Nexh98's workspace
Runs
39
Name
2 visualized
val_f1
Best VAL MacroF1
exp: FullData
exp: FullData
4
21
0.92442
0.92579
exp: Pretraining
exp: Pretraining
1
2
0.98527
0.98533
exp: Augmentations
exp: Augmentations
4
4
0.78708
0.78717
exp: Baseline
exp: Baseline
1
1
0.11815
0.15485
State
Notes
User
Tags
Created
Runtime
Sweep
augment.aug
augment.cache
augment.img_shape
augment.reverse
augment.use_bbox
comment
data_dir
debug
epochs
exp
lr
lr_scheduler.factor
lr_scheduler.patience
model
name
notebook
num_class
output_dir
train_batch
use_wandb
valid_batch
wandb.entity
wandb.project
augment.dilation
augment.mixup
augment.opening
augment.contour_cutout_number
augment.contour_cutout_prob
checkpoint_path
resume
all_data
Best VAL Accuracy
Best VAL Loss
epoch
lr
train_acc
train_f1
train_loss
val_acc
val_loss
Finished
nexh98
5d 17h 20m 22s
-
false
true
41.33333
true
[false,true]
["Model2(48x48)","Model3(48x48)","Model_Lenet(28x28)","Reverse"]
/kaggle/input/numta
false
35.19048
FullData
0.005381
0.7
2
[["Conv2D",[3,16,3,1,1]],["Conv2D",[16,32,3,1,1]],["Conv2D",[32,32,3,1,1]],["Flatten",[]],["Linear",[10]],["Linear",[512]],["Linear",[1024]],["MaxPool2D",[2,2]],["ReLU",[]],["Softmax",[]]]
["Model_Lenet(48x48) + Reverse + Dilation + BBox + lr=0.01 + CCutout","Pretrained Model3(48x48) + Reverse + Dilation + BBox","Pretrained Model3(48x48) Grayscale + Reverse + Dilation + BBox","Pretrained Model_Lenet(48x48) + Reverse + Dilation + BBox","Pretrained Psuedo Model3(48x48) + Reverse + Dilation + BBox + lr=0.002 + CCutout","Pretrained Psuedo Model3(48x48) + Reverse + Dilation + BBox + lr=0.01 + CCutout","Pretrained Psuedo Model_Lenet(48x48) + Reverse + Dilation + BBox + lr=0.002 + CCutout","Pretrained Psuedo Model_Lenet(48x48) + Reverse + Dilation + BBox + lr=0.01 + CCutout","Psuedo Model3(48x48) + Reverse + Dilation + BBox + lr=0.01 + CCutout","Psuedo Model_Lenet(48x48) + Reverse + Dilation + BBox + lr=0.01 + CCutout"]
["https://www.kaggle.com/code/nexh98/cse-472-offline4-bbox-model3-pseudo-p2/edit/run/118622946","https://www.kaggle.com/nexh98/cse-472-offline4-bbox-model2-pseudo/edit","https://www.kaggle.com/nexh98/cse-472-offline4-bbox-pret-model2-pseudo-lr/edit","https://www.kaggle.com/nexh98/cse-472-offline4-bbox-pret-model2-pseudo/edit","https://www.kaggle.com/nexh98/cse-472-offline4-bbox-pret-model3-pseudo-lr0-01/edit","https://www.kaggle.com/nexh98/cse-472-offline4-bbox-pret-model3-pseudo/edit","https://www.kaggle.com/nexh98/cse-472-offline4-cutout-model2-rev-dil-bbox/edit","https://www.kaggle.com/nexh98/cse-472-offline4-pret-model2-rev-dil-bbox/edit","https://www.kaggle.com/nexh98/cse-472-offline4-pretmodel3-rev-dil-bbox-gray/edit","https://www.kaggle.com/nexh98/cse-472-offline4-pretmodel3-rev-dil-bbox/edit"]
10
output
128
true
128
nexh98
cse472_cnn_scratch
true
0
false
5
0.5
