65536 \
--use_bn_in_head false \
--instance_out_dim 256 \
--instance_temp 0.2 \
--local_group_out_dim 256 \
--local_group_temp 0.2 \
--local_group_knn_top_n 8 \
--group_out_dim 65536 \
--group_student_temp 0.1 \
--group_warmup_teacher_temp 0.04 \
--group_teacher_temp 0.07 \
--group_warmup_teacher_temp_epochs 50 \
--norm_last_layer true \
--norm_before_pred true \
--batch_size_per_gpu $BATCH_SIZE_PER_GPU \
--epochs 250 \
--warmup_epochs 10 \
--clip_grad 3.0 \
--lr 0.0015 \
--min_lr 1.5e-4 \
--patch_embed_lr_mult 0.2 \
--drop_path_rate 0.3 \
--weight_decay 0.025 \
--weight_decay_end 0.12 \
--freeze_last_layer 3 \
--momentum_teacher 0.996 \
--use_fp16 false \
--local_crops_number 10 \
--size_crops 96 \
--global_crops_scale 0.25 1 \
--local_crops_scale 0.05 0.25 \
--timm_auto_augment_par rand-m9-mstd0.5-inc1 \
--prob 0.5 \
--use_prefetcher true \
--debug $DEBUG |