Added models

.gitattributes

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+models/*.pth filter=lfs diff=lfs merge=lfs -text

models/swin_base_b32x4-fp16_fungi+val_res_384_cb_epochs_6.py

@@ -0,0 +1,283 @@
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='SwinTransformer',
+        arch='base',
+        img_size=384,
+        stage_cfgs=dict(block_cfgs=dict(window_size=12)),
+        drop_path_rate=0.5,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint=
+            'https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-base_3rdparty_in21k-384px.pth',
+            prefix='backbone')),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1604,
+        in_channels=1024,
+        init_cfg=None,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.0),
+        dict(type='Constant', layer='LayerNorm', val=1.0, bias=0.0)
+    ],
+    train_cfg=dict())
+rand_increasing_policies = [
+    dict(type='AutoContrast'),
+    dict(type='Equalize'),
+    dict(type='Invert'),
+    dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),
+    dict(type='Posterize', magnitude_key='bits', magnitude_range=(4, 0)),
+    dict(type='Solarize', magnitude_key='thr', magnitude_range=(256, 0)),
+    dict(
+        type='SolarizeAdd',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 110)),
+    dict(
+        type='ColorTransform',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.9)),
+    dict(type='Contrast', magnitude_key='magnitude', magnitude_range=(0, 0.9)),
+    dict(
+        type='Brightness', magnitude_key='magnitude',
+        magnitude_range=(0, 0.9)),
+    dict(
+        type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0.9)),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        direction='horizontal'),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        direction='vertical'),
+    dict(
+        type='Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.45),
+        direction='horizontal'),
+    dict(
+        type='Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.45),
+        direction='vertical')
+]
+dataset_type = 'Fungi'
+data_preprocessor = dict(
+    num_classes=1604,
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    to_rgb=True)
+bgr_mean = [103.53, 116.28, 123.675]
+bgr_std = [57.375, 57.12, 58.395]
+train_pipeline = [
+    dict(type='LoadImageFromFileFungi'),
+    dict(
+        type='RandomResizedCrop',
+        scale=384,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies='timm_increasing',
+        num_policies=2,
+        total_level=10,
+        magnitude_level=9,
+        magnitude_std=0.5,
+        hparams=dict(pad_val=[104, 116, 124], interpolation='bicubic')),
+    dict(
+        type='RandomErasing',
+        erase_prob=0.25,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=0.3333333333333333,
+        fill_color=[103.53, 116.28, 123.675],
+        fill_std=[57.375, 57.12, 58.395]),
+    dict(type='PackInputs')
+]
+test_pipeline = [
+    dict(type='LoadImageFromFileFungi'),
+    dict(
+        type='ResizeEdge',
+        scale=438,
+        edge='short',
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='CenterCrop', crop_size=384),
+    dict(type='PackInputs')
+]
+train_dataloader = dict(
+    pin_memory=True,
+    persistent_workers=True,
+    collate_fn=dict(type='default_collate'),
+    batch_size=32,
+    num_workers=14,
+    dataset=dict(
+        type='ClassBalancedDataset',
+        oversample_thr=0.01,
+        dataset=dict(
+            type='Fungi',
+            data_root='/scratch/slurm_tmpdir/job_22252118/',
+            ann_file='FungiCLEF2023_train_metadata_PRODUCTION.csv',
+            data_prefix='DF20/',
+            pipeline=[
+                dict(type='LoadImageFromFileFungi'),
+                dict(
+                    type='RandomResizedCrop',
+                    scale=384,
+                    backend='pillow',
+                    interpolation='bicubic'),
+                dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+                dict(
+                    type='RandAugment',
+                    policies='timm_increasing',
+                    num_policies=2,
+                    total_level=10,
+                    magnitude_level=9,
+                    magnitude_std=0.5,
+                    hparams=dict(
+                        pad_val=[104, 116, 124], interpolation='bicubic')),
+                dict(
+                    type='RandomErasing',
+                    erase_prob=0.25,
+                    mode='rand',
+                    min_area_ratio=0.02,
+                    max_area_ratio=0.3333333333333333,
+                    fill_color=[103.53, 116.28, 123.675],
+                    fill_std=[57.375, 57.12, 58.395]),
+                dict(type='PackInputs')
+            ])),
+    sampler=dict(type='DefaultSampler', shuffle=True))
+val_dataloader = dict(
+    pin_memory=True,
+    persistent_workers=True,
+    collate_fn=dict(type='default_collate'),
+    batch_size=64,
+    num_workers=12,
+    dataset=dict(
+        type='Fungi',
+        data_root='/scratch/slurm_tmpdir/job_22252118/',
+        ann_file='FungiCLEF2023_val_metadata_PRODUCTION.csv',
+        data_prefix='DF21/',
+        pipeline=[
+            dict(type='LoadImageFromFileFungi'),
+            dict(
+                type='RandomResizedCrop',
+                scale=384,
+                backend='pillow',
+                interpolation='bicubic'),
+            dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+            dict(
+                type='RandAugment',
+                policies='timm_increasing',
+                num_policies=2,
+                total_level=10,
+                magnitude_level=9,
+                magnitude_std=0.5,
+                hparams=dict(pad_val=[104, 116, 124],
+                             interpolation='bicubic')),
+            dict(
+                type='RandomErasing',
+                erase_prob=0.25,
+                mode='rand',
+                min_area_ratio=0.02,
+                max_area_ratio=0.3333333333333333,
+                fill_color=[103.53, 116.28, 123.675],
+                fill_std=[57.375, 57.12, 58.395]),
+            dict(type='PackInputs')
+        ]),
+    sampler=dict(type='DefaultSampler', shuffle=False))
+val_evaluator = dict(
+    type='SingleLabelMetric', items=['precision', 'recall', 'f1-score'])
+test_dataloader = dict(
+    pin_memory=True,
+    persistent_workers=True,
+    collate_fn=dict(type='default_collate'),
+    batch_size=64,
+    num_workers=12,
+    dataset=dict(
+        type='FungiTest',
+        data_root='data/fungi2023/',
+        ann_file='FungiCLEF2023_public_test_metadata_PRODUCTION.csv',
+        data_prefix='DF21/',
+        pipeline=[
+            dict(type='LoadImageFromFileFungi'),
+            dict(
+                type='ResizeEdge',
+                scale=438,
+                edge='short',
+                backend='pillow',
+                interpolation='bicubic'),
+            dict(type='CenterCrop', crop_size=384),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='PackInputs'),
+        ]),
+    sampler=dict(type='DefaultSampler', shuffle=False))
+test_evaluator = dict(
+    type='SingleLabelMetric', items=['precision', 'recall', 'f1-score'])
+optim_wrapper = dict(
+    optimizer=dict(
+        type='AdamW',
+        lr=6.25e-05,
+        weight_decay=0.05,
+        eps=1e-08,
+        betas=(0.9, 0.999)),
+    paramwise_cfg=dict(
+        norm_decay_mult=0.0,
+        bias_decay_mult=0.0,
+        flat_decay_mult=0.0,
+        custom_keys=dict({
+            '.absolute_pos_embed': dict(decay_mult=0.0),
+            '.relative_position_bias_table': dict(decay_mult=0.0)
+        })),
+    clip_grad=dict(max_norm=5),
+    type='AmpOptimWrapper')
+param_scheduler = [
+    dict(type='LinearLR', start_factor=0.01, by_epoch=False, end=4200),
+    dict(type='CosineAnnealingLR', eta_min=0, by_epoch=False, begin=4200)
+]
