Jimmiemunyi's workspace
Runs
71
Name
6 visualized
Runtime
GitHub
Notes
GPU Type
GPU Count
State
Tags
CastToTensor
Learner._name
Learner.arch
Learner.default_cbs
Learner.loss_func._name
Learner.loss_func.axis
Learner.loss_func.flatten
Learner.loss_func.floatify
Learner.loss_func.is_2d
Learner.lr
Learner.metrics
Learner.model_dir
Learner.moms
Learner.n_out
Learner.normalize
Learner.opt_func
Learner.path
Learner.pretrained
Learner.splitter
Learner.train_bn
Learner.wd_bn_bias
MixedPrecision
ParamScheduler
ProgressCallback
Recorder.add_time
Recorder.train_metrics
Recorder.valid_metrics
TrainEvalCallback
WandbCallback.log_dataset
WandbCallback.log_model
WandbCallback.log_preds
WandbCallback.log_preds_every_epoch
WandbCallback.n_preds
WandbCallback.reorder
WandbCallback.seed
batch per epoch
batch size
dataset.tfms
device
dls.after_batch
dls.after_item
dls.before_batch
frozen idx
frozen
1h 41m 14s
-
NVIDIA GeForce RTX 3060
1
Finished
true
<fastai.learner.Learner object at 0x7fe9c959fdc0>
convnext_tiny
true
-
-1
true
false
true
0.001
["fastai.metrics.error_rate","fastai.metrics.accuracy","functools.partial(<function top_k_accuracy at 0x7fe9d03095a0>, k=3)",{"func":"sklearn.metrics._classification.f1_score","_name":"<fastai.metrics.AccumMetric object at 0x7fe9ca9b8df0>","thresh":null,"flatten":true,"activation":"no","dim_argmax":-1}]
models
[0.95,0.85,0.95]
50
true
fastai.optimizer.Adam
.
true
fastai.vision.learner.default_split
true
false
true
true
true
true
false
true
true
false
false
true
false
36
true
12345
1416
8
[Pipeline: partial -> PILBase.create, Pipeline: partial -> Categorize -- {'vocab': None, 'sort': True, 'add_na': False}]
cuda
Pipeline: IntToFloatTensor -- {'div': 255.0, 'div_mask': 1} -> Normalize -- {'mean': tensor([[[[0.4850]],
[[0.4560]],
[[0.4060]]]], device='cuda:0'), 'std': tensor([[[[0.2290]],
[[0.2240]],
[[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}
Pipeline: Resize -- {'size': (850, 850), 'method': 'crop', 'pad_mode': 'reflection', 'resamples': (<Resampling.BILINEAR: 2>, <Resampling.NEAREST: 0>), 'p': 1.0} -> AlbumentationsTransform -- {'train_aug': Compose([
RandomResizedCrop(always_apply=False, p=1.0, height=800, width=800, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1),
Transpose(always_apply=False, p=0.5),
VerticalFlip(always_apply=False, p=0.5),
HorizontalFlip(always_apply=False, p=0.5),
ShiftScaleRotate(always_apply=False, p=0.5, shift_limit_x=(-0.0625, 0.0625), shift_limit_y=(-0.0625, 0.0625), scale_limit=(-0.09999999999999998, 0.10000000000000009), rotate_limit=(-45, 45), interpolation=1, border_mode=4, value=None, mask_value=None, rotate_method='largest_box'),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={}), 'valid_aug': Compose([
CenterCrop(always_apply=False, p=1.0, height=800, width=800),
Resize(always_apply=False, p=1, height=800, width=800, interpolation=1),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={})} -> ToTensor
Pipeline:
0
false
8m 5s
-
NVIDIA GeForce RTX 3060
1
Failed
true
<fastai.learner.Learner object at 0x7f25b44580d0>
convnext_tiny
true
-
-1
true
false
true
0.001
["fastai.metrics.error_rate","fastai.metrics.accuracy","functools.partial(<function top_k_accuracy at 0x7f25b57c95a0>, k=3)",{"activation":"no","dim_argmax":-1,"func":"sklearn.metrics._classification.f1_score","_name":"<fastai.metrics.AccumMetric object at 0x7f25b4478d90>","thresh":null,"flatten":true}]
models
[0.95,0.85,0.95]
50
true
fastai.optimizer.Adam
.
