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-rw-r--r--results/efficientnet_b0_clahe=False/classification_report.txt13
-rw-r--r--results/efficientnet_b0_clahe=False/confusion_matrix.pngbin0 -> 77436 bytes
-rw-r--r--results/efficientnet_b0_clahe=False/training_curves.pngbin0 -> 78851 bytes
-rw-r--r--results/efficientnet_b0_clahe=True/classification_report.txt13
-rw-r--r--results/efficientnet_b0_clahe=True/confusion_matrix.pngbin0 -> 78612 bytes
-rw-r--r--results/efficientnet_b0_clahe=True/training_curves.pngbin0 -> 83377 bytes
-rw-r--r--results/old_output.txt375
-rw-r--r--results/resnet50_clahe=False/classification_report.txt13
-rw-r--r--results/resnet50_clahe=False/confusion_matrix.pngbin0 -> 79723 bytes
-rw-r--r--results/resnet50_clahe=False/training_curves.pngbin0 -> 79370 bytes
-rw-r--r--results/resnet50_clahe=True/classification_report.txt13
-rw-r--r--results/resnet50_clahe=True/confusion_matrix.pngbin0 -> 80274 bytes
-rw-r--r--results/resnet50_clahe=True/training_curves.pngbin0 -> 80405 bytes
-rw-r--r--results/vit_b_16_clahe=False/classification_report.txt13
-rw-r--r--results/vit_b_16_clahe=False/confusion_matrix.pngbin0 -> 77874 bytes
-rw-r--r--results/vit_b_16_clahe=False/training_curves.pngbin0 -> 98667 bytes
-rw-r--r--results/vit_b_16_clahe=True/classification_report.txt13
-rw-r--r--results/vit_b_16_clahe=True/confusion_matrix.pngbin0 -> 78314 bytes
-rw-r--r--results/vit_b_16_clahe=True/training_curves.pngbin0 -> 82953 bytes
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diff --git a/results/efficientnet_b0_clahe=False/classification_report.txt b/results/efficientnet_b0_clahe=False/classification_report.txt
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1Experiment: efficientnet_b0_clahe=False
2
3 precision recall f1-score support
4
5 Healthy (0) 0.85 0.95 0.90 3872
6 Mild NPDR (1) 0.25 0.06 0.10 366
7 Moderate NPDR (2) 0.62 0.53 0.57 794
8 Severe NPDR (3) 0.51 0.40 0.45 131
9Proliferative DR (4) 0.62 0.55 0.58 106
10
11 accuracy 0.80 5269
12 macro avg 0.57 0.50 0.52 5269
13 weighted avg 0.76 0.80 0.78 5269
diff --git a/results/efficientnet_b0_clahe=False/confusion_matrix.png b/results/efficientnet_b0_clahe=False/confusion_matrix.png
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diff --git a/results/efficientnet_b0_clahe=False/training_curves.png b/results/efficientnet_b0_clahe=False/training_curves.png
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diff --git a/results/efficientnet_b0_clahe=True/classification_report.txt b/results/efficientnet_b0_clahe=True/classification_report.txt
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1Experiment: efficientnet_b0_clahe=True
2
3 precision recall f1-score support
4
5 Healthy (0) 0.86 0.91 0.88 3872
6 Mild NPDR (1) 0.17 0.08 0.11 366
7 Moderate NPDR (2) 0.55 0.53 0.54 794
8 Severe NPDR (3) 0.40 0.52 0.45 131
9Proliferative DR (4) 0.62 0.58 0.60 106
10
11 accuracy 0.78 5269
12 macro avg 0.52 0.52 0.51 5269
13 weighted avg 0.75 0.78 0.76 5269
diff --git a/results/efficientnet_b0_clahe=True/confusion_matrix.png b/results/efficientnet_b0_clahe=True/confusion_matrix.png
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diff --git a/results/old_output.txt b/results/old_output.txt
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1Using device: cuda
2Downloading/locating dataset via kagglehub...
3Dataset root: /home/vin/.cache/kagglehub/datasets/amanneo/diabetic-retinopathy-resized-arranged/versions/1
4Dataset split — train: 24588, val: 5269, test: 5269
5
6======================================================================
7Experiment: resnet50 | CLAHE: False
8======================================================================
9 Using 2 GPUs via DataParallel
10/home/vin/src/public/eel4759_finalproject_classification/guix-root/lib/python3.10/site-packages/torch/nn/modules/linear.py:125: UserWarning: Attempting to use hipBLASLt on an unsupported architecture! Overriding blas backend to hipblas (Triggered internally at /tmp/guix-build-python-pytorch-rocm-2.5.1.drv-0/source/aten/src/ATen/Context.cpp:296.)
11 return F.linear(input, self.weight, self.bias)
12[resnet50_clahe=False] Epoch 1/50 | train loss: 1.0245 | val loss: 0.9964 | macro F1: 0.1852 | weighted F1: 0.3999 | lr: 1.00e-06
13[resnet50_clahe=False] Epoch 2/50 | train loss: 0.9314 | val loss: 0.8414 | macro F1: 0.2588 | weighted F1: 0.4671 | lr: 4.00e-06
14[resnet50_clahe=False] Epoch 3/50 | train loss: 0.7615 | val loss: 0.6517 | macro F1: 0.3182 | weighted F1: 0.4632 | lr: 7.00e-06
15/home/vin/src/public/eel4759_finalproject_classification/guix-root/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:240: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
16 warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
17[resnet50_clahe=False] Epoch 4/50 | train loss: 0.6579 | val loss: 0.5784 | macro F1: 0.3628 | weighted F1: 0.5279 | lr: 1.00e-05
18[resnet50_clahe=False] Epoch 5/50 | train loss: 0.6052 | val loss: 0.5553 | macro F1: 0.3466 | weighted F1: 0.5052 | lr: 9.99e-06
19[resnet50_clahe=False] Epoch 6/50 | train loss: 0.5523 | val loss: 0.5035 | macro F1: 0.3929 | weighted F1: 0.6145 | lr: 9.96e-06
20[resnet50_clahe=False] Epoch 7/50 | train loss: 0.5230 | val loss: 0.4800 | macro F1: 0.4087 | weighted F1: 0.5930 | lr: 9.90e-06
21[resnet50_clahe=False] Epoch 8/50 | train loss: 0.4867 | val loss: 0.5005 | macro F1: 0.3790 | weighted F1: 0.5139 | lr: 9.82e-06
22[resnet50_clahe=False] Epoch 9/50 | train loss: 0.4552 | val loss: 0.5179 | macro F1: 0.3835 | weighted F1: 0.5228 | lr: 9.72e-06
23[resnet50_clahe=False] Epoch 10/50 | train loss: 0.4253 | val loss: 0.4365 | macro F1: 0.4274 | weighted F1: 0.6266 | lr: 9.60e-06
24[resnet50_clahe=False] Epoch 11/50 | train loss: 0.4028 | val loss: 0.4509 | macro F1: 0.4160 | weighted F1: 0.6024 | lr: 9.46e-06
25[resnet50_clahe=False] Epoch 12/50 | train loss: 0.3771 | val loss: 0.4038 | macro F1: 0.4373 | weighted F1: 0.6812 | lr: 9.30e-06
26[resnet50_clahe=False] Epoch 13/50 | train loss: 0.3598 | val loss: 0.4183 | macro F1: 0.4330 | weighted F1: 0.6428 | lr: 9.12e-06
27[resnet50_clahe=False] Epoch 14/50 | train loss: 0.3373 | val loss: 0.4384 | macro F1: 0.4201 | weighted F1: 0.6056 | lr: 8.92e-06
28[resnet50_clahe=False] Epoch 15/50 | train loss: 0.3218 | val loss: 0.4118 | macro F1: 0.4360 | weighted F1: 0.6580 | lr: 8.71e-06
29[resnet50_clahe=False] Epoch 16/50 | train loss: 0.3145 | val loss: 0.4351 | macro F1: 0.4273 | weighted F1: 0.6133 | lr: 8.48e-06
30[resnet50_clahe=False] Epoch 17/50 | train loss: 0.2979 | val loss: 0.4389 | macro F1: 0.4241 | weighted F1: 0.6119 | lr: 8.23e-06
31[resnet50_clahe=False] Epoch 18/50 | train loss: 0.2895 | val loss: 0.4328 | macro F1: 0.4269 | weighted F1: 0.6206 | lr: 7.97e-06
32[resnet50_clahe=False] Epoch 19/50 | train loss: 0.2756 | val loss: 0.4400 | macro F1: 0.4357 | weighted F1: 0.6137 | lr: 7.69e-06
33[resnet50_clahe=False] Epoch 20/50 | train loss: 0.2712 | val loss: 0.4219 | macro F1: 0.4503 | weighted F1: 0.6445 | lr: 7.40e-06
34[resnet50_clahe=False] Epoch 21/50 | train loss: 0.2614 | val loss: 0.4592 | macro F1: 0.4380 | weighted F1: 0.5830 | lr: 7.10e-06
35[resnet50_clahe=False] Epoch 22/50 | train loss: 0.2565 | val loss: 0.4278 | macro F1: 0.4368 | weighted F1: 0.6325 | lr: 6.80e-06
36[resnet50_clahe=False] Epoch 23/50 | train loss: 0.2517 | val loss: 0.4127 | macro F1: 0.4541 | weighted F1: 0.6641 | lr: 6.48e-06
37[resnet50_clahe=False] Epoch 24/50 | train loss: 0.2415 | val loss: 0.4373 | macro F1: 0.4382 | weighted F1: 0.6198 | lr: 6.16e-06
38[resnet50_clahe=False] Epoch 25/50 | train loss: 0.2382 | val loss: 0.4130 | macro F1: 0.4585 | weighted F1: 0.6676 | lr: 5.83e-06
39[resnet50_clahe=False] Epoch 26/50 | train loss: 0.2363 | val loss: 0.4211 | macro F1: 0.4591 | weighted F1: 0.6563 | lr: 5.50e-06
40[resnet50_clahe=False] Epoch 27/50 | train loss: 0.2290 | val loss: 0.4322 | macro F1: 0.4538 | weighted F1: 0.6440 | lr: 5.17e-06
41[resnet50_clahe=False] Epoch 28/50 | train loss: 0.2263 | val loss: 0.4299 | macro F1: 0.4437 | weighted F1: 0.6414 | lr: 4.83e-06
42[resnet50_clahe=False] Epoch 29/50 | train loss: 0.2177 | val loss: 0.4171 | macro F1: 0.4536 | weighted F1: 0.6538 | lr: 4.50e-06
43[resnet50_clahe=False] Epoch 30/50 | train loss: 0.2174 | val loss: 0.4189 | macro F1: 0.4599 | weighted F1: 0.6671 | lr: 4.17e-06
44[resnet50_clahe=False] Epoch 31/50 | train loss: 0.2156 | val loss: 0.4180 | macro F1: 0.4590 | weighted F1: 0.6540 | lr: 3.84e-06
45[resnet50_clahe=False] Epoch 32/50 | train loss: 0.2094 | val loss: 0.4388 | macro F1: 0.4489 | weighted F1: 0.6309 | lr: 3.52e-06
46[resnet50_clahe=False] Epoch 33/50 | train loss: 0.2095 | val loss: 0.4170 | macro F1: 0.4544 | weighted F1: 0.6624 | lr: 3.20e-06
47[resnet50_clahe=False] Epoch 34/50 | train loss: 0.2047 | val loss: 0.4129 | macro F1: 0.4593 | weighted F1: 0.6698 | lr: 2.90e-06
48[resnet50_clahe=False] Epoch 35/50 | train loss: 0.1990 | val loss: 0.4372 | macro F1: 0.4538 | weighted F1: 0.6288 | lr: 2.60e-06
49[resnet50_clahe=False] Epoch 36/50 | train loss: 0.1997 | val loss: 0.4292 | macro F1: 0.4531 | weighted F1: 0.6472 | lr: 2.31e-06
50[resnet50_clahe=False] Epoch 37/50 | train loss: 0.2002 | val loss: 0.4450 | macro F1: 0.4547 | weighted F1: 0.6356 | lr: 2.03e-06
51[resnet50_clahe=False] Epoch 38/50 | train loss: 0.1953 | val loss: 0.4359 | macro F1: 0.4492 | weighted F1: 0.6448 | lr: 1.77e-06
52[resnet50_clahe=False] Epoch 39/50 | train loss: 0.1960 | val loss: 0.4283 | macro F1: 0.4617 | weighted F1: 0.6526 | lr: 1.52e-06
53[resnet50_clahe=False] Epoch 40/50 | train loss: 0.1910 | val loss: 0.4416 | macro F1: 0.4534 | weighted F1: 0.6405 | lr: 1.29e-06
54[resnet50_clahe=False] Epoch 41/50 | train loss: 0.1896 | val loss: 0.4199 | macro F1: 0.4626 | weighted F1: 0.6711 | lr: 1.08e-06
55[resnet50_clahe=False] Epoch 42/50 | train loss: 0.1915 | val loss: 0.4326 | macro F1: 0.4648 | weighted F1: 0.6557 | lr: 8.78e-07
56[resnet50_clahe=False] Epoch 43/50 | train loss: 0.1904 | val loss: 0.4213 | macro F1: 0.4547 | weighted F1: 0.6577 | lr: 6.98e-07
57[resnet50_clahe=False] Epoch 44/50 | train loss: 0.1883 | val loss: 0.4284 | macro F1: 0.4598 | weighted F1: 0.6512 | lr: 5.37e-07
58[resnet50_clahe=False] Epoch 45/50 | train loss: 0.1909 | val loss: 0.4330 | macro F1: 0.4561 | weighted F1: 0.6465 | lr: 3.97e-07
59[resnet50_clahe=False] Epoch 46/50 | train loss: 0.1902 | val loss: 0.4259 | macro F1: 0.4594 | weighted F1: 0.6519 | lr: 2.77e-07
60[resnet50_clahe=False] Epoch 47/50 | train loss: 0.1863 | val loss: 0.4299 | macro F1: 0.4550 | weighted F1: 0.6489 | lr: 1.78e-07
