Delete StomataPy400K_density__betatest_n387

StomataPy400K_density__betatest_n387/det_rein_dinov2_mask2former_evensample.py

@@ -1,813 +0,0 @@
-ReduceOnPlateauLR_patience = 50
-all_datasets = [
-    'ClearStain_Brightfield',
-    'Imprints_Brightfield',
-    'Imprints_DIC',
-    'Leaf_Brightfield',
-    'Leaf_Topometry',
-    'Peels_Brightfield',
-    'Peels_SEM',
-    'Beta_test',
-]
-auto_scale_lr = dict(base_batch_size=16, enable=False)
-batch_augments = [
-    dict(
-        img_pad_value=0,
-        mask_pad_value=0,
-        pad_mask=True,
-        pad_seg=False,
-        seg_pad_value=255,
-        size=(
-            512,
-            512,
-        ),
-        type='BatchFixedSizePad'),
-]
-batch_size = 2
-classes = ('stomatal complex', )
-crop_size = (
-    1024,
-    768,
-)
-custom_hooks = [
-    dict(type='NumClassCheckHook'),
-    dict(interval=200, type='MemoryProfilerHook'),
-    dict(interval=200, type='CheckInvalidLossHook'),
-    dict(type='EMAHook'),
-]
-custom_imports = dict(
-    allow_failed_imports=False, imports=[
-        'mmpretrain.models',
-    ])
-data_preprocessor = dict(
-    batch_augments=[
-        dict(
-            img_pad_value=0,
-            mask_pad_value=0,
-            pad_mask=True,
-            pad_seg=False,
-            seg_pad_value=255,
-            size=(
-                512,
-                512,
-            ),
-            type='BatchFixedSizePad'),
-    ],
-    bgr_to_rgb=True,
-    mask_pad_value=0,
-    mean=[
-        123.675,
-        116.28,
-        103.53,
-    ],
-    pad_mask=True,
-    pad_seg=False,
-    pad_size_divisor=32,
-    seg_pad_value=255,
-    std=[
-        58.395,
-        57.12,
-        57.375,
-    ],
-    type='DetDataPreprocessor')
-data_root = 'train/data/Ensemble/'
-dataset_type = 'CocoEevenSamplerDataset'
-default_hooks = dict(
-    checkpoint=dict(
-        by_epoch=True,
-        interval=24,
-        max_keep_ckpts=5,
-        rule='greater',
-        save_best='coco/segm_mAP',
-        save_last=True,
-        type='CheckpointHook'),
-    early_stopping=dict(
-        monitor='coco/segm_mAP',
-        patience=150,
-        rule='greater',
-        type='EarlyStoppingHook'),
-    logger=dict(interval=200, type='LoggerHook'),
-    param_scheduler=dict(type='ParamSchedulerHook'),
-    sampler_seed=dict(type='DistSamplerSeedHook'),
-    timer=dict(type='IterTimerHook'),
-    visualization=dict(draw=True, interval=10, type='DetVisualizationHook'))
-default_scope = 'mmdet'
-dinov2_checkpoint = 'train/checkpoints/dinov2_converted.pth'
-early_stopping_patience = 150
-embed_multi = dict(decay_mult=0.0, lr_mult=1.0)
-env_cfg = dict(
-    cudnn_benchmark=False,
-    dist_cfg=dict(backend='nccl'),
-    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
-find_unused_parameters = True
-fp16 = dict(loss_scale='dynamic')
-image_size = (
-    512,
-    512,
-)
-launcher = 'pytorch'
-load_from = 'Models//2025.02.07_det_stomata_21K//best_coco_segm_mAP_epoch_72.pth'
-load_pipeline = [
-    dict(to_float32=True, type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-    dict(prob=0.5, type='RandomFlip'),
-    dict(type='PhotoMetricDistortion'),
-    dict(
-        img_border_value=(
-            0,
-            0,
-            0,
-        ),
-        interpolation='lanczos',
-        prob=0.5,
-        type='GeomTransform'),
-    dict(keep_ratio=True, scale=(
-        1024,
-        768,
-    ), type='Resize'),
-]
-log_level = 'INFO'
-log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
-lr = 9.375e-05
-max_epochs = 120
-model = dict(
-    backbone=dict(
-        block_chunks=0,
-        depth=24,
-        embed_dim=1024,
-        ffn_bias=True,
-        ffn_layer='mlp',
-        img_size=512,
-        init_cfg=dict(
-            checkpoint='train/checkpoints/dinov2_converted.pth',
-            type='Pretrained'),
-        init_values=1e-05,
-        mlp_ratio=4,
-        num_heads=16,
-        patch_size=16,
-        proj_bias=True,
-        qkv_bias=True,
-        reins_config=dict(
-            embed_dims=1024,
-            link_token_to_query=True,