["/kaggle/input/cse-472-offline4-bbox-model2-pseudo/CSE-472-Machine-Learning/Offline 4/output/best_model_E14.npy","/kaggle/input/cse-472-offline4-bbox-model3-pseudo-p2/CSE-472-Machine-Learning/Offline 4/output/best_model_E5.npy","/kaggle/input/cse-472-offline4-bbox-pret-model2-pseudo/CSE-472-Machine-Learning/Offline 4/output/best_model_E13.npy","/kaggle/input/cse-472-offline4-bbox-pret-model3-pseudo-lr0-01/CSE-472-Machine-Learning/Offline 4/output/best_model_E4.npy","/kaggle/input/cse-472-offline4-bbox-pret-model3-pseudo/CSE-472-Machine-Learning/Offline 4/output/best_model_E5.npy","/kaggle/input/cse-472-offline4-pretrain/CSE-472-Machine-Learning/Offline 4/output/best_model_E17.npy","/kaggle/input/cse472-pretrained/Pretrained_Model2_best_model_E4.npy","/kaggle/input/cse472-pretrained/Pretrained_Model3_best_model_E0.npy",false]
[false,true]
-
0.93172
0.22676
34.19048
0.0018299
0.93669
0.93407
0.20773
0.93047
0.22873
Finished
nexh98
10h 34m 56s
-
false
true
46
false
false
EMNIST
/kaggle/input/emnist
false
10.5
Pretraining
0.01
0.7
2
[["Conv2D",[1,16,3,1,1]],["Conv2D",[16,32,3,1,1]],["Conv2D",[32,32,3,1,1]],["Flatten",[]],["Linear",[10]],["Linear",[512]],["Linear",[1024]],["MaxPool2D",[2,2]],["ReLU",[]],["Softmax",[]]]
["Model1","ModelLenet"]
["https://www.kaggle.com/nexh98/cse-472-offline4-pretrain-model1/edit","https://www.kaggle.com/nexh98/cse-472-offline4-pretrain/edit"]
10
output
128
true
128
nexh98
cse472_cnn_scratch
false
0
false
-
-
-
-
-
0.98606
0.046069
9.5
0.00745
0.97181
0.97051
0.094756
0.98602
0.046069
Finished
nexh98
2d 17h 4m 38s
-
false
true
64
[false,true]
[false,true]
["BBox","BBox + Reverse","BBox + Reverse + Opening","Reverse"]
/kaggle/input/numta
false
22.5
Augmentations
0.001
0.7
2
[["Conv2D",[3,16,3,1,1]],["Conv2D",[16,32,3,1,1]],["Conv2D",[32,32,3,1,1]],["Flatten",[]],["Linear",[10]],["Linear",[512]],["Linear",[1024]],["MaxPool2D",[2,2]],["ReLU",[]],["Softmax",[]]]
["Model1-BBox","Model1-BBox-Reverse","Model1-BBox-Reverse-Opening","Model1-Reverse"]
["https://www.kaggle.com/nexh98/cse-472-offline4-bbox-exp/edit","https://www.kaggle.com/nexh98/cse-472-offline4-bbox-reverse-dilation/edit","https://www.kaggle.com/nexh98/cse-472-offline4-bbox-reverse/edit","https://www.kaggle.com/nexh98/cse-472-offline4-reverse/edit"]
10
output
128
true
128
nexh98
cse472_cnn_scratch
false
0
true
-
-
-
-
-
0.80279
0.65556
21.5
0.001
0.80677
0.80049
0.63624
0.80279
0.65556
Finished
nexh98
9h 15m 54s
-
false
true
64
false
false
Model1-64x64
/kaggle/input/numta
false
20
Baseline
0.001
0.7
2
[["Conv2D",[3,16,3,1,1]],["Conv2D",[16,32,3,1,1]],["Conv2D",[32,32,3,1,1]],["Flatten",[]],["Linear",[10]],["Linear",[512]],["Linear",[1024]],["MaxPool2D",[2,2]],["ReLU",[]],["Softmax",[]]]
Model1-Baseline
10
output
128
true
128
nexh98
cse472_cnn_scratch
-
-
-
-
-
-
-
-
0.21046
2.26295
19
0.00011765
0.18744
0.13943
2.26123
0.16312
2.26338
1-4
of 4