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ba8d231 | 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 | === EXP 2 (arch AB x ds=1) start 2026-07-15T11:31:28+00:00 ===
[main] device=cuda
[main] paired train=11610 val=3582
Setting up [LPIPS] perceptual loss: trunk [alex], v[0.1], spatial [off]
/usr/local/lib/python3.12/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
warnings.warn(
/usr/local/lib/python3.12/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/alexnet-owt-7be5be79.pth" to /home/ubuntu/.cache/torch/hub/checkpoints/alexnet-owt-7be5be79.pth
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Loading model from: /home/ubuntu/.local/lib/python3.12/site-packages/lpips/weights/v0.1/alex.pth
[arch=A ds=1] params = 11.12M
[arch=A ds=1] step 500 val_l1=0.1432 val_lpips=0.3509 best=0.3509 (249s)
[arch=A ds=1] step 1000 val_l1=0.1349 val_lpips=0.3188 best=0.3188 (497s)
[arch=A ds=1] step 1500 val_l1=0.1308 val_lpips=0.2996 best=0.2996 (748s)
[arch=A ds=1] step 2000 val_l1=0.1264 val_lpips=0.2920 best=0.2920 (995s)
[arch=A ds=1] step 2500 val_l1=0.1239 val_lpips=0.2677 best=0.2677 (1242s)
[arch=A ds=1] step 3000 val_l1=0.1235 val_lpips=0.2607 best=0.2607 (1489s)
[arch=A ds=1] step 3500 val_l1=0.1221 val_lpips=0.2509 best=0.2509 (1732s)
[arch=A ds=1] step 4000 val_l1=0.1224 val_lpips=0.2529 best=0.2509 (1976s)
[arch=A ds=1] step 4500 val_l1=0.1216 val_lpips=0.2450 best=0.2450 (2223s)
[arch=A ds=1] step 5000 val_l1=0.1229 val_lpips=0.2488 best=0.2450 (2473s)
[arch=A ds=1] step 5500 val_l1=0.1212 val_lpips=0.2344 best=0.2344 (2719s)
[arch=A ds=1] step 6000 val_l1=0.1210 val_lpips=0.2414 best=0.2344 (2962s)
[arch=A ds=1] step 6500 val_l1=0.1216 val_lpips=0.2359 best=0.2344 (3216s)
[arch=A ds=1] step 7000 val_l1=0.1211 val_lpips=0.2331 best=0.2331 (3468s)
[arch=A ds=1] step 7500 val_l1=0.1215 val_lpips=0.2300 best=0.2300 (3716s)
[arch=A ds=1] step 8000 val_l1=0.1226 val_lpips=0.2273 best=0.2273 (3961s)
[arch=A ds=1] DONE. best_val_lpips=0.2273 final=0.2273 elapsed=4007s
[arch=B ds=1] params = 11.12M
[arch=B ds=1] step 500 val_l1=0.1523 val_lpips=0.3325 best=0.3325 (245s)
[arch=B ds=1] step 1000 val_l1=0.1535 val_lpips=0.3081 best=0.3081 (482s)
[arch=B ds=1] step 1500 val_l1=0.1531 val_lpips=0.3034 best=0.3034 (698s)
[arch=B ds=1] step 2000 val_l1=0.1522 val_lpips=0.3062 best=0.3034 (951s)
[arch=B ds=1] step 2500 val_l1=0.1524 val_lpips=0.3021 best=0.3021 (1197s)
[arch=B ds=1] step 3000 val_l1=0.1518 val_lpips=0.2994 best=0.2994 (1444s)
[arch=B ds=1] step 3500 val_l1=0.1476 val_lpips=0.3042 best=0.2994 (1686s)
[arch=B ds=1] step 4000 val_l1=0.1441 val_lpips=0.3010 best=0.2994 (1928s)
[arch=B ds=1] step 4500 val_l1=0.1406 val_lpips=0.3019 best=0.2994 (2172s)
[arch=B ds=1] step 5000 val_l1=0.1387 val_lpips=0.2995 best=0.2994 (2408s)
[arch=B ds=1] step 5500 val_l1=0.1399 val_lpips=0.3038 best=0.2994 (2647s)
[arch=B ds=1] step 6000 val_l1=0.1364 val_lpips=0.2983 best=0.2983 (2881s)
[arch=B ds=1] step 6500 val_l1=0.1371 val_lpips=0.2954 best=0.2954 (3123s)
[arch=B ds=1] step 7000 val_l1=0.1393 val_lpips=0.3018 best=0.2954 (3363s)
[arch=B ds=1] step 7500 val_l1=0.1326 val_lpips=0.2949 best=0.2949 (3576s)
[arch=B ds=1] step 8000 val_l1=0.1323 val_lpips=0.2955 best=0.2949 (3778s)
[arch=B ds=1] DONE. best_val_lpips=0.2949 final=0.2955 elapsed=3814s
=== LEADERBOARD (best val LPIPS, lower better) ===
arch ds best_lpips final_lpips final_l1 params(M)
A 1 0.2273 0.2273 0.1226 11.12
B 1 0.2949 0.2955 0.1323 11.12
=== EXP 2 done 2026-07-15T13:42:10+00:00 exit=0 ===
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