-            lora_dim=16,
-            num_layers=24,
-            patch_size=16,
-            token_length=100,
-            type='LoRAReins'),
-        type='ReinsDinoVisionTransformer'),
-    data_preprocessor=dict(
-        batch_augments=[
-            dict(
-                img_pad_value=0,
-                mask_pad_value=0,
-                pad_mask=True,
-                pad_seg=False,
-                seg_pad_value=255,
-                size=(
-                    512,
-                    512,
-                ),
-                type='BatchFixedSizePad'),
-        ],
-        bgr_to_rgb=True,
-        mask_pad_value=0,
-        mean=[
-            123.675,
-            116.28,
-            103.53,
-        ],
-        pad_mask=True,
-        pad_seg=False,
-        pad_size_divisor=32,
-        seg_pad_value=255,
-        std=[
-            58.395,
-            57.12,
-            57.375,
-        ],
-        type='DetDataPreprocessor'),
-    init_cfg=None,
-    panoptic_fusion_head=dict(
-        init_cfg=None,
-        loss_panoptic=None,
-        num_stuff_classes=0,
-        num_things_classes=1,
-        type='MaskFormerFusionHead'),
-    panoptic_head=dict(
-        enforce_decoder_input_project=False,
-        feat_channels=256,
-        in_channels=[
-            1024,
-            1024,
-            1024,
-            1024,
-        ],
-        loss_cls=dict(
-            class_weight=[
-                1.0,
-                0.1,
-            ],
-            loss_weight=2.0,
-            reduction='mean',
-            type='CrossEntropyLoss',
-            use_sigmoid=False),
-        loss_dice=dict(
-            activate=True,
-            eps=1.0,
-            loss_weight=5.0,
-            naive_dice=True,
-            reduction='mean',
-            type='DiceLoss',
-            use_sigmoid=True),
-        loss_mask=dict(
-            loss_weight=5.0,
-            reduction='mean',
-            type='CrossEntropyLoss',
-            use_sigmoid=True),
-        num_queries=100,
-        num_stuff_classes=0,
-        num_things_classes=1,
-        num_transformer_feat_level=3,
-        out_channels=256,
-        pixel_decoder=dict(
-            act_cfg=dict(type='ReLU'),
-            encoder=dict(
-                layer_cfg=dict(
-                    ffn_cfg=dict(
-                        act_cfg=dict(inplace=True, type='ReLU'),
-                        embed_dims=256,
-                        feedforward_channels=1024,
-                        ffn_drop=0.0,
-                        num_fcs=2),
-                    self_attn_cfg=dict(
-                        batch_first=True,
-                        dropout=0.0,
-                        embed_dims=256,
-                        num_heads=8,
-                        num_levels=3,
-                        num_points=4)),
-                num_layers=6),
-            norm_cfg=dict(num_groups=32, type='GN'),
-            num_outs=3,
-            positional_encoding=dict(normalize=True, num_feats=128),
-            type='MSDeformAttnPixelDecoder'),
-        positional_encoding=dict(normalize=True, num_feats=128),
-        strides=[
-            4,
-            8,
-            16,
-            32,
-        ],
-        transformer_decoder=dict(
-            init_cfg=None,
-            layer_cfg=dict(
-                cross_attn_cfg=dict(
-                    batch_first=True, dropout=0.0, embed_dims=256,
-                    num_heads=8),
-                ffn_cfg=dict(
-                    act_cfg=dict(inplace=True, type='ReLU'),
-                    embed_dims=256,
-                    feedforward_channels=2048,
-                    ffn_drop=0.0,
-                    num_fcs=2),
-                self_attn_cfg=dict(
-                    batch_first=True, dropout=0.0, embed_dims=256,
-                    num_heads=8)),
-            num_layers=9,
-            return_intermediate=True),
-        type='ReinMask2FormerHead'),
-    test_cfg=dict(
-        filter_low_score=True,
-        instance_on=True,
-        iou_thr=0.8,
-        max_per_image=100,