+train_cfg = dict(by_epoch=True, max_epochs=6, val_interval=1)
+val_cfg = dict()
+test_cfg = dict()
+auto_scale_lr = dict(base_batch_size=64, enable=True)
+default_scope = 'mmpretrain'
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=100),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', interval=1),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='VisualizationHook', enable=False))
+env_cfg = dict(
+    cudnn_benchmark=False,
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    dist_cfg=dict(backend='nccl'))
+vis_backends = [
+    dict(type='LocalVisBackend'),
+    dict(type='TensorboardVisBackend')
+]
+visualizer = dict(
+    type='UniversalVisualizer',
+    vis_backends=[
+        dict(type='LocalVisBackend'),
+        dict(type='TensorboardVisBackend')
+    ])
+log_level = 'INFO'
+load_from = None
+resume = False
+randomness = dict(seed=None, deterministic=False)
+checkpoint = 'https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-base_3rdparty_in21k-384px.pth'
+custom_imports = dict(
+    imports=['mmpretrain_custom'], allow_failed_imports=False)
+launcher = 'pytorch'
+work_dir = './work_dirs/swin_base_b32x4-fp16_fungi+val_res_384_cb_epochs_6'

models/swin_base_b32x4-fp16_fungi+val_res_384_cb_epochs_6_20230524-5197a7e6.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5197a7e62e88740e7d950203e52a08996bcc3f6a648367c55ee9631e12220844
+size 358213519

models/swin_large_b12x6-fp16_fungi+val_res_384_cb_epochs_6.py

@@ -0,0 +1,283 @@
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='SwinTransformer',
+        arch='large',
+        img_size=384,
+        stage_cfgs=dict(block_cfgs=dict(window_size=12)),
+        drop_path_rate=0.5,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint=
+            'https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-base_3rdparty_in21k-384px.pth',
+            prefix='backbone')),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1604,
+        in_channels=1536,
+        init_cfg=None,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.0),
+        dict(type='Constant', layer='LayerNorm', val=1.0, bias=0.0)
+    ],
+    train_cfg=dict())
+rand_increasing_policies = [
+    dict(type='AutoContrast'),
+    dict(type='Equalize'),
+    dict(type='Invert'),
+    dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),
+    dict(type='Posterize', magnitude_key='bits', magnitude_range=(4, 0)),
+    dict(type='Solarize', magnitude_key='thr', magnitude_range=(256, 0)),
+    dict(
+        type='SolarizeAdd',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 110)),
+    dict(
+        type='ColorTransform',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.9)),
+    dict(type='Contrast', magnitude_key='magnitude', magnitude_range=(0, 0.9)),
+    dict(
+        type='Brightness', magnitude_key='magnitude',
+        magnitude_range=(0, 0.9)),
+    dict(
+        type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0.9)),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        direction='horizontal'),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        direction='vertical'),
+    dict(
+        type='Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.45),
+        direction='horizontal'),
+    dict(
+        type='Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.45),
+        direction='vertical')
+]
+dataset_type = 'Fungi'
+data_preprocessor = dict(
+    num_classes=1604,
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    to_rgb=True)
+bgr_mean = [103.53, 116.28, 123.675]
+bgr_std = [57.375, 57.12, 58.395]
+train_pipeline = [
+    dict(type='LoadImageFromFileFungi'),
+    dict(
+        type='RandomResizedCrop',
+        scale=384,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies='timm_increasing',
+        num_policies=2,
+        total_level=10,
+        magnitude_level=9,
+        magnitude_std=0.5,
+        hparams=dict(pad_val=[104, 116, 124], interpolation='bicubic')),
+    dict(
+        type='RandomErasing',