61[resnet50_clahe=False] Epoch 48/50 | train loss: 0.1882 | val loss: 0.4276 | macro F1: 0.4600 | weighted F1: 0.6555 | lr: 1.00e-07
62[resnet50_clahe=False] Epoch 49/50 | train loss: 0.1891 | val loss: 0.4210 | macro F1: 0.4625 | weighted F1: 0.6657 | lr: 4.46e-08
63[resnet50_clahe=False] Epoch 50/50 | train loss: 0.1895 | val loss: 0.4263 | macro F1: 0.4566 | weighted F1: 0.6599 | lr: 1.12e-08
64 Training complete: 50 epochs, 7514.0s, best val macro F1: 0.4648
65 Checkpoint saved: results/resnet50_clahe=False/best_model.pth
66
67 Test results — Weighted F1: 0.6570 | Macro F1: 0.4630 | Accuracy: 0.6111 | Kappa: 0.5341
68 precision recall f1-score support
69
70 Healthy (0) 0.86 0.67 0.75 3872
71 Mild NPDR (1) 0.11 0.35 0.17 366
72 Moderate NPDR (2) 0.46 0.50 0.48 794
73 Severe NPDR (3) 0.45 0.41 0.43 131
74Proliferative DR (4) 0.64 0.40 0.49 106
75
76 accuracy 0.61 5269
77 macro avg 0.50 0.47 0.46 5269
78 weighted avg 0.73 0.61 0.66 5269
79
80
81======================================================================
82Experiment: resnet50 | CLAHE: True
83======================================================================
84 Using 2 GPUs via DataParallel
85[resnet50_clahe=True] Epoch 1/50 | train loss: 1.0238 | val loss: 1.0225 | macro F1: 0.1611 | weighted F1: 0.3049 | lr: 1.00e-06
86[resnet50_clahe=True] Epoch 2/50 | train loss: 0.9321 | val loss: 0.8636 | macro F1: 0.2377 | weighted F1: 0.3381 | lr: 4.00e-06
87[resnet50_clahe=True] Epoch 3/50 | train loss: 0.7489 | val loss: 0.7394 | macro F1: 0.2726 | weighted F1: 0.2384 | lr: 7.00e-06
88/home/vin/src/public/eel4759_finalproject_classification/guix-root/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:240: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
89 warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
90[resnet50_clahe=True] Epoch 4/50 | train loss: 0.6605 | val loss: 0.6103 | macro F1: 0.3472 | weighted F1: 0.4773 | lr: 1.00e-05
91[resnet50_clahe=True] Epoch 5/50 | train loss: 0.6087 | val loss: 0.5574 | macro F1: 0.3812 | weighted F1: 0.5527 | lr: 9.99e-06
92[resnet50_clahe=True] Epoch 6/50 | train loss: 0.5717 | val loss: 0.6886 | macro F1: 0.3069 | weighted F1: 0.3767 | lr: 9.96e-06
93[resnet50_clahe=True] Epoch 7/50 | train loss: 0.5410 | val loss: 0.5472 | macro F1: 0.3861 | weighted F1: 0.5587 | lr: 9.90e-06
94[resnet50_clahe=True] Epoch 8/50 | train loss: 0.5153 | val loss: 0.6006 | macro F1: 0.3587 | weighted F1: 0.4748 | lr: 9.82e-06
95[resnet50_clahe=True] Epoch 9/50 | train loss: 0.4801 | val loss: 0.5778 | macro F1: 0.3511 | weighted F1: 0.4467 | lr: 9.72e-06
96[resnet50_clahe=True] Epoch 10/50 | train loss: 0.4605 | val loss: 0.6081 | macro F1: 0.3479 | weighted F1: 0.3552 | lr: 9.60e-06
97[resnet50_clahe=True] Epoch 11/50 | train loss: 0.4312 | val loss: 0.5329 | macro F1: 0.3830 | weighted F1: 0.5007 | lr: 9.46e-06
98[resnet50_clahe=True] Epoch 12/50 | train loss: 0.4050 | val loss: 0.6114 | macro F1: 0.3433 | weighted F1: 0.3942 | lr: 9.30e-06
99[resnet50_clahe=True] Epoch 13/50 | train loss: 0.3893 | val loss: 0.4973 | macro F1: 0.4002 | weighted F1: 0.5184 | lr: 9.12e-06
100[resnet50_clahe=True] Epoch 14/50 | train loss: 0.3720 | val loss: 0.5185 | macro F1: 0.3813 | weighted F1: 0.4926 | lr: 8.92e-06
101[resnet50_clahe=True] Epoch 15/50 | train loss: 0.3572 | val loss: 0.4602 | macro F1: 0.4253 | weighted F1: 0.6034 | lr: 8.71e-06
102[resnet50_clahe=True] Epoch 16/50 | train loss: 0.3412 | val loss: 0.4855 | macro F1: 0.4119 | weighted F1: 0.5678 | lr: 8.48e-06
103[resnet50_clahe=True] Epoch 17/50 | train loss: 0.3293 | val loss: 0.5191 | macro F1: 0.3913 | weighted F1: 0.4668 | lr: 8.23e-06
104[resnet50_clahe=True] Epoch 18/50 | train loss: 0.3160 | val loss: 0.4860 | macro F1: 0.4172 | weighted F1: 0.5307 | lr: 7.97e-06
105[resnet50_clahe=True] Epoch 19/50 | train loss: 0.3008 | val loss: 0.4435 | macro F1: 0.4492 | weighted F1: 0.6188 | lr: 7.69e-06
106[resnet50_clahe=True] Epoch 20/50 | train loss: 0.3014 | val loss: 0.4456 | macro F1: 0.4312 | weighted F1: 0.6130 | lr: 7.40e-06
107[resnet50_clahe=True] Epoch 21/50 | train loss: 0.2881 | val loss: 0.4873 | macro F1: 0.4171 | weighted F1: 0.5459 | lr: 7.10e-06
108[resnet50_clahe=True] Epoch 22/50 | train loss: 0.2866 | val loss: 0.4176 | macro F1: 0.4596 | weighted F1: 0.6512 | lr: 6.80e-06
109[resnet50_clahe=True] Epoch 23/50 | train loss: 0.2749 | val loss: 0.4252 | macro F1: 0.4611 | weighted F1: 0.6455 | lr: 6.48e-06
110[resnet50_clahe=True] Epoch 24/50 | train loss: 0.2686 | val loss: 0.4157 | macro F1: 0.4631 | weighted F1: 0.6507 | lr: 6.16e-06
111[resnet50_clahe=True] Epoch 25/50 | train loss: 0.2612 | val loss: 0.4571 | macro F1: 0.4375 | weighted F1: 0.5825 | lr: 5.83e-06
112[resnet50_clahe=True] Epoch 26/50 | train loss: 0.2605 | val loss: 0.4122 | macro F1: 0.4560 | weighted F1: 0.6547 | lr: 5.50e-06
113[resnet50_clahe=True] Epoch 27/50 | train loss: 0.2521 | val loss: 0.4362 | macro F1: 0.4559 | weighted F1: 0.6230 | lr: 5.17e-06
114[resnet50_clahe=True] Epoch 28/50 | train loss: 0.2487 | val loss: 0.4511 | macro F1: 0.4397 | weighted F1: 0.5744 | lr: 4.83e-06
115[resnet50_clahe=True] Epoch 29/50 | train loss: 0.2446 | val loss: 0.4332 | macro F1: 0.4599 | weighted F1: 0.6134 | lr: 4.50e-06
116[resnet50_clahe=True] Epoch 30/50 | train loss: 0.2440 | val loss: 0.4364 | macro F1: 0.4538 | weighted F1: 0.5992 | lr: 4.17e-06
117[resnet50_clahe=True] Epoch 31/50 | train loss: 0.2387 | val loss: 0.4493 | macro F1: 0.4453 | weighted F1: 0.5933 | lr: 3.84e-06
118[resnet50_clahe=True] Epoch 32/50 | train loss: 0.2329 | val loss: 0.4114 | macro F1: 0.4750 | weighted F1: 0.6623 | lr: 3.52e-06
119[resnet50_clahe=True] Epoch 33/50 | train loss: 0.2332 | val loss: 0.4253 | macro F1: 0.4582 | weighted F1: 0.6266 | lr: 3.20e-06
120[resnet50_clahe=True] Epoch 34/50 | train loss: 0.2295 | val loss: 0.4181 | macro F1: 0.4710 | weighted F1: 0.6431 | lr: 2.90e-06
121[resnet50_clahe=True] Epoch 35/50 | train loss: 0.2271 | val loss: 0.4429 | macro F1: 0.4485 | weighted F1: 0.6159 | lr: 2.60e-06
122[resnet50_clahe=True] Epoch 36/50 | train loss: 0.2248 | val loss: 0.4404 | macro F1: 0.4527 | weighted F1: 0.6060 | lr: 2.31e-06
123[resnet50_clahe=True] Epoch 37/50 | train loss: 0.2201 | val loss: 0.4329 | macro F1: 0.4633 | weighted F1: 0.6234 | lr: 2.03e-06
124[resnet50_clahe=True] Epoch 38/50 | train loss: 0.2219 | val loss: 0.4181 | macro F1: 0.4654 | weighted F1: 0.6392 | lr: 1.77e-06
125[resnet50_clahe=True] Epoch 39/50 | train loss: 0.2201 | val loss: 0.4433 | macro F1: 0.4566 | weighted F1: 0.6084 | lr: 1.52e-06
126[resnet50_clahe=True] Epoch 40/50 | train loss: 0.2203 | val loss: 0.4200 | macro F1: 0.4683 | weighted F1: 0.6455 | lr: 1.29e-06
127[resnet50_clahe=True] Epoch 41/50 | train loss: 0.2174 | val loss: 0.4340 | macro F1: 0.4613 | weighted F1: 0.6123 | lr: 1.08e-06
128[resnet50_clahe=True] Epoch 42/50 | train loss: 0.2191 | val loss: 0.4246 | macro F1: 0.4619 | weighted F1: 0.6323 | lr: 8.78e-07
129 Early stopping at epoch 42 (no improvement for 10 epochs).
130 Training complete: 42 epochs, 7428.3s, best val macro F1: 0.4750
131 Checkpoint saved: results/resnet50_clahe=True/best_model.pth
132
133 Test results — Weighted F1: 0.6473 | Macro F1: 0.4426 | Accuracy: 0.6058 | Kappa: 0.5234
134 precision recall f1-score support
135
136 Healthy (0) 0.86 0.66 0.75 3872
137 Mild NPDR (1) 0.12 0.32 0.17 366
138 Moderate NPDR (2) 0.39 0.52 0.45 794
139 Severe NPDR (3) 0.35 0.33 0.34 131
140Proliferative DR (4) 0.46 0.56 0.50 106
141
142 accuracy 0.61 5269
143 macro avg 0.44 0.48 0.44 5269
144 weighted avg 0.72 0.61 0.65 5269
145
146
147======================================================================
148Experiment: efficientnet_b0 | CLAHE: False
149======================================================================
150 Using 2 GPUs via DataParallel
151[efficientnet_b0_clahe=False] Epoch 1/50 | train loss: 1.0094 | val loss: 1.0305 | macro F1: 0.1622 | weighted F1: 0.2599 | lr: 1.00e-06
152[efficientnet_b0_clahe=False] Epoch 2/50 | train loss: 0.9135 | val loss: 0.8390 | macro F1: 0.2462 | weighted F1: 0.3625 | lr: 4.00e-06
153[efficientnet_b0_clahe=False] Epoch 3/50 | train loss: 0.7802 | val loss: 0.6796 | macro F1: 0.3085 | weighted F1: 0.4454 | lr: 7.00e-06
154/home/vin/src/public/eel4759_finalproject_classification/guix-root/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:240: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
155 warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
156[efficientnet_b0_clahe=False] Epoch 4/50 | train loss: 0.6916 | val loss: 0.6145 | macro F1: 0.3412 | weighted F1: 0.4696 | lr: 1.00e-05
157[efficientnet_b0_clahe=False] Epoch 5/50 | train loss: 0.6479 | val loss: 0.6016 | macro F1: 0.3530 | weighted F1: 0.4816 | lr: 9.99e-06
158[efficientnet_b0_clahe=False] Epoch 6/50 | train loss: 0.6232 | val loss: 0.5439 | macro F1: 0.3814 | weighted F1: 0.5468 | lr: 9.96e-06
159[efficientnet_b0_clahe=False] Epoch 7/50 | train loss: 0.6024 | val loss: 0.5210 | macro F1: 0.3909 | weighted F1: 0.5471 | lr: 9.90e-06
160[efficientnet_b0_clahe=False] Epoch 8/50 | train loss: 0.5788 | val loss: 0.5330 | macro F1: 0.3936 | weighted F1: 0.5652 | lr: 9.82e-06
161[efficientnet_b0_clahe=False] Epoch 9/50 | train loss: 0.5693 | val loss: 0.4784 | macro F1: 0.4241 | weighted F1: 0.6237 | lr: 9.72e-06
162[efficientnet_b0_clahe=False] Epoch 10/50 | train loss: 0.5533 | val loss: 0.4836 | macro F1: 0.4168 | weighted F1: 0.6054 | lr: 9.60e-06
163[efficientnet_b0_clahe=False] Epoch 11/50 | train loss: 0.5360 | val loss: 0.4841 | macro F1: 0.4225 | weighted F1: 0.6004 | lr: 9.46e-06
164[efficientnet_b0_clahe=False] Epoch 12/50 | train loss: 0.5262 | val loss: 0.4864 | macro F1: 0.4240 | weighted F1: 0.5978 | lr: 9.30e-06
165[efficientnet_b0_clahe=False] Epoch 13/50 | train loss: 0.5072 | val loss: 0.4667 | macro F1: 0.4395 | weighted F1: 0.6369 | lr: 9.12e-06
166[efficientnet_b0_clahe=False] Epoch 14/50 | train loss: 0.4993 | val loss: 0.4608 | macro F1: 0.4295 | weighted F1: 0.5932 | lr: 8.92e-06
167[efficientnet_b0_clahe=False] Epoch 15/50 | train loss: 0.4913 | val loss: 0.4630 | macro F1: 0.4375 | weighted F1: 0.6177 | lr: 8.71e-06
168[efficientnet_b0_clahe=False] Epoch 16/50 | train loss: 0.4824 | val loss: 0.4439 | macro F1: 0.4395 | weighted F1: 0.6329 | lr: 8.48e-06
169[efficientnet_b0_clahe=False] Epoch 17/50 | train loss: 0.4761 | val loss: 0.4512 | macro F1: 0.4469 | weighted F1: 0.6369 | lr: 8.23e-06