-        panoptic_on=False,
-        semantic_on=False),
-    train_cfg=dict(
-        assigner=dict(
-            match_costs=[
-                dict(type='ClassificationCost', weight=2.0),
-                dict(
-                    type='CrossEntropyLossCost', use_sigmoid=True, weight=5.0),
-                dict(eps=1.0, pred_act=True, type='DiceCost', weight=5.0),
-            ],
-            type='HungarianAssigner'),
-        importance_sample_ratio=0.75,
-        num_points=12544,
-        oversample_ratio=3.0,
-        sampler=dict(type='MaskPseudoSampler')),
-    type='Mask2Former')
-n_gpus = 6
-num_classes = 1
-num_stuff_classes = 0
-num_things_classes = 1
-num_workers = 16
-optim_wrapper = dict(
-    clip_grad=dict(max_norm=0.01, norm_type=2),
-    constructor='PEFTOptimWrapperConstructor',
-    optimizer=dict(
-        betas=(
-            0.9,
-            0.999,
-        ),
-        eps=1e-08,
-        lr=9.375e-05,
-        type='AdamW',
-        weight_decay=0.05),
-    paramwise_cfg=dict(
-        custom_keys=dict(
-            backbone=dict(decay_mult=1.0, lr_mult=0.1),
-            level_embed=dict(decay_mult=0.0, lr_mult=1.0),
-            query_embed=dict(decay_mult=0.0, lr_mult=1.0),
-            query_feat=dict(decay_mult=0.0, lr_mult=1.0)),
-        norm_decay_mult=0.0),
-    type='OptimWrapper')
-optimizer_config = dict(
-    cumulative_iters=4, type='GradientCumulativeOptimizerHook')
-original_batch_size = 16
-original_lr = 0.0001
-original_n_gpus = 8
-output_dir = '2025.03.20_Beta_test_n387'
-param_scheduler = [
-    dict(
-        begin=0,
-        by_epoch=True,
-        convert_to_iter_based=True,
-        end=30,
-        end_factor=1.0,
-        start_factor=0.001,
-        type='LinearLR',
-        verbose=False),
-    dict(
-        T_max=90,
-        begin=30,
-        by_epoch=True,
-        convert_to_iter_based=True,
-        end=120,
-        eta_min=9.375e-08,
-        eta_min_ratio=None,
-        type='CosineAnnealingLR',
-        verbose=False),
-    dict(
-        by_epoch=True,
-        factor=0.75,
-        monitor='coco/bbox_mAP',
-        patience=50,
-        rule='greater',
-        type='ReduceOnPlateauLR',
-        verbose=False),
-]
-randomness = dict(deterministic=False, seed=42)
-resume = None
-test_ann_file = 'COCO.json'
-test_cfg = dict(type='ValLoop')
-test_dataloader = dict(
-    batch_sampler=dict(type='AspectRatioBatchSampler'),
-    batch_size=2,
-    dataset=dict(
-        batch_size=2,
-        dataset=dict(
-            all_datasets=[
-                'ClearStain_Brightfield',
-                'Imprints_Brightfield',
-                'Imprints_DIC',
-                'Leaf_Brightfield',
-                'Leaf_Topometry',
-                'Peels_Brightfield',
-                'Peels_SEM',
-                'Beta_test',
-            ],
-            ann_file='COCO.json',
-            backend_args=None,
-            data_prefix=dict(
-                img='test/', seg='annotations/panoptic_train2017/'),
-            data_root='train/data/Ensemble/',
-            metainfo=dict(classes=('stomatal complex', )),
-            pipeline=[
-                dict(to_float32=True, type='LoadImageFromFile'),
-                dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-            ],
-            test_mode=False,
-            total_samples=2625,
-            type='CocoEevenSamplerDataset'),
-        mode='val',
-        pipeline=[
-            dict(keep_ratio=True, scale=(
-                1024,
-                768,
-            ), type='Resize'),
-            dict(
-                meta_keys=(
-                    'img_id',
-                    'img_path',
-                    'img',
-                    'img_shape',
-                    'ori_shape',
-                    'scale_factor',
-                    'gt_bboxes',
-                    'gt_ignore_flags',
-                    'gt_bboxes_labels',
-                    'gt_masks',
-                ),
-                type='PackDetInputs'),
-        ],
-        type='MultiImageMixEvenSamplerDataset'),