+        erase_prob=0.25,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=0.3333333333333333,
+        fill_color=[103.53, 116.28, 123.675],
+        fill_std=[57.375, 57.12, 58.395]),
+    dict(type='PackInputs')
+]
+test_pipeline = [
+    dict(type='LoadImageFromFileFungi'),
+    dict(
+        type='ResizeEdge',
+        scale=438,
+        edge='short',
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='CenterCrop', crop_size=384),
+    dict(type='PackInputs')
+]
+train_dataloader = dict(
+    pin_memory=True,
+    persistent_workers=True,
+    collate_fn=dict(type='default_collate'),
+    batch_size=32,
+    num_workers=14,
+    dataset=dict(
+        type='ClassBalancedDataset',
+        oversample_thr=0.01,
+        dataset=dict(
+            type='Fungi',
+            data_root='/scratch/slurm_tmpdir/job_22252118/',
+            ann_file='FungiCLEF2023_train_metadata_PRODUCTION.csv',
+            data_prefix='DF20/',
+            pipeline=[
+                dict(type='LoadImageFromFileFungi'),
+                dict(
+                    type='RandomResizedCrop',
+                    scale=384,
+                    backend='pillow',
+                    interpolation='bicubic'),
+                dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+                dict(
+                    type='RandAugment',
+                    policies='timm_increasing',
+                    num_policies=2,
+                    total_level=10,
+                    magnitude_level=9,
+                    magnitude_std=0.5,
+                    hparams=dict(
+                        pad_val=[104, 116, 124], interpolation='bicubic')),
+                dict(
+                    type='RandomErasing',
+                    erase_prob=0.25,
+                    mode='rand',
+                    min_area_ratio=0.02,
+                    max_area_ratio=0.3333333333333333,
+                    fill_color=[103.53, 116.28, 123.675],
+                    fill_std=[57.375, 57.12, 58.395]),
+                dict(type='PackInputs')
+            ])),
+    sampler=dict(type='DefaultSampler', shuffle=True))
+val_dataloader = dict(
+    pin_memory=True,
+    persistent_workers=True,
+    collate_fn=dict(type='default_collate'),
+    batch_size=64,
+    num_workers=12,
+    dataset=dict(
+        type='Fungi',
+        data_root='/scratch/slurm_tmpdir/job_22252118/',
+        ann_file='FungiCLEF2023_val_metadata_PRODUCTION.csv',
+        data_prefix='DF21/',
+        pipeline=[
+            dict(type='LoadImageFromFileFungi'),
+            dict(
+                type='RandomResizedCrop',
+                scale=384,
+                backend='pillow',
+                interpolation='bicubic'),
+            dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+            dict(
+                type='RandAugment',
+                policies='timm_increasing',
+                num_policies=2,
+                total_level=10,
+                magnitude_level=9,
+                magnitude_std=0.5,
+                hparams=dict(pad_val=[104, 116, 124],
+                             interpolation='bicubic')),
+            dict(
+                type='RandomErasing',
+                erase_prob=0.25,
+                mode='rand',
+                min_area_ratio=0.02,
+                max_area_ratio=0.3333333333333333,
+                fill_color=[103.53, 116.28, 123.675],
+                fill_std=[57.375, 57.12, 58.395]),
+            dict(type='PackInputs')
+        ]),
+    sampler=dict(type='DefaultSampler', shuffle=False))
+val_evaluator = dict(
+    type='SingleLabelMetric', items=['precision', 'recall', 'f1-score'])
+test_dataloader = dict(
+    pin_memory=True,
+    persistent_workers=True,
+    collate_fn=dict(type='default_collate'),
+    batch_size=64,
+    num_workers=12,
+    dataset=dict(
+        type='FungiTest',
+        data_root='data/fungi2023/',
+        ann_file='FungiCLEF2023_public_test_metadata_PRODUCTION.csv',
+        data_prefix='DF21/',