true
fastai.vision.learner.default_split
true
false
true
true
true
true
false
true
true
false
false
true
false
36
true
12345
708
16
[Pipeline: partial -> PILBase.create, Pipeline: partial -> Categorize -- {'vocab': None, 'sort': True, 'add_na': False}]
cuda
Pipeline: IntToFloatTensor -- {'div': 255.0, 'div_mask': 1} -> Normalize -- {'mean': tensor([[[[0.4850]],
[[0.4560]],
[[0.4060]]]], device='cuda:0'), 'std': tensor([[[[0.2290]],
[[0.2240]],
[[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}
Pipeline: Resize -- {'size': (850, 850), 'method': 'crop', 'pad_mode': 'reflection', 'resamples': (<Resampling.BILINEAR: 2>, <Resampling.NEAREST: 0>), 'p': 1.0} -> AlbumentationsTransform -- {'train_aug': Compose([
RandomResizedCrop(always_apply=False, p=1.0, height=800, width=800, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1),
Transpose(always_apply=False, p=0.5),
VerticalFlip(always_apply=False, p=0.5),
HorizontalFlip(always_apply=False, p=0.5),
ShiftScaleRotate(always_apply=False, p=0.5, shift_limit_x=(-0.0625, 0.0625), shift_limit_y=(-0.0625, 0.0625), scale_limit=(-0.09999999999999998, 0.10000000000000009), rotate_limit=(-45, 45), interpolation=1, border_mode=4, value=None, mask_value=None, rotate_method='largest_box'),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={}), 'valid_aug': Compose([
CenterCrop(always_apply=False, p=1.0, height=800, width=800),
Resize(always_apply=False, p=1, height=800, width=800, interpolation=1),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={})} -> ToTensor
Pipeline:
0
false
24m 31s
-
NVIDIA GeForce RTX 3060
1
Finished
true
<fastai.learner.Learner object at 0x7f16de0afee0>
convnext_tiny
true
-
-1
true
false
true
0.001
["fastai.metrics.error_rate","fastai.metrics.accuracy","functools.partial(<function top_k_accuracy at 0x7f16e43812d0>, k=3)",{"_name":"<fastai.metrics.AccumMetric object at 0x7f16de05d7b0>","thresh":null,"flatten":true,"activation":"no","dim_argmax":-1,"func":"sklearn.metrics._classification.f1_score"}]
models
[0.95,0.85,0.95]
50
true
fastai.optimizer.Adam
.
true
fastai.vision.learner.default_split
true
false
true
true
true
true
false
true
true
false
false
true
false
36
true
12345
354
32
[Pipeline: partial -> PILBase.create, Pipeline: partial -> Categorize -- {'vocab': None, 'sort': True, 'add_na': False}]
cuda
Pipeline: IntToFloatTensor -- {'div': 255.0, 'div_mask': 1} -> Normalize -- {'mean': tensor([[[[0.4850]],
[[0.4560]],
[[0.4060]]]], device='cuda:0'), 'std': tensor([[[[0.2290]],
[[0.2240]],
[[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}
Pipeline: Resize -- {'size': (400, 400), 'method': 'crop', 'pad_mode': 'reflection', 'resamples': (<Resampling.BILINEAR: 2>, <Resampling.NEAREST: 0>), 'p': 1.0} -> AlbumentationsTransform -- {'train_aug': Compose([
RandomResizedCrop(always_apply=False, p=1.0, height=384, width=384, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1),
Transpose(always_apply=False, p=0.5),
VerticalFlip(always_apply=False, p=0.5),
HorizontalFlip(always_apply=False, p=0.5),
ShiftScaleRotate(always_apply=False, p=0.5, shift_limit_x=(-0.0625, 0.0625), shift_limit_y=(-0.0625, 0.0625), scale_limit=(-0.09999999999999998, 0.10000000000000009), rotate_limit=(-45, 45), interpolation=1, border_mode=4, value=None, mask_value=None, rotate_method='largest_box'),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={}), 'valid_aug': Compose([
CenterCrop(always_apply=False, p=1.0, height=384, width=384),
Resize(always_apply=False, p=1, height=384, width=384, interpolation=1),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={})} -> ToTensor
Pipeline:
0
false
24m 30s
-
NVIDIA GeForce RTX 3060
1
Finished
true
<fastai.learner.Learner object at 0x7f889c307ee0>
convnext_tiny
true
-
-1
true
false
true
0.001
["fastai.metrics.error_rate","fastai.metrics.accuracy","functools.partial(<function top_k_accuracy at 0x7f88a46f1000>, k=3)",{"_name":"<fastai.metrics.AccumMetric object at 0x7f889f3a1180>","thresh":null,"flatten":true,"activation":"no","dim_argmax":-1,"func":"sklearn.metrics._classification.f1_score"}]
