Select Git revision
train_results.txt
train_results.txt 8.59 KiB
Training with 7 classes: T2star: 25, T2w: 1156, FLAIRCE: 1126, FLAIR: 5950, T1w: 5881, OTHER: 211, T1wCE: 5947
Machine: Cremi/falcon core:24, CPU: i9-14900, 64Go, GPU: RTX 4060 (8Go)
fix parameters:
criterion_balanced = nn.CrossEntropyLoss(weight = class_weights_tensor)
optimizer_Adam = optim.Adam(model.parameters(), 1e-3)
scaler = torch.cuda.amp.GradScaler()
epochs = 30
BATCH_SIZE = 64
WORKERS = 8
MODEL_NAME = 'efficientnet_b0'
time: 33 min 25 sec
Best epoch: 26
validation accuracy: 0.9303
validation loss: 1.6010
test accuracy: 98.68%
balanced accuracy: 94.15%
size: 16MB
checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_efficientnet_b0_adam_amp_criterion_balanced
MODEL_NAME = 'resnet50'
time: 56 min 39 sec
Best epoch: 18 (epoch 22 fails to be loaded)
validation accuracy: 0.9237
validation loss: 1.6202
test accuracy: 98.10%
balanced accuracy: 94.37%
size: 91MB
checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_resnet50_adam_amp_criterion_balanced
MODEL_NAME = 'resnet18'
time: 21 min 24 sec
Best epoch: 25
validation accuracy: 0.9321
validation loss: 1.8383
test accuracy: 98.77%
balanced accuracy: 99.11%
size: 43MB
checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_resnet18_adam_amp_criterion_balanced
Inference:
- time on GPU: ms
- GPU memory use: bytes
- time on CPU: ms
MODEL_NAME = 'resnet18.a2_in1k'
time: 20 min 6 sec
Best epoch: 12
validation accuracy: 0.9317
validation loss: 1.2443
test accuracy: 98.77%
balanced accuracy: 99.18%
size: 43 MB
checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_resnet18.a2_in1k
Inference:
- time on GPU: ms
- GPU memory use: bytes
- time on CPU: ms
MODEL_NAME = 'resnet18.fb_ssl_yfcc100m_ft_in1k'
time: 20 min 4 sec
Best epoch: 22
validation accuracy: 0.9227
validation loss: 1.8298
test accuracy: 97.49%
balanced accuracy: 97.30%
size: 43MB
checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_resnet18.fb_ssl_yfcc100m_ft_in1k
Inference: - time on GPU: ms
- GPU memory use: bytes
- time on CPU: ms
MODEL_NAME = 'mobilenetv3_small_100.lamb_in1k'
time: 7 min 32 sec
Best epoch: 28
validation accuracy: 0.9206
validation loss: 1.8223
test accuracy: 97.01%
balanced accuracy: 97.25%
size: 6 MB
checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_mobilenetv3_small_100.lamb_in1k
Inference:
- time on GPU: ms
- GPU memory use: bytes
- time on CPU: ms
MODEL_NAME = 'mobilenetv4_conv_large.e500_r256_in1k'
time: 46 min 50 sec
Best epoch: 4
validation accuracy: 0.9216
validation loss: 1.0341
test accuracy: 97.27%
balanced accuracy: 97.44%
size: 121 MB
checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_mobilenetv4_conv_large.e500_r256_in1k
Inference:
- time on GPU: sec
- GPU memory use: KB
- time on CPU: sec
MODEL_NAME = 'mobilenetv4_hybrid_medium.e500_r224_in1k'
time: 30 min 3 sec
Best epoch: 7
validation accuracy: 0.9112
validation loss: 0.8010
test accuracy: 97.04%
balanced accuracy: 87.13%
size: 39 MB
checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_mobilenetv4_hybrid_medium.e500_r224_in1k
Inference:
- time on GPU: sec
- GPU memory use: KB
- time on CPU: sec
-------------------------------------------------------------------------------------------------------------
ADD Augmentation to train images:
MODEL_NAME = 'resnet18'
transforms.v2.RandomHorizontalFlip(p=0.5),
transforms.v2.RandomRotation(degrees=15),
transforms.v2.ColorJitter(contrast=0.3),
transforms.v2.GaussianBlur(7, sigma=2),
RandomResample(scale_factor=4),
time: ~20 min (I forgot to save)
Best epoch: 21
validation accuracy: 0.5566
validation loss: 4.0260
test accuracy: 57.37%
balanced accuracy: 42.34%
size: 43MB
checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_resnet18_augmentation1
note: poor results on validation and test sets