+        pipeline=[
+            dict(type='LoadImageFromFileFungi'),
+            dict(
+                type='ResizeEdge',
+                scale=438,
+                edge='short',
+                backend='pillow',
+                interpolation='bicubic'),
+            dict(type='CenterCrop', crop_size=384),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='PackInputs'),
+        ]),
+    sampler=dict(type='DefaultSampler', shuffle=False))
+test_evaluator = dict(
+    type='SingleLabelMetric', items=['precision', 'recall', 'f1-score'])
+optim_wrapper = dict(
+    optimizer=dict(
+        type='AdamW',
+        lr=6.25e-05,
+        weight_decay=0.05,
+        eps=1e-08,
+        betas=(0.9, 0.999)),
+    paramwise_cfg=dict(
+        norm_decay_mult=0.0,
+        bias_decay_mult=0.0,
+        flat_decay_mult=0.0,
+        custom_keys=dict({
+            '.absolute_pos_embed': dict(decay_mult=0.0),
+            '.relative_position_bias_table': dict(decay_mult=0.0)
+        })),
+    clip_grad=dict(max_norm=5),
+    type='AmpOptimWrapper')
+param_scheduler = [
+    dict(type='LinearLR', start_factor=0.01, by_epoch=False, end=4200),
+    dict(type='CosineAnnealingLR', eta_min=0, by_epoch=False, begin=4200)
+]
+train_cfg = dict(by_epoch=True, max_epochs=6, val_interval=1)
+val_cfg = dict()
+test_cfg = dict()
+auto_scale_lr = dict(base_batch_size=64, enable=True)
+default_scope = 'mmpretrain'
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=100),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', interval=1),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='VisualizationHook', enable=False))
+env_cfg = dict(
+    cudnn_benchmark=False,
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    dist_cfg=dict(backend='nccl'))
+vis_backends = [
+    dict(type='LocalVisBackend'),
+    dict(type='TensorboardVisBackend')
+]
+visualizer = dict(
+    type='UniversalVisualizer',
+    vis_backends=[
+        dict(type='LocalVisBackend'),
+        dict(type='TensorboardVisBackend')
+    ])
+log_level = 'INFO'
+load_from = None
+resume = False
+randomness = dict(seed=None, deterministic=False)
+checkpoint = 'https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-base_3rdparty_in21k-384px.pth'
+custom_imports = dict(
+    imports=['mmpretrain_custom'], allow_failed_imports=False)
+launcher = 'pytorch'
+work_dir = './work_dirs/swin_base_b32x4-fp16_fungi+val_res_384_cb_epochs_6'

models/swinv2_base_w24_b32x4-fp16_fungi+val_res_384_cb_epochs_6.py

@@ -0,0 +1,284 @@
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='SwinTransformerV2',
+        arch='base',
+        img_size=384,
+        drop_path_rate=0.2,
+        window_size=[24, 24, 24, 12],
+        pretrained_window_sizes=[12, 12, 12, 6],
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint=
+            'https://download.openmmlab.com/mmclassification/v0/swin-v2/pretrain/swinv2-base-w12_3rdparty_in21k-192px_20220803-f7dc9763.pth',
+            prefix='backbone')),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='LinearClsHead',
+        num_classes=1604,
+        in_channels=1024,
+        init_cfg=None,
+        loss=dict(
+            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
+        cal_acc=False),
+    init_cfg=[
+        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.0),
+        dict(type='Constant', layer='LayerNorm', val=1.0, bias=0.0)
+    ],
+    train_cfg=dict())
+rand_increasing_policies = [
+    dict(type='AutoContrast'),
+    dict(type='Equalize'),
+    dict(type='Invert'),
+    dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),
+    dict(type='Posterize', magnitude_key='bits', magnitude_range=(4, 0)),
+    dict(type='Solarize', magnitude_key='thr', magnitude_range=(256, 0)),
+    dict(
+        type='SolarizeAdd',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 110)),
+    dict(
+        type='ColorTransform',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.9)),