models
[0.95,0.85,0.95]
50
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fastai.optimizer.Adam
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true
fastai.vision.learner.default_split
true
false
true
true
true
true
false
true
true
false
false
true
false
36
true
12345
354
32
[Pipeline: partial -> PILBase.create, Pipeline: partial -> Categorize -- {'vocab': None, 'sort': True, 'add_na': False}]
cuda
Pipeline: IntToFloatTensor -- {'div': 255.0, 'div_mask': 1} -> Normalize -- {'mean': tensor([[[[0.4850]],
[[0.4560]],
[[0.4060]]]], device='cuda:0'), 'std': tensor([[[[0.2290]],
[[0.2240]],
[[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}
Pipeline: Resize -- {'size': (400, 400), 'method': 'crop', 'pad_mode': 'reflection', 'resamples': (<Resampling.BILINEAR: 2>, <Resampling.NEAREST: 0>), 'p': 1.0} -> AlbumentationsTransform -- {'train_aug': Compose([
RandomResizedCrop(always_apply=False, p=1.0, height=384, width=384, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1),
Transpose(always_apply=False, p=0.5),
VerticalFlip(always_apply=False, p=0.5),
HorizontalFlip(always_apply=False, p=0.5),
ShiftScaleRotate(always_apply=False, p=0.5, shift_limit_x=(-0.0625, 0.0625), shift_limit_y=(-0.0625, 0.0625), scale_limit=(-0.09999999999999998, 0.10000000000000009), rotate_limit=(-45, 45), interpolation=1, border_mode=4, value=None, mask_value=None, rotate_method='largest_box'),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={}), 'valid_aug': Compose([
CenterCrop(always_apply=False, p=1.0, height=384, width=384),
Resize(always_apply=False, p=1, height=384, width=384, interpolation=1),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={})} -> ToTensor
Pipeline:
0
false
2m 13s
-
NVIDIA GeForce RTX 3060
1
Failed
true
<fastai.learner.Learner object at 0x7f240019ffa0>
convnext_tiny
true
-
-1
true
false
true
0.001
["fastai.metrics.error_rate","fastai.metrics.accuracy","functools.partial(<function top_k_accuracy at 0x7f2408275000>, k=3)",{"func":"sklearn.metrics._classification.f1_score","_name":"<fastai.metrics.AccumMetric object at 0x7f24021c5240>","thresh":null,"flatten":true,"activation":"no","dim_argmax":-1}]
models
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50
true
fastai.optimizer.Adam
.
true
fastai.vision.learner.default_split
true
false
true
true
true
true
false
true
true
false
false
true
false
36
true
12345
177
64
[Pipeline: partial -> PILBase.create, Pipeline: partial -> Categorize -- {'vocab': None, 'sort': True, 'add_na': False}]
cuda
Pipeline: IntToFloatTensor -- {'div': 255.0, 'div_mask': 1} -> Normalize -- {'mean': tensor([[[[0.4850]],
[[0.4560]],
[[0.4060]]]], device='cuda:0'), 'std': tensor([[[[0.2290]],
[[0.2240]],
[[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}
Pipeline: Resize -- {'size': (400, 400), 'method': 'crop', 'pad_mode': 'reflection', 'resamples': (<Resampling.BILINEAR: 2>, <Resampling.NEAREST: 0>), 'p': 1.0} -> AlbumentationsTransform -- {'train_aug': Compose([
RandomResizedCrop(always_apply=False, p=1.0, height=384, width=384, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1),
Transpose(always_apply=False, p=0.5),
VerticalFlip(always_apply=False, p=0.5),
HorizontalFlip(always_apply=False, p=0.5),
ShiftScaleRotate(always_apply=False, p=0.5, shift_limit_x=(-0.0625, 0.0625), shift_limit_y=(-0.0625, 0.0625), scale_limit=(-0.09999999999999998, 0.10000000000000009), rotate_limit=(-45, 45), interpolation=1, border_mode=4, value=None, mask_value=None, rotate_method='largest_box'),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={}), 'valid_aug': Compose([
CenterCrop(always_apply=False, p=1.0, height=384, width=384),
Resize(always_apply=False, p=1, height=384, width=384, interpolation=1),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={})} -> ToTensor
Pipeline:
0
false
10m 23s
-
NVIDIA GeForce RTX 3060
1
Finished
true
<fastai.learner.Learner object at 0x7fe748133eb0>
convnext_tiny
true
-
-1
true
false
true
0.001
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models
[0.95,0.85,0.95]
50
true
fastai.optimizer.Adam
.