-    drop_last=False,
-    num_workers=16,
-    persistent_workers=True,
-    sampler=dict(shuffle=False, type='DefaultSampler'))
-test_dataset = dict(
-    batch_size=2,
-    dataset=dict(
-        all_datasets=[
-            'ClearStain_Brightfield',
-            'Imprints_Brightfield',
-            'Imprints_DIC',
-            'Leaf_Brightfield',
-            'Leaf_Topometry',
-            'Peels_Brightfield',
-            'Peels_SEM',
-            'Beta_test',
-        ],
-        ann_file='COCO.json',
-        backend_args=None,
-        data_prefix=dict(img='test/', seg='annotations/panoptic_train2017/'),
-        data_root='train/data/Ensemble/',
-        metainfo=dict(classes=('stomatal complex', )),
-        pipeline=[
-            dict(to_float32=True, type='LoadImageFromFile'),
-            dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-        ],
-        test_mode=False,
-        total_samples=2625,
-        type='CocoEevenSamplerDataset'),
-    mode='val',
-    pipeline=[
-        dict(keep_ratio=True, scale=(
-            1024,
-            768,
-        ), type='Resize'),
-        dict(
-            meta_keys=(
-                'img_id',
-                'img_path',
-                'img',
-                'img_shape',
-                'ori_shape',
-                'scale_factor',
-                'gt_bboxes',
-                'gt_ignore_flags',
-                'gt_bboxes_labels',
-                'gt_masks',
-            ),
-            type='PackDetInputs'),
-    ],
-    type='MultiImageMixEvenSamplerDataset')
-test_evaluator = dict(
-    ann_file='train/data/Ensemble/sahi_coco_val.json',
-    backend_args=None,
-    format_only=False,
-    metric=[
-        'bbox',
-        'segm',
-    ],
-    type='CocoMetric')
-test_load_pipeline = [
-    dict(to_float32=True, type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-]
-test_pipeline = [
-    dict(keep_ratio=True, scale=(
-        1024,
-        768,
-    ), type='Resize'),
-    dict(
-        meta_keys=(
-            'img_id',
-            'img_path',
-            'img',
-            'img_shape',
-            'ori_shape',
-            'scale_factor',
-            'gt_bboxes',
-            'gt_ignore_flags',
-            'gt_bboxes_labels',
-            'gt_masks',
-        ),
-        type='PackDetInputs'),
-]
-total_samples_train = 10500
-total_samples_val = 2625
-train_ann_file = 'sahi_coco_train.json'
-train_cfg = dict(max_epochs=120, type='EpochBasedTrainLoop', val_interval=12)
-train_dataloader = dict(
-    batch_sampler=dict(type='AspectRatioBatchSampler'),
-    batch_size=2,
-    dataset=dict(
-        batch_size=2,
-        dataset=dict(
-            all_datasets=[
-                'ClearStain_Brightfield',
-                'Imprints_Brightfield',
-                'Imprints_DIC',
-                'Leaf_Brightfield',
-                'Leaf_Topometry',
-                'Peels_Brightfield',
-                'Peels_SEM',
-                'Beta_test',
-            ],
-            ann_file='sahi_coco_train.json',
-            backend_args=None,
-            data_prefix=dict(
-                img='train_sahi/', seg='annotations/panoptic_train2017/'),
-            data_root='train/data/Ensemble/',
-            filter_cfg=dict(filter_empty_gt=True, min_size=32),
-            metainfo=dict(classes=('stomatal complex', )),
-            pipeline=[
-                dict(to_float32=True, type='LoadImageFromFile'),
-                dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-                dict(prob=0.5, type='RandomFlip'),
-                dict(type='PhotoMetricDistortion'),
-                dict(
-                    img_border_value=(
-                        0,
-                        0,
-                        0,
-                    ),
-                    interpolation='lanczos',
-                    prob=0.5,
-                    type='GeomTransform'),
-                dict(keep_ratio=True, scale=(
-                    1024,
-                    768,
-                ), type='Resize'),
-            ],
-            total_samples=10500,