170[efficientnet_b0_clahe=False] Epoch 18/50 | train loss: 0.4608 | val loss: 0.4420 | macro F1: 0.4512 | weighted F1: 0.6506 | lr: 7.97e-06
171[efficientnet_b0_clahe=False] Epoch 19/50 | train loss: 0.4509 | val loss: 0.4284 | macro F1: 0.4489 | weighted F1: 0.6558 | lr: 7.69e-06
172[efficientnet_b0_clahe=False] Epoch 20/50 | train loss: 0.4410 | val loss: 0.4232 | macro F1: 0.4586 | weighted F1: 0.6566 | lr: 7.40e-06
173[efficientnet_b0_clahe=False] Epoch 21/50 | train loss: 0.4339 | val loss: 0.4292 | macro F1: 0.4498 | weighted F1: 0.6438 | lr: 7.10e-06
174[efficientnet_b0_clahe=False] Epoch 22/50 | train loss: 0.4260 | val loss: 0.4554 | macro F1: 0.4252 | weighted F1: 0.6216 | lr: 6.80e-06
175[efficientnet_b0_clahe=False] Epoch 23/50 | train loss: 0.4163 | val loss: 0.4584 | macro F1: 0.4267 | weighted F1: 0.5894 | lr: 6.48e-06
176[efficientnet_b0_clahe=False] Epoch 24/50 | train loss: 0.4194 | val loss: 0.4317 | macro F1: 0.4393 | weighted F1: 0.6219 | lr: 6.16e-06
177[efficientnet_b0_clahe=False] Epoch 25/50 | train loss: 0.4121 | val loss: 0.4469 | macro F1: 0.4260 | weighted F1: 0.6084 | lr: 5.83e-06
178[efficientnet_b0_clahe=False] Epoch 26/50 | train loss: 0.4032 | val loss: 0.4448 | macro F1: 0.4315 | weighted F1: 0.6125 | lr: 5.50e-06
179[efficientnet_b0_clahe=False] Epoch 27/50 | train loss: 0.3985 | val loss: 0.4556 | macro F1: 0.4163 | weighted F1: 0.5744 | lr: 5.17e-06
180[efficientnet_b0_clahe=False] Epoch 28/50 | train loss: 0.4012 | val loss: 0.4414 | macro F1: 0.4369 | weighted F1: 0.6114 | lr: 4.83e-06
181[efficientnet_b0_clahe=False] Epoch 29/50 | train loss: 0.3901 | val loss: 0.4544 | macro F1: 0.4269 | weighted F1: 0.5848 | lr: 4.50e-06
182[efficientnet_b0_clahe=False] Epoch 30/50 | train loss: 0.3827 | val loss: 0.4198 | macro F1: 0.4493 | weighted F1: 0.6541 | lr: 4.17e-06
183 Early stopping at epoch 30 (no improvement for 10 epochs).
184 Training complete: 30 epochs, 4417.0s, best val macro F1: 0.4586
185 Checkpoint saved: results/efficientnet_b0_clahe=False/best_model.pth
186
187 Test results — Weighted F1: 0.6544 | Macro F1: 0.4565 | Accuracy: 0.6092 | Kappa: 0.5508
188 precision recall f1-score support
189
190 Healthy (0) 0.86 0.68 0.76 3872
191 Mild NPDR (1) 0.12 0.38 0.18 366
192 Moderate NPDR (2) 0.43 0.44 0.43 794
193 Severe NPDR (3) 0.37 0.40 0.39 131
194Proliferative DR (4) 0.50 0.55 0.52 106
195
196 accuracy 0.61 5269
197 macro avg 0.46 0.49 0.46 5269
198 weighted avg 0.73 0.61 0.65 5269
199
200
201======================================================================
202Experiment: efficientnet_b0 | CLAHE: True
203======================================================================
204 Using 2 GPUs via DataParallel
205[efficientnet_b0_clahe=True] Epoch 1/50 | train loss: 1.0340 | val loss: 0.9535 | macro F1: 0.1977 | weighted F1: 0.4595 | lr: 1.00e-06
206[efficientnet_b0_clahe=True] Epoch 2/50 | train loss: 0.9357 | val loss: 0.8644 | macro F1: 0.2540 | weighted F1: 0.4719 | lr: 4.00e-06
207[efficientnet_b0_clahe=True] Epoch 3/50 | train loss: 0.8071 | val loss: 0.7581 | macro F1: 0.2782 | weighted F1: 0.3761 | lr: 7.00e-06
208/home/vin/src/public/eel4759_finalproject_classification/guix-root/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:240: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
209 warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
210[efficientnet_b0_clahe=True] Epoch 4/50 | train loss: 0.7152 | val loss: 0.7045 | macro F1: 0.3216 | weighted F1: 0.4750 | lr: 1.00e-05
211[efficientnet_b0_clahe=True] Epoch 5/50 | train loss: 0.6679 | val loss: 0.6061 | macro F1: 0.3530 | weighted F1: 0.5111 | lr: 9.99e-06
212[efficientnet_b0_clahe=True] Epoch 6/50 | train loss: 0.6345 | val loss: 0.5824 | macro F1: 0.3795 | weighted F1: 0.5610 | lr: 9.96e-06
213[efficientnet_b0_clahe=True] Epoch 7/50 | train loss: 0.6218 | val loss: 0.6082 | macro F1: 0.3673 | weighted F1: 0.5309 | lr: 9.90e-06
214[efficientnet_b0_clahe=True] Epoch 8/50 | train loss: 0.5995 | val loss: 0.5310 | macro F1: 0.4200 | weighted F1: 0.6034 | lr: 9.82e-06
215[efficientnet_b0_clahe=True] Epoch 9/50 | train loss: 0.5713 | val loss: 0.5494 | macro F1: 0.3984 | weighted F1: 0.5903 | lr: 9.72e-06
216[efficientnet_b0_clahe=True] Epoch 10/50 | train loss: 0.5695 | val loss: 0.5148 | macro F1: 0.4143 | weighted F1: 0.5797 | lr: 9.60e-06
217[efficientnet_b0_clahe=True] Epoch 11/50 | train loss: 0.5582 | val loss: 0.5137 | macro F1: 0.4164 | weighted F1: 0.6019 | lr: 9.46e-06
218[efficientnet_b0_clahe=True] Epoch 12/50 | train loss: 0.5420 | val loss: 0.4829 | macro F1: 0.4390 | weighted F1: 0.6368 | lr: 9.30e-06
219[efficientnet_b0_clahe=True] Epoch 13/50 | train loss: 0.5272 | val loss: 0.5464 | macro F1: 0.4034 | weighted F1: 0.5600 | lr: 9.12e-06
220[efficientnet_b0_clahe=True] Epoch 14/50 | train loss: 0.5217 | val loss: 0.5122 | macro F1: 0.4191 | weighted F1: 0.5894 | lr: 8.92e-06
221[efficientnet_b0_clahe=True] Epoch 15/50 | train loss: 0.5070 | val loss: 0.4664 | macro F1: 0.4436 | weighted F1: 0.6234 | lr: 8.71e-06
222[efficientnet_b0_clahe=True] Epoch 16/50 | train loss: 0.5059 | val loss: 0.5205 | macro F1: 0.4144 | weighted F1: 0.5574 | lr: 8.48e-06
223[efficientnet_b0_clahe=True] Epoch 17/50 | train loss: 0.4872 | val loss: 0.4746 | macro F1: 0.4323 | weighted F1: 0.6283 | lr: 8.23e-06