true
fastai.vision.learner.default_split
true
false
true
true
true
true
false
true
true
false
false
true
false
36
true
12345
177
64
[Pipeline: partial -> PILBase.create, Pipeline: partial -> Categorize -- {'vocab': None, 'sort': True, 'add_na': False}]
cuda
Pipeline: IntToFloatTensor -- {'div': 255.0, 'div_mask': 1} -> Normalize -- {'mean': tensor([[[[0.4850]],
[[0.4560]],
[[0.4060]]]], device='cuda:0'), 'std': tensor([[[[0.2290]],
[[0.2240]],
[[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}
Pipeline: Resize -- {'size': (256, 256), 'method': 'crop', 'pad_mode': 'reflection', 'resamples': (<Resampling.BILINEAR: 2>, <Resampling.NEAREST: 0>), 'p': 1.0} -> AlbumentationsTransform -- {'train_aug': Compose([
RandomResizedCrop(always_apply=False, p=1.0, height=224, width=224, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1),
Transpose(always_apply=False, p=0.5),
VerticalFlip(always_apply=False, p=0.5),
HorizontalFlip(always_apply=False, p=0.5),
ShiftScaleRotate(always_apply=False, p=0.5, shift_limit_x=(-0.0625, 0.0625), shift_limit_y=(-0.0625, 0.0625), scale_limit=(-0.09999999999999998, 0.10000000000000009), rotate_limit=(-45, 45), interpolation=1, border_mode=4, value=None, mask_value=None, rotate_method='largest_box'),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={}), 'valid_aug': Compose([
CenterCrop(always_apply=False, p=1.0, height=224, width=224),
Resize(always_apply=False, p=1, height=224, width=224, interpolation=1),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={})} -> ToTensor
Pipeline:
0
false
20m 30s
-
NVIDIA GeForce RTX 3060
1
Finished
true
<fastai.learner.Learner object at 0x7f1215a97f70>
convnext_tiny
true
-
-1
true
false
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0.001
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models
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50
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fastai.optimizer.Adam
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true
fastai.vision.learner.default_split
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false
true
true
true
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true
true
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12345
177
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[Pipeline: partial -> PILBase.create, Pipeline: partial -> Categorize -- {'vocab': None, 'sort': True, 'add_na': False}]
cuda
Pipeline: IntToFloatTensor -- {'div': 255.0, 'div_mask': 1} -> Normalize -- {'mean': tensor([[[[0.4850]],
[[0.4560]],
[[0.4060]]]], device='cuda:0'), 'std': tensor([[[[0.2290]],
[[0.2240]],
[[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}
Pipeline: Resize -- {'size': (256, 256), 'method': 'crop', 'pad_mode': 'reflection', 'resamples': (<Resampling.BILINEAR: 2>, <Resampling.NEAREST: 0>), 'p': 1.0} -> AlbumentationsTransform -- {'train_aug': Compose([
RandomResizedCrop(always_apply=False, p=1.0, height=224, width=224, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1),
Transpose(always_apply=False, p=0.5),
VerticalFlip(always_apply=False, p=0.5),
HorizontalFlip(always_apply=False, p=0.5),
ShiftScaleRotate(always_apply=False, p=0.5, shift_limit_x=(-0.0625, 0.0625), shift_limit_y=(-0.0625, 0.0625), scale_limit=(-0.09999999999999998, 0.10000000000000009), rotate_limit=(-45, 45), interpolation=1, border_mode=4, value=None, mask_value=None, rotate_method='largest_box'),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={}), 'valid_aug': Compose([
CenterCrop(always_apply=False, p=1.0, height=224, width=224),
Resize(always_apply=False, p=1, height=224, width=224, interpolation=1),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={})} -> ToTensor
Pipeline:
0
false
20m 37s
-
NVIDIA GeForce RTX 3060
1
Crashed
true
<fastai.learner.Learner object at 0x7f15c164bf40>
convnext_tiny
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models