+    dict(type='Contrast', magnitude_key='magnitude', magnitude_range=(0, 0.9)),
+    dict(
+        type='Brightness', magnitude_key='magnitude',
+        magnitude_range=(0, 0.9)),
+    dict(
+        type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0.9)),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        direction='horizontal'),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        direction='vertical'),
+    dict(
+        type='Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.45),
+        direction='horizontal'),
+    dict(
+        type='Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.45),
+        direction='vertical')
+]
+dataset_type = 'Fungi'
+data_preprocessor = dict(
+    num_classes=1604,
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    to_rgb=True)
+bgr_mean = [103.53, 116.28, 123.675]
+bgr_std = [57.375, 57.12, 58.395]
+train_pipeline = [
+    dict(type='LoadImageFromFileFungi'),
+    dict(
+        type='RandomResizedCrop',
+        scale=384,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+    dict(
+        type='RandAugment',
+        policies='timm_increasing',
+        num_policies=2,
+        total_level=10,
+        magnitude_level=9,
+        magnitude_std=0.5,
+        hparams=dict(pad_val=[104, 116, 124], interpolation='bicubic')),
+    dict(
+        type='RandomErasing',
+        erase_prob=0.25,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=0.3333333333333333,
+        fill_color=[103.53, 116.28, 123.675],
+        fill_std=[57.375, 57.12, 58.395]),
+    dict(type='PackInputs')
+]
+test_pipeline = [
+    dict(type='LoadImageFromFileFungi'),
+    dict(
+        type='ResizeEdge',
+        scale=438,
+        edge='short',
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='CenterCrop', crop_size=384),
+    dict(type='PackInputs')
+]
+train_dataloader = dict(
+    pin_memory=True,
+    persistent_workers=True,
+    collate_fn=dict(type='default_collate'),
+    batch_size=32,
+    num_workers=14,
+    dataset=dict(
+        type='ClassBalancedDataset',
+        oversample_thr=0.01,
+        dataset=dict(
+            type='Fungi',
+            data_root='/scratch/slurm_tmpdir/job_22252299/',
+            ann_file='FungiCLEF2023_train_metadata_PRODUCTION.csv',
+            data_prefix='DF20/',
+            pipeline=[
+                dict(type='LoadImageFromFileFungi'),
+                dict(
+                    type='RandomResizedCrop',
+                    scale=384,
+                    backend='pillow',
+                    interpolation='bicubic'),
+                dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+                dict(
+                    type='RandAugment',
+                    policies='timm_increasing',
+                    num_policies=2,
+                    total_level=10,
+                    magnitude_level=9,
+                    magnitude_std=0.5,
+                    hparams=dict(
+                        pad_val=[104, 116, 124], interpolation='bicubic')),
+                dict(
+                    type='RandomErasing',
+                    erase_prob=0.25,
+                    mode='rand',
+                    min_area_ratio=0.02,
+                    max_area_ratio=0.3333333333333333,
+                    fill_color=[103.53, 116.28, 123.675],
+                    fill_std=[57.375, 57.12, 58.395]),
+                dict(type='PackInputs')
+            ])),
+    sampler=dict(type='DefaultSampler', shuffle=True))
+val_dataloader = dict(
+    pin_memory=True,
+    persistent_workers=True,
+    collate_fn=dict(type='default_collate'),
+    batch_size=64,
+    num_workers=12,
+    dataset=dict(
+        type='Fungi',
+        data_root='/scratch/slurm_tmpdir/job_22252299/',
+        ann_file='FungiCLEF2023_val_metadata_PRODUCTION.csv',
+        data_prefix='DF21/',
+        pipeline=[
+            dict(type='LoadImageFromFileFungi'),
+            dict(
+                type='RandomResizedCrop',
+                scale=384,
+                backend='pillow',