augmentation2: lower random contrast, blur and resample
MODEL_NAME = 'resnet18'
transforms.v2.RandomHorizontalFlip(p=0.5),
transforms.v2.RandomRotation(degrees=15),transforms.v2.ColorJitter(contrast=0.1),
transforms.v2.GaussianBlur(7, sigma=1),
RandomResample(scale_factor=2),
time: 20 min 44 sec
Best epoch: 19
validation accuracy: 0.8666
validation loss: 1.1987
test accuracy: 91.48%
balanced accuracy: 92.11%
size: 43MB
checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_resnet18_augmentation2
note: much better results, but only around 90%, this cen be the result of generalization, but there are distinct faulty cells in the confusion matrix
2 confusion nodes: t2 <-> t2*, t1<->flair -> can still be the problem of contrast manipulation
augmentation3: without contrast change, have higehr blur (from augmentation1) and same resample (augmentation2)
MODEL_NAME = 'resnet18'
transforms.v2.RandomHorizontalFlip(p=0.5),
transforms.v2.RandomRotation(degrees=15),
transforms.v2.GaussianBlur(7, sigma=2),
RandomResample(scale_factor=2),
transforms.ToTensor()
time: 20 min 36 sec
Best epoch: 3
validation accuracy: 0.5524
validation loss: 2.5870
test accuracy: 59.24%
balanced accuracy: 46.14%
size: 43MB
checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_resnet18_augmentation3
note: essentially no training, everithing is detected as t2* or t1
augmentation4: without contrast change, blur or resampling, just rotation and flip
MODEL_NAME = 'resnet18'
transforms.v2.RandomHorizontalFlip(p=0.5),
transforms.v2.RandomRotation(degrees=15),
transforms.ToTensor()
time: 20 min 22 sec
Best epoch: 24
validation accuracy: 0.9216
validation loss: 1.6285
test accuracy: 98.53%
balanced accuracy: 98.77%
size: 43MB
checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_resnet18_augmentation4
note: as expected: much better results, but the learning curves still show overfitting
augmentation using horizontal and vertical flips:
MODEL_NAME = 'resnet18_flips'
transforms.v2.RandomHorizontalFlip(p=0.5),
transforms.v2.RandomVerticalFlip(p=0.5),
transforms.ToTensor()
time: 20 min 16 sec
Best epoch: 22
validation accuracy: 0.9294
validation loss: 1.9463
test accuracy: 98.71%
balanced accuracy: 98.73%
size: 43MB
checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_resnet18_flips
augmentation using horizontal, vertical flips and 90 degrees random rotation:
MODEL_NAME = 'resnet18_flips_90'
transforms.v2.RandomHorizontalFlip(p=0.5),
transforms.v2.RandomVerticalFlip(p=0.5),
transforms.v2.RandomRotation(degrees=90, expand=True),
transforms.ToTensor()
time: 20 min 19 sec
Best epoch: 13
validation accuracy: 89.24%
validation loss: 1.8287 test accuracy: 93.27%
balanced accuracy: 95.00%
size: 43
checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_resnet18_flips_90
augmentation using horizontal, vertical flips and 180 degrees random rotation:
MODEL_NAME = 'resnet18_flips_180'
transforms.v2.RandomHorizontalFlip(p=0.5),
transforms.v2.RandomVerticalFlip(p=0.5),
transforms.v2.RandomRotation(degrees=180, expand=True),
transforms.ToTensor()
time: 20 min 12 sec
Best epoch: 22
validation accuracy: 1.7595
validation loss: 90.17%
test accuracy: 95.95%
balanced accuracy: 95.60%
size: 43
checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_resnet18_flips_180
augmentation using horizontal, vertical flips and 360 degrees random rotation:
MODEL_NAME = 'resnet18_flips_360'
transforms.v2.RandomHorizontalFlip(p=0.5),
transforms.v2.RandomVerticalFlip(p=0.5),
transforms.v2.RandomRotation(degrees=360, expand=True),
transforms.ToTensor()
time: 20 min 12 sec
Best epoch: 22
validation accuracy: 1.8767
validation loss: 90.58%
test accuracy: 95.13%
balanced accuracy: 95.60%
size: 43
checkpoints: /net/travail/bformanek/checkpoints/transfer_checkpoints_resnet18_flips_360