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50
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fastai.optimizer.Adam
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fastai.vision.learner.default_split
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12345
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[Pipeline: partial -> PILBase.create, Pipeline: partial -> Categorize -- {'vocab': None, 'sort': True, 'add_na': False}]
cuda
Pipeline: IntToFloatTensor -- {'div': 255.0, 'div_mask': 1} -> Normalize -- {'mean': tensor([[[[0.4850]],
[[0.4560]],
[[0.4060]]]], device='cuda:0'), 'std': tensor([[[[0.2290]],
[[0.2240]],
[[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}
Pipeline: Resize -- {'size': (256, 256), 'method': 'crop', 'pad_mode': 'reflection', 'resamples': (<Resampling.BILINEAR: 2>, <Resampling.NEAREST: 0>), 'p': 1.0} -> AlbumentationsTransform -- {'train_aug': Compose([
RandomResizedCrop(always_apply=False, p=1.0, height=224, width=224, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1),
Transpose(always_apply=False, p=0.5),
VerticalFlip(always_apply=False, p=0.5),
HorizontalFlip(always_apply=False, p=0.5),
ShiftScaleRotate(always_apply=False, p=0.5, shift_limit_x=(-0.0625, 0.0625), shift_limit_y=(-0.0625, 0.0625), scale_limit=(-0.09999999999999998, 0.10000000000000009), rotate_limit=(-45, 45), interpolation=1, border_mode=4, value=None, mask_value=None, rotate_method='largest_box'),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={}), 'valid_aug': Compose([
CenterCrop(always_apply=False, p=1.0, height=224, width=224),
Resize(always_apply=False, p=1, height=224, width=224, interpolation=1),
], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={})} -> ToTensor
Pipeline:
0
false
10m 50s
-
NVIDIA GeForce RTX 3060
1
Finished
true
<fastai.learner.Learner object at 0x7f601be2be20>
convnext_tiny
true
-
-1
true
false
true
0.001
["fastai.metrics.error_rate","fastai.metrics.accuracy","functools.partial(<function top_k_accuracy at 0x7f60212c1000>, k=3)",{"dim_argmax":-1,"func":"sklearn.metrics._classification.f1_score","_name":"<fastai.metrics.AccumMetric object at 0x7f60181b8610>","thresh":null,"flatten":true,"activation":"no"}]
models
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50
true
fastai.optimizer.Adam
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true
fastai.vision.learner.default_split
true
false
true
true
true
true
false
true
true
false
false
true
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36
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12345
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[Pipeline: partial -> PILBase.create, Pipeline: partial -> Categorize -- {'vocab': None, 'sort': True, 'add_na': False}]
cuda
Pipeline: IntToFloatTensor -- {'div': 255.0, 'div_mask': 1} -> Normalize -- {'mean': tensor([[[[0.4850]],
[[0.4560]],
[[0.4060]]]], device='cuda:0'), 'std': tensor([[[[0.2290]],
[[0.2240]],
[[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}
Pipeline: Resize -- {'size': (140, 140), 'method': 'crop', 'pad_mode': 'reflection', 'resamples': (<Resampling.BILINEAR: 2>, <Resampling.NEAREST: 0>), 'p': 1.0} -> AlbumentationsTransform -- {'train_aug': Compose([
RandomResizedCrop(always_apply=False, p=1.0, height=128, width=128, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1),
Transpose(always_apply=False, p=0.5),
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Pipeline: IntToFloatTensor -- {'div': 255.0, 'div_mask': 1} -> Normalize -- {'mean': tensor([[[[0.4850]],
[[0.4560]],
[[0.4060]]]], device='cuda:0'), 'std': tensor([[[[0.2290]],
[[0.2240]],
[[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}
Pipeline: Resize -- {'size': (224, 224), 'method': 'crop', 'pad_mode': 'reflection', 'resamples': (<Resampling.BILINEAR: 2>, <Resampling.NEAREST: 0>), 'p': 1.0} -> ToTensor
Pipeline:
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