+                interpolation='bicubic'),
+            dict(type='RandomFlip', prob=0.5, direction='horizontal'),
+            dict(
+                type='RandAugment',
+                policies='timm_increasing',
+                num_policies=2,
+                total_level=10,
+                magnitude_level=9,
+                magnitude_std=0.5,
+                hparams=dict(pad_val=[104, 116, 124],
+                             interpolation='bicubic')),
+            dict(
+                type='RandomErasing',
+                erase_prob=0.25,
+                mode='rand',
+                min_area_ratio=0.02,
+                max_area_ratio=0.3333333333333333,
+                fill_color=[103.53, 116.28, 123.675],
+                fill_std=[57.375, 57.12, 58.395]),
+            dict(type='PackInputs')
+        ]),
+    sampler=dict(type='DefaultSampler', shuffle=False))
+val_evaluator = dict(
+    type='SingleLabelMetric', items=['precision', 'recall', 'f1-score'])
+test_dataloader = dict(
+    pin_memory=True,
+    persistent_workers=True,
+    collate_fn=dict(type='default_collate'),
+    batch_size=64,
+    num_workers=12,
+    dataset=dict(
+        type='FungiTest',
+        data_root='data/fungi2023/',
+        ann_file='FungiCLEF2023_public_test_metadata_PRODUCTION.csv',
+        data_prefix='DF21/',
+        pipeline=[
+            dict(type='LoadImageFromFileFungi'),
+            dict(
+                type='ResizeEdge',
+                scale=438,
+                edge='short',
+                backend='pillow',
+                interpolation='bicubic'),
+            dict(type='CenterCrop', crop_size=384),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='PackInputs'),
+        ]),
+    sampler=dict(type='DefaultSampler', shuffle=False))
+test_evaluator = dict(
+    type='SingleLabelMetric', items=['precision', 'recall', 'f1-score'])
+optim_wrapper = dict(
+    optimizer=dict(
+        type='AdamW',
+        lr=6.25e-05,
+        weight_decay=0.05,
+        eps=1e-08,
+        betas=(0.9, 0.999)),
+    paramwise_cfg=dict(
+        norm_decay_mult=0.0,
+        bias_decay_mult=0.0,
+        flat_decay_mult=0.0,
+        custom_keys=dict({
+            '.absolute_pos_embed': dict(decay_mult=0.0),
+            '.relative_position_bias_table': dict(decay_mult=0.0)
+        })),
+    clip_grad=dict(max_norm=5),
+    type='AmpOptimWrapper')
+param_scheduler = [
+    dict(type='LinearLR', start_factor=0.01, by_epoch=False, end=4200),
+    dict(type='CosineAnnealingLR', eta_min=0, by_epoch=False, begin=4200)
+]
+train_cfg = dict(by_epoch=True, max_epochs=6, val_interval=1)
+val_cfg = dict()
+test_cfg = dict()
+auto_scale_lr = dict(base_batch_size=64, enable=True)
+default_scope = 'mmpretrain'
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=100),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', interval=1),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='VisualizationHook', enable=False))
+env_cfg = dict(
+    cudnn_benchmark=False,
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    dist_cfg=dict(backend='nccl'))
+vis_backends = [
+    dict(type='LocalVisBackend'),
+    dict(type='TensorboardVisBackend')
+]
+visualizer = dict(
+    type='UniversalVisualizer',
+    vis_backends=[
+        dict(type='LocalVisBackend'),
+        dict(type='TensorboardVisBackend')
+    ])
+log_level = 'INFO'
+load_from = None
+resume = False
+randomness = dict(seed=None, deterministic=False)
+checkpoint = 'https://download.openmmlab.com/mmclassification/v0/swin-v2/pretrain/swinv2-base-w12_3rdparty_in21k-192px_20220803-f7dc9763.pth'
+custom_imports = dict(
+    imports=['mmpretrain_custom'], allow_failed_imports=False)
+launcher = 'pytorch'
+work_dir = './work_dirs/swinv2_base_w24_b32x4-fp16_fungi+val_res_384_cb_epochs_6'

models/swinv2_base_w24_b32x4-fp16_fungi+val_res_384_cb_epochs_6_20230524-a251a50a.pth

@@ -0,0 +1,3 @@
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+size 413462721