224[efficientnet_b0_clahe=True] Epoch 18/50 | train loss: 0.4801 | val loss: 0.4534 | macro F1: 0.4345 | weighted F1: 0.6202 | lr: 7.97e-06
225[efficientnet_b0_clahe=True] Epoch 19/50 | train loss: 0.4659 | val loss: 0.4260 | macro F1: 0.4591 | weighted F1: 0.6698 | lr: 7.69e-06
226[efficientnet_b0_clahe=True] Epoch 20/50 | train loss: 0.4631 | val loss: 0.4721 | macro F1: 0.4343 | weighted F1: 0.6318 | lr: 7.40e-06
227[efficientnet_b0_clahe=True] Epoch 21/50 | train loss: 0.4559 | val loss: 0.4620 | macro F1: 0.4281 | weighted F1: 0.6117 | lr: 7.10e-06
228[efficientnet_b0_clahe=True] Epoch 22/50 | train loss: 0.4491 | val loss: 0.4908 | macro F1: 0.4294 | weighted F1: 0.6191 | lr: 6.80e-06
229[efficientnet_b0_clahe=True] Epoch 23/50 | train loss: 0.4461 | val loss: 0.4441 | macro F1: 0.4530 | weighted F1: 0.6423 | lr: 6.48e-06
230[efficientnet_b0_clahe=True] Epoch 24/50 | train loss: 0.4359 | val loss: 0.4833 | macro F1: 0.4273 | weighted F1: 0.6094 | lr: 6.16e-06
231[efficientnet_b0_clahe=True] Epoch 25/50 | train loss: 0.4335 | val loss: 0.4640 | macro F1: 0.4372 | weighted F1: 0.6126 | lr: 5.83e-06
232[efficientnet_b0_clahe=True] Epoch 26/50 | train loss: 0.4239 | val loss: 0.4630 | macro F1: 0.4415 | weighted F1: 0.6327 | lr: 5.50e-06
233[efficientnet_b0_clahe=True] Epoch 27/50 | train loss: 0.4233 | val loss: 0.4814 | macro F1: 0.4333 | weighted F1: 0.5907 | lr: 5.17e-06
234[efficientnet_b0_clahe=True] Epoch 28/50 | train loss: 0.4195 | val loss: 0.4707 | macro F1: 0.4348 | weighted F1: 0.6253 | lr: 4.83e-06
235[efficientnet_b0_clahe=True] Epoch 29/50 | train loss: 0.4106 | val loss: 0.4524 | macro F1: 0.4444 | weighted F1: 0.6188 | lr: 4.50e-06
236 Early stopping at epoch 29 (no improvement for 10 epochs).
237 Training complete: 29 epochs, 5039.9s, best val macro F1: 0.4591
238 Checkpoint saved: results/efficientnet_b0_clahe=True/best_model.pth
239
240 Test results — Weighted F1: 0.6583 | Macro F1: 0.4510 | Accuracy: 0.6225 | Kappa: 0.5388
241 precision recall f1-score support
242
243 Healthy (0) 0.85 0.71 0.77 3872
244 Mild NPDR (1) 0.11 0.31 0.17 366
245 Moderate NPDR (2) 0.41 0.36 0.38 794
246 Severe NPDR (3) 0.35 0.48 0.41 131
247Proliferative DR (4) 0.46 0.62 0.53 106
248
249 accuracy 0.62 5269
250 macro avg 0.44 0.50 0.45 5269
251 weighted avg 0.71 0.62 0.66 5269
252
253
254======================================================================
255Experiment: vit_b_16 | CLAHE: False
256======================================================================
257 Using 2 GPUs via DataParallel
258/home/vin/src/public/eel4759_finalproject_classification/guix-root/lib/python3.10/site-packages/torch/nn/functional.py:6278: UserWarning: 1Torch was not compiled with memory efficient attention. (Triggered internally at /tmp/guix-build-python-pytorch-rocm-2.5.1.drv-0/source/aten/src/ATen/native/transformers/hip/sdp_utils.cpp:655.)
259 attn_output = scaled_dot_product_attention(
260[vit_b_16_clahe=False] Epoch 1/50 | train loss: 0.8742 | val loss: 0.6483 | macro F1: 0.3378 | weighted F1: 0.5137 | lr: 1.00e-06
261[vit_b_16_clahe=False] Epoch 2/50 | train loss: 0.6200 | val loss: 0.5276 | macro F1: 0.4139 | weighted F1: 0.6270 | lr: 4.00e-06
262[vit_b_16_clahe=False] Epoch 3/50 | train loss: 0.5423 | val loss: 0.4780 | macro F1: 0.4336 | weighted F1: 0.6650 | lr: 7.00e-06
263/home/vin/src/public/eel4759_finalproject_classification/guix-root/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:240: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
264 warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
265[vit_b_16_clahe=False] Epoch 4/50 | train loss: 0.4824 | val loss: 0.4713 | macro F1: 0.4397 | weighted F1: 0.6484 | lr: 1.00e-05
266[vit_b_16_clahe=False] Epoch 5/50 | train loss: 0.3943 | val loss: 0.5388 | macro F1: 0.4058 | weighted F1: 0.5305 | lr: 9.99e-06
267[vit_b_16_clahe=False] Epoch 6/50 | train loss: 0.3380 | val loss: 0.4502 | macro F1: 0.4414 | weighted F1: 0.6493 | lr: 9.96e-06
268[vit_b_16_clahe=False] Epoch 7/50 | train loss: 0.2967 | val loss: 0.5407 | macro F1: 0.4048 | weighted F1: 0.5500 | lr: 9.90e-06
269[vit_b_16_clahe=False] Epoch 8/50 | train loss: 0.2683 | val loss: 0.4669 | macro F1: 0.4255 | weighted F1: 0.6103 | lr: 9.82e-06
270[vit_b_16_clahe=False] Epoch 9/50 | train loss: 0.2491 | val loss: 0.5058 | macro F1: 0.4202 | weighted F1: 0.5681 | lr: 9.72e-06
271[vit_b_16_clahe=False] Epoch 10/50 | train loss: 0.2314 | val loss: 0.5206 | macro F1: 0.3947 | weighted F1: 0.5672 | lr: 9.60e-06
272[vit_b_16_clahe=False] Epoch 11/50 | train loss: 0.2123 | val loss: 0.4093 | macro F1: 0.4600 | weighted F1: 0.6873 | lr: 9.46e-06
273[vit_b_16_clahe=False] Epoch 12/50 | train loss: 0.1984 | val loss: 0.4557 | macro F1: 0.4462 | weighted F1: 0.6466 | lr: 9.30e-06
274[vit_b_16_clahe=False] Epoch 13/50 | train loss: 0.1796 | val loss: 0.5881 | macro F1: 0.3977 | weighted F1: 0.4722 | lr: 9.12e-06
275[vit_b_16_clahe=False] Epoch 14/50 | train loss: 0.1653 | val loss: 0.5450 | macro F1: 0.4196 | weighted F1: 0.5442 | lr: 8.92e-06
276[vit_b_16_clahe=False] Epoch 15/50 | train loss: 0.1543 | val loss: 0.4631 | macro F1: 0.4565 | weighted F1: 0.6523 | lr: 8.71e-06
277[vit_b_16_clahe=False] Epoch 16/50 | train loss: 0.1406 | val loss: 0.4707 | macro F1: 0.4367 | weighted F1: 0.6526 | lr: 8.48e-06
278[vit_b_16_clahe=False] Epoch 17/50 | train loss: 0.1301 | val loss: 0.4762 | macro F1: 0.4604 | weighted F1: 0.6645 | lr: 8.23e-06
279[vit_b_16_clahe=False] Epoch 18/50 | train loss: 0.1178 | val loss: 0.4418 | macro F1: 0.4740 | weighted F1: 0.7078 | lr: 7.97e-06
280[vit_b_16_clahe=False] Epoch 19/50 | train loss: 0.1070 | val loss: 0.5175 | macro F1: 0.4431 | weighted F1: 0.6389 | lr: 7.69e-06
281[vit_b_16_clahe=False] Epoch 20/50 | train loss: 0.0997 | val loss: 0.4831 | macro F1: 0.4548 | weighted F1: 0.7043 | lr: 7.40e-06
282[vit_b_16_clahe=False] Epoch 21/50 | train loss: 0.0932 | val loss: 0.5100 | macro F1: 0.4572 | weighted F1: 0.6734 | lr: 7.10e-06
283[vit_b_16_clahe=False] Epoch 22/50 | train loss: 0.0820 | val loss: 0.5025 | macro F1: 0.4599 | weighted F1: 0.6818 | lr: 6.80e-06
284[vit_b_16_clahe=False] Epoch 23/50 | train loss: 0.0732 | val loss: 0.5247 | macro F1: 0.4565 | weighted F1: 0.6924 | lr: 6.48e-06
285[vit_b_16_clahe=False] Epoch 24/50 | train loss: 0.0690 | val loss: 0.5487 | macro F1: 0.4732 | weighted F1: 0.7083 | lr: 6.16e-06
286[vit_b_16_clahe=False] Epoch 25/50 | train loss: 0.0631 | val loss: 0.5392 | macro F1: 0.4704 | weighted F1: 0.7079 | lr: 5.83e-06
287[vit_b_16_clahe=False] Epoch 26/50 | train loss: 0.0585 | val loss: 0.5939 | macro F1: 0.4463 | weighted F1: 0.6931 | lr: 5.50e-06
288[vit_b_16_clahe=False] Epoch 27/50 | train loss: 0.0534 | val loss: 0.5767 | macro F1: 0.4859 | weighted F1: 0.7079 | lr: 5.17e-06
289[vit_b_16_clahe=False] Epoch 28/50 | train loss: 0.0489 | val loss: 0.5820 | macro F1: 0.4646 | weighted F1: 0.7143 | lr: 4.83e-06
290[vit_b_16_clahe=False] Epoch 29/50 | train loss: 0.0472 | val loss: 0.5762 | macro F1: 0.4666 | weighted F1: 0.7230 | lr: 4.50e-06
291[vit_b_16_clahe=False] Epoch 30/50 | train loss: 0.0435 | val loss: 0.6125 | macro F1: 0.4653 | weighted F1: 0.7279 | lr: 4.17e-06
292[vit_b_16_clahe=False] Epoch 31/50 | train loss: 0.0415 | val loss: 0.6332 | macro F1: 0.4475 | weighted F1: 0.7150 | lr: 3.84e-06
293[vit_b_16_clahe=False] Epoch 32/50 | train loss: 0.0366 | val loss: 0.6243 | macro F1: 0.4765 | weighted F1: 0.7230 | lr: 3.52e-06
294[vit_b_16_clahe=False] Epoch 33/50 | train loss: 0.0338 | val loss: 0.6617 | macro F1: 0.4562 | weighted F1: 0.7240 | lr: 3.20e-06
295[vit_b_16_clahe=False] Epoch 34/50 | train loss: 0.0330 | val loss: 0.6543 | macro F1: 0.4573 | weighted F1: 0.7261 | lr: 2.90e-06
296[vit_b_16_clahe=False] Epoch 35/50 | train loss: 0.0306 | val loss: 0.6551 | macro F1: 0.4678 | weighted F1: 0.7145 | lr: 2.60e-06
297[vit_b_16_clahe=False] Epoch 36/50 | train loss: 0.0278 | val loss: 0.6882 | macro F1: 0.4576 | weighted F1: 0.7208 | lr: 2.31e-06
298[vit_b_16_clahe=False] Epoch 37/50 | train loss: 0.0263 | val loss: 0.6960 | macro F1: 0.4667 | weighted F1: 0.7268 | lr: 2.03e-06
299 Early stopping at epoch 37 (no improvement for 10 epochs).
300 Training complete: 37 epochs, 5571.3s, best val macro F1: 0.4859
301 Checkpoint saved: results/vit_b_16_clahe=False/best_model.pth
302
303 Test results — Weighted F1: 0.6913 | Macro F1: 0.4654 | Accuracy: 0.6800 | Kappa: 0.5326
304 precision recall f1-score support
305
306 Healthy (0) 0.83 0.79 0.81 3872
307 Mild NPDR (1) 0.12 0.17 0.14 366
308 Moderate NPDR (2) 0.41 0.46 0.43 794
309 Severe NPDR (3) 0.50 0.37 0.43 131
310Proliferative DR (4) 0.63 0.43 0.51 106
311
312 accuracy 0.68 5269
313 macro avg 0.50 0.45 0.47 5269
314 weighted avg 0.71 0.68 0.69 5269
315
316
317======================================================================
318Experiment: vit_b_16 | CLAHE: True
319======================================================================
320 Using 2 GPUs via DataParallel
321[vit_b_16_clahe=True] Epoch 1/50 | train loss: 0.8458 | val loss: 0.6484 | macro F1: 0.3372 | weighted F1: 0.5862 | lr: 1.00e-06
322[vit_b_16_clahe=True] Epoch 2/50 | train loss: 0.6195 | val loss: 0.5034 | macro F1: 0.4192 | weighted F1: 0.6381 | lr: 4.00e-06
323[vit_b_16_clahe=True] Epoch 3/50 | train loss: 0.5527 | val loss: 0.4780 | macro F1: 0.4058 | weighted F1: 0.5776 | lr: 7.00e-06
324/home/vin/src/public/eel4759_finalproject_classification/guix-root/lib/python3.10/site-packages/torch/optim/lr_scheduler.py:240: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
325 warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
326[vit_b_16_clahe=True] Epoch 4/50 | train loss: 0.4705 | val loss: 0.5859 | macro F1: 0.3382 | weighted F1: 0.3211 | lr: 1.00e-05
327[vit_b_16_clahe=True] Epoch 5/50 | train loss: 0.3984 | val loss: 0.4626 | macro F1: 0.4063 | weighted F1: 0.5911 | lr: 9.99e-06
328[vit_b_16_clahe=True] Epoch 6/50 | train loss: 0.3541 | val loss: 0.5024 | macro F1: 0.4102 | weighted F1: 0.5132 | lr: 9.96e-06
329[vit_b_16_clahe=True] Epoch 7/50 | train loss: 0.3034 | val loss: 0.5261 | macro F1: 0.3908 | weighted F1: 0.5489 | lr: 9.90e-06
330[vit_b_16_clahe=True] Epoch 8/50 | train loss: 0.2757 | val loss: 0.5225 | macro F1: 0.4164 | weighted F1: 0.5205 | lr: 9.82e-06
331[vit_b_16_clahe=True] Epoch 9/50 | train loss: 0.2469 | val loss: 0.5351 | macro F1: 0.4040 | weighted F1: 0.5129 | lr: 9.72e-06
332[vit_b_16_clahe=True] Epoch 10/50 | train loss: 0.2217 | val loss: 0.4544 | macro F1: 0.4237 | weighted F1: 0.6428 | lr: 9.60e-06
333[vit_b_16_clahe=True] Epoch 11/50 | train loss: 0.2108 | val loss: 0.4344 | macro F1: 0.4614 | weighted F1: 0.6458 | lr: 9.46e-06
334[vit_b_16_clahe=True] Epoch 12/50 | train loss: 0.1908 | val loss: 0.3916 | macro F1: 0.4673 | weighted F1: 0.6911 | lr: 9.30e-06
335[vit_b_16_clahe=True] Epoch 13/50 | train loss: 0.1738 | val loss: 0.4206 | macro F1: 0.4557 | weighted F1: 0.6754 | lr: 9.12e-06
336[vit_b_16_clahe=True] Epoch 14/50 | train loss: 0.1640 | val loss: 0.5151 | macro F1: 0.4403 | weighted F1: 0.5692 | lr: 8.92e-06
337[vit_b_16_clahe=True] Epoch 15/50 | train loss: 0.1422 | val loss: 0.4217 | macro F1: 0.4604 | weighted F1: 0.6939 | lr: 8.71e-06
338[vit_b_16_clahe=True] Epoch 16/50 | train loss: 0.1299 | val loss: 0.5239 | macro F1: 0.4394 | weighted F1: 0.5880 | lr: 8.48e-06
339[vit_b_16_clahe=True] Epoch 17/50 | train loss: 0.1205 | val loss: 0.4722 | macro F1: 0.4480 | weighted F1: 0.6693 | lr: 8.23e-06
340[vit_b_16_clahe=True] Epoch 18/50 | train loss: 0.1061 | val loss: 0.4824 | macro F1: 0.4514 | weighted F1: 0.6643 | lr: 7.97e-06
341[vit_b_16_clahe=True] Epoch 19/50 | train loss: 0.0960 | val loss: 0.4572 | macro F1: 0.4621 | weighted F1: 0.6980 | lr: 7.69e-06
342[vit_b_16_clahe=True] Epoch 20/50 | train loss: 0.0899 | val loss: 0.4783 | macro F1: 0.4621 | weighted F1: 0.6845 | lr: 7.40e-06
343[vit_b_16_clahe=True] Epoch 21/50 | train loss: 0.0842 | val loss: 0.5032 | macro F1: 0.4517 | weighted F1: 0.6685 | lr: 7.10e-06
344[vit_b_16_clahe=True] Epoch 22/50 | train loss: 0.0742 | val loss: 0.5754 | macro F1: 0.4630 | weighted F1: 0.6337 | lr: 6.80e-06
345 Early stopping at epoch 22 (no improvement for 10 epochs).
346 Training complete: 22 epochs, 3937.8s, best val macro F1: 0.4673
347 Checkpoint saved: results/vit_b_16_clahe=True/best_model.pth
348
349 Test results — Weighted F1: 0.6913 | Macro F1: 0.4777 | Accuracy: 0.6705 | Kappa: 0.5624
350 precision recall f1-score support
351
352 Healthy (0) 0.85 0.76 0.80 3872
353 Mild NPDR (1) 0.13 0.22 0.16 366
354 Moderate NPDR (2) 0.43 0.48 0.45 794
355 Severe NPDR (3) 0.33 0.43 0.37 131
356Proliferative DR (4) 0.66 0.56 0.60 106
357
358 accuracy 0.67 5269
359 macro avg 0.48 0.49 0.48 5269
360 weighted avg 0.72 0.67 0.69 5269
361
362
363======================================================================
364Model CLAHE W-F1 M-F1 Acc Kappa Time(s) Epochs
365----------------------------------------------------------------------
366resnet50 False 0.6570 0.4630 0.6111 0.5341 7514.0 50
367resnet50 True 0.6473 0.4426 0.6058 0.5234 7428.3 42
368efficientnet_b0 False 0.6544 0.4565 0.6092 0.5508 4417.0 30
369efficientnet_b0 True 0.6583 0.4510 0.6225 0.5388 5039.9 29
370vit_b_16 False 0.6913 0.4654 0.6800 0.5326 5571.3 37
371vit_b_16 True 0.6913 0.4777 0.6705 0.5624 3937.8 22
372======================================================================
373
374Summary saved to results/summary.csv
375Comparison chart saved to results/comparison_chart.png
diff --git a/results/resnet50_clahe=False/classification_report.txt b/results/resnet50_clahe=False/classification_report.txt
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1Experiment: resnet50_clahe=False
2
3 precision recall f1-score support
4
5 Healthy (0) 0.86 0.94 0.90 3872
6 Mild NPDR (1) 0.19 0.06 0.09 366
7 Moderate NPDR (2) 0.60 0.54 0.57 794
8 Severe NPDR (3) 0.48 0.52 0.50 131
9Proliferative DR (4) 0.55 0.48 0.52 106
10
11 accuracy 0.80 5269
12 macro avg 0.54 0.51 0.51 5269
13 weighted avg 0.76 0.80 0.77 5269
diff --git a/results/resnet50_clahe=False/confusion_matrix.png b/results/resnet50_clahe=False/confusion_matrix.png
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1Experiment: resnet50_clahe=True
2
3 precision recall f1-score support
4
5 Healthy (0) 0.87 0.91 0.89 3872
6 Mild NPDR (1) 0.18 0.16 0.17 366
7 Moderate NPDR (2) 0.60 0.48 0.53 794
8 Severe NPDR (3) 0.39 0.53 0.45 131
9Proliferative DR (4) 0.66 0.58 0.61 106
10
11 accuracy 0.78 5269
12 macro avg 0.54 0.53 0.53 5269
13 weighted avg 0.76 0.78 0.77 5269
diff --git a/results/resnet50_clahe=True/confusion_matrix.png b/results/resnet50_clahe=True/confusion_matrix.png
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diff --git a/results/vit_b_16_clahe=False/classification_report.txt b/results/vit_b_16_clahe=False/classification_report.txt
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1Experiment: vit_b_16_clahe=False
2
3 precision recall f1-score support
4
5 Healthy (0) 0.83 0.79 0.81 3872
6 Mild NPDR (1) 0.12 0.17 0.14 366
7 Moderate NPDR (2) 0.41 0.46 0.43 794
8 Severe NPDR (3) 0.50 0.37 0.43 131
9Proliferative DR (4) 0.63 0.43 0.51 106
10
11 accuracy 0.68 5269
12 macro avg 0.50 0.45 0.47 5269
13 weighted avg 0.71 0.68 0.69 5269
diff --git a/results/vit_b_16_clahe=False/confusion_matrix.png b/results/vit_b_16_clahe=False/confusion_matrix.png
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1Experiment: vit_b_16_clahe=True
2
3 precision recall f1-score support
4
5 Healthy (0) 0.85 0.80 0.83 3872
6 Mild NPDR (1) 0.14 0.22 0.17 366
7 Moderate NPDR (2) 0.45 0.45 0.45 794
8 Severe NPDR (3) 0.43 0.47 0.45 131
9Proliferative DR (4) 0.56 0.56 0.56 106
10
11 accuracy 0.69 5269
12 macro avg 0.49 0.50 0.49 5269
13 weighted avg 0.73 0.69 0.71 5269
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generated by cgit v1.2.3 (git 2.25.1) at 2026-09-03 06:33:53 +0000