-            type='CocoEevenSamplerDataset'),
-        mode='train',
-        n_gpus=6,
-        n_workers=16,
-        pipeline=[
-            dict(
-                bbox_occluded_thr=50,
-                mask_occluded_thr=1000,
-                max_num_pasted=5,
-                paste_by_box=False,
-                selected=True,
-                type='CopyPaste'),
-            dict(
-                by_mask=True,
-                min_gt_bbox_wh=(
-                    10,
-                    10,
-                ),
-                min_gt_mask_area=10,
-                type='FilterAnnotations'),
-            dict(
-                meta_keys=(
-                    'img_path',
-                    'img',
-                    'gt_bboxes',
-                    'gt_ignore_flags',
-                    'gt_bboxes_labels',
-                    'gt_masks',
-                ),
-                type='PackDetInputs'),
-        ],
-        type='MultiImageMixEvenSamplerDataset'),
-    num_workers=16,
-    persistent_workers=True,
-    sampler=dict(shuffle=True, type='DefaultSampler'))
-train_dataset = dict(
-    batch_size=2,
-    dataset=dict(
-        all_datasets=[
-            'ClearStain_Brightfield',
-            'Imprints_Brightfield',
-            'Imprints_DIC',
-            'Leaf_Brightfield',
-            'Leaf_Topometry',
-            'Peels_Brightfield',
-            'Peels_SEM',
-            'Beta_test',
-        ],
-        ann_file='sahi_coco_train.json',
-        backend_args=None,
-        data_prefix=dict(
-            img='train_sahi/', seg='annotations/panoptic_train2017/'),
-        data_root='train/data/Ensemble/',
-        filter_cfg=dict(filter_empty_gt=True, min_size=32),
-        metainfo=dict(classes=('stomatal complex', )),
-        pipeline=[
-            dict(to_float32=True, type='LoadImageFromFile'),
-            dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-            dict(prob=0.5, type='RandomFlip'),
-            dict(type='PhotoMetricDistortion'),
-            dict(
-                img_border_value=(
-                    0,
-                    0,
-                    0,
-                ),
-                interpolation='lanczos',
-                prob=0.5,
-                type='GeomTransform'),
-            dict(keep_ratio=True, scale=(
-                1024,
-                768,
-            ), type='Resize'),
-        ],
-        total_samples=10500,
-        type='CocoEevenSamplerDataset'),
-    mode='train',
-    n_gpus=6,
-    n_workers=16,
-    pipeline=[
-        dict(
-            bbox_occluded_thr=50,
-            mask_occluded_thr=1000,
-            max_num_pasted=5,
-            paste_by_box=False,
-            selected=True,
-            type='CopyPaste'),
-        dict(
-            by_mask=True,
-            min_gt_bbox_wh=(
-                10,
-                10,
-            ),
-            min_gt_mask_area=10,
-            type='FilterAnnotations'),
-        dict(
-            meta_keys=(
-                'img_path',
-                'img',
-                'gt_bboxes',
-                'gt_ignore_flags',
-                'gt_bboxes_labels',
-                'gt_masks',
-            ),
-            type='PackDetInputs'),
-    ],
-    type='MultiImageMixEvenSamplerDataset')
-train_pipeline = [
-    dict(
-        bbox_occluded_thr=50,
-        mask_occluded_thr=1000,
-        max_num_pasted=5,
-        paste_by_box=False,
-        selected=True,
-        type='CopyPaste'),
-    dict(
-        by_mask=True,
-        min_gt_bbox_wh=(
-            10,
-            10,
-        ),
-        min_gt_mask_area=10,
-        type='FilterAnnotations'),
-    dict(
-        meta_keys=(
-            'img_path',
-            'img',
-            'gt_bboxes',
-            'gt_ignore_flags',
-            'gt_bboxes_labels',
-            'gt_masks',
-        ),
-        type='PackDetInputs'),
-]
-val_ann_file = 'sahi_coco_val.json'
-val_cfg = dict(type='ValLoop')
-val_dataloader = dict(
-    batch_sampler=dict(type='AspectRatioBatchSampler'),
-    batch_size=2,
