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Browse files- README.md +19 -0
- adamw/adamw-lr_0002-checkpoint-203.pth.tar +3 -0
- adamw/adamw-lr_0005-checkpoint-185.pth.tar +3 -0
- adamw/adamw-lr_001-checkpoint-199.pth.tar +3 -0
- adamw/adamw-lr_001-clip-checkpoint-195.pth.tar +3 -0
- adamw/cadamw-lr_0002-checkpoint-189.pth.tar +3 -0
- adamw/cadamw-lr_0005-checkpoint-199.pth.tar +3 -0
- adamw/cadamw-lr_001-checkpