1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
|
Using device: cuda
Downloading/locating dataset via kagglehub...
Dataset root: /home/vin/.cache/kagglehub/datasets/amanneo/diabetic-retinopathy-resized-arranged/versions/1
Dataset split — train: 24588, val: 5269, test: 5269
======================================================================
Experiment: resnet50 | CLAHE: False
======================================================================
Using 2 GPUs via DataParallel
/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.)
return F.linear(input, self.weight, self.bias)
[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
[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
[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
/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.
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
Training complete: 50 epochs, 7514.0s, best val macro F1: 0.4648
Checkpoint saved: results/resnet50_clahe=False/best_model.pth
Test results — Weighted F1: 0.6570 | Macro F1: 0.4630 | Accuracy: 0.6111 | Kappa: 0.5341
precision recall f1-score support
Healthy (0) 0.86 0.67 0.75 3872
Mild NPDR (1) 0.11 0.35 0.17 366
Moderate NPDR (2) 0.46 0.50 0.48 794
Severe NPDR (3) 0.45 0.41 0.43 131
Proliferative DR (4) 0.64 0.40 0.49 106
accuracy 0.61 5269
macro avg 0.50 0.47 0.46 5269
weighted avg 0.73 0.61 0.66 5269
======================================================================
Experiment: resnet50 | CLAHE: True
======================================================================
Using 2 GPUs via DataParallel
[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
[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
[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
/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.
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
Early stopping at epoch 42 (no improvement for 10 epochs).
Training complete: 42 epochs, 7428.3s, best val macro F1: 0.4750
Checkpoint saved: results/resnet50_clahe=True/best_model.pth
Test results — Weighted F1: 0.6473 | Macro F1: 0.4426 | Accuracy: 0.6058 | Kappa: 0.5234
precision recall f1-score support
Healthy (0) 0.86 0.66 0.75 3872
Mild NPDR (1) 0.12 0.32 0.17 366
Moderate NPDR (2) 0.39 0.52 0.45 794
Severe NPDR (3) 0.35 0.33 0.34 131
Proliferative DR (4) 0.46 0.56 0.50 106
accuracy 0.61 5269
macro avg 0.44 0.48 0.44 5269
weighted avg 0.72 0.61 0.65 5269
======================================================================
Experiment: efficientnet_b0 | CLAHE: False
======================================================================
Using 2 GPUs via DataParallel
[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
[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
[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
/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.
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
Early stopping at epoch 30 (no improvement for 10 epochs).
Training complete: 30 epochs, 4417.0s, best val macro F1: 0.4586
Checkpoint saved: results/efficientnet_b0_clahe=False/best_model.pth
Test results — Weighted F1: 0.6544 | Macro F1: 0.4565 | Accuracy: 0.6092 | Kappa: 0.5508
precision recall f1-score support
Healthy (0) 0.86 0.68 0.76 3872
Mild NPDR (1) 0.12 0.38 0.18 366
Moderate NPDR (2) 0.43 0.44 0.43 794
Severe NPDR (3) 0.37 0.40 0.39 131
Proliferative DR (4) 0.50 0.55 0.52 106
accuracy 0.61 5269
macro avg 0.46 0.49 0.46 5269
weighted avg 0.73 0.61 0.65 5269
======================================================================
Experiment: efficientnet_b0 | CLAHE: True
======================================================================
Using 2 GPUs via DataParallel
[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
[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
[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
/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.
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
Early stopping at epoch 29 (no improvement for 10 epochs).
Training complete: 29 epochs, 5039.9s, best val macro F1: 0.4591
Checkpoint saved: results/efficientnet_b0_clahe=True/best_model.pth
Test results — Weighted F1: 0.6583 | Macro F1: 0.4510 | Accuracy: 0.6225 | Kappa: 0.5388
precision recall f1-score support
Healthy (0) 0.85 0.71 0.77 3872
Mild NPDR (1) 0.11 0.31 0.17 366
Moderate NPDR (2) 0.41 0.36 0.38 794
Severe NPDR (3) 0.35 0.48 0.41 131
Proliferative DR (4) 0.46 0.62 0.53 106
accuracy 0.62 5269
macro avg 0.44 0.50 0.45 5269
weighted avg 0.71 0.62 0.66 5269
======================================================================
Experiment: vit_b_16 | CLAHE: False
======================================================================
Using 2 GPUs via DataParallel
/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.)
attn_output = scaled_dot_product_attention(
[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
[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
[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
/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.
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
Early stopping at epoch 37 (no improvement for 10 epochs).
Training complete: 37 epochs, 5571.3s, best val macro F1: 0.4859
Checkpoint saved: results/vit_b_16_clahe=False/best_model.pth
Test results — Weighted F1: 0.6913 | Macro F1: 0.4654 | Accuracy: 0.6800 | Kappa: 0.5326
precision recall f1-score support
Healthy (0) 0.83 0.79 0.81 3872
Mild NPDR (1) 0.12 0.17 0.14 366
Moderate NPDR (2) 0.41 0.46 0.43 794
Severe NPDR (3) 0.50 0.37 0.43 131
Proliferative DR (4) 0.63 0.43 0.51 106
accuracy 0.68 5269
macro avg 0.50 0.45 0.47 5269
weighted avg 0.71 0.68 0.69 5269
======================================================================
Experiment: vit_b_16 | CLAHE: True
======================================================================
Using 2 GPUs via DataParallel
[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
[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
[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
/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.
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
Early stopping at epoch 22 (no improvement for 10 epochs).
Training complete: 22 epochs, 3937.8s, best val macro F1: 0.4673
Checkpoint saved: results/vit_b_16_clahe=True/best_model.pth
Test results — Weighted F1: 0.6913 | Macro F1: 0.4777 | Accuracy: 0.6705 | Kappa: 0.5624
precision recall f1-score support
Healthy (0) 0.85 0.76 0.80 3872
Mild NPDR (1) 0.13 0.22 0.16 366
Moderate NPDR (2) 0.43 0.48 0.45 794
Severe NPDR (3) 0.33 0.43 0.37 131
Proliferative DR (4) 0.66 0.56 0.60 106
accuracy 0.67 5269
macro avg 0.48 0.49 0.48 5269
weighted avg 0.72 0.67 0.69 5269
======================================================================
Model CLAHE W-F1 M-F1 Acc Kappa Time(s) Epochs
----------------------------------------------------------------------
resnet50 False 0.6570 0.4630 0.6111 0.5341 7514.0 50
resnet50 True 0.6473 0.4426 0.6058 0.5234 7428.3 42
efficientnet_b0 False 0.6544 0.4565 0.6092 0.5508 4417.0 30
efficientnet_b0 True 0.6583 0.4510 0.6225 0.5388 5039.9 29
vit_b_16 False 0.6913 0.4654 0.6800 0.5326 5571.3 37
vit_b_16 True 0.6913 0.4777 0.6705 0.5624 3937.8 22
======================================================================
Summary saved to results/summary.csv
Comparison chart saved to results/comparison_chart.png
|