-    dataset=dict(
-        batch_size=2,
-        dataset=dict(
-            all_datasets=[
-                'ClearStain_Brightfield',
-                'Imprints_Brightfield',
-                'Imprints_DIC',
-                'Leaf_Brightfield',
-                'Leaf_Topometry',
-                'Peels_Brightfield',
-                'Peels_SEM',
-                'Beta_test',
-            ],
-            ann_file='sahi_coco_val.json',
-            backend_args=None,
-            data_prefix=dict(
-                img='val_sahi/', seg='annotations/panoptic_train2017/'),
-            data_root='train/data/Ensemble/',
-            metainfo=dict(classes=('stomatal complex', )),
-            pipeline=[
-                dict(to_float32=True, type='LoadImageFromFile'),
-                dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-            ],
-            test_mode=False,
-            total_samples=2625,
-            type='CocoEevenSamplerDataset'),
-        mode='val',
-        n_gpus=6,
-        n_workers=16,
-        pipeline=[
-            dict(keep_ratio=True, scale=(
-                1024,
-                768,
-            ), type='Resize'),
-            dict(
-                meta_keys=(
-                    'img_id',
-                    'img_path',
-                    'img',
-                    'img_shape',
-                    'ori_shape',
-                    'scale_factor',
-                    'gt_bboxes',
-                    'gt_ignore_flags',
-                    'gt_bboxes_labels',
-                    'gt_masks',
-                ),
-                type='PackDetInputs'),
-        ],
-        type='MultiImageMixEvenSamplerDataset'),
-    drop_last=False,
-    num_workers=16,
-    persistent_workers=True,
-    sampler=dict(shuffle=False, type='DefaultSampler'))
-val_dataset = dict(
-    batch_size=2,
-    dataset=dict(
-        all_datasets=[
-            'ClearStain_Brightfield',
-            'Imprints_Brightfield',
-            'Imprints_DIC',
-            'Leaf_Brightfield',
-            'Leaf_Topometry',
-            'Peels_Brightfield',
-            'Peels_SEM',
-            'Beta_test',
-        ],
-        ann_file='sahi_coco_val.json',
-        backend_args=None,
-        data_prefix=dict(
-            img='val_sahi/', seg='annotations/panoptic_train2017/'),
-        data_root='train/data/Ensemble/',
-        metainfo=dict(classes=('stomatal complex', )),
-        pipeline=[
-            dict(to_float32=True, type='LoadImageFromFile'),
-            dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
-        ],
-        test_mode=False,
-        total_samples=2625,
-        type='CocoEevenSamplerDataset'),
-    mode='val',
-    n_gpus=6,
-    n_workers=16,
-    pipeline=[
-        dict(keep_ratio=True, scale=(
-            1024,
-            768,
-        ), type='Resize'),
-        dict(
-            meta_keys=(
-                'img_id',
-                'img_path',
-                'img',
-                'img_shape',
-                'ori_shape',
-                'scale_factor',
-                'gt_bboxes',
-                'gt_ignore_flags',
-                'gt_bboxes_labels',
-                'gt_masks',
-            ),
-            type='PackDetInputs'),
-    ],
-    type='MultiImageMixEvenSamplerDataset')
-val_evaluator = dict(
-    ann_file='train/data/Ensemble/sahi_coco_val.json',
-    backend_args=None,
-    format_only=False,
-    metric=[
-        'bbox',
-        'segm',
-    ],
-    type='CocoMetric')
-val_interval = 12
-visualizer = dict(
-    name='visualizer',
-    type='DetLocalVisualizer',
-    vis_backends=[
-        dict(type='LocalVisBackend'),
-        dict(
-            init_kwargs=dict(
-                name='2025.03.20_Beta_test_n387', project='StomataPy'),
-            type='WandbVisBackend'),
-    ])
-wandb_project = 'StomataPy'
-warmup_epochs = 30
-with_cp = True
-work_dir = 'Models//2025.03.20_Beta_test_n387'

StomataPy400K_density__betatest_n387/dinov2_detector.pth

@@ -1,3 +0,0 @@
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-size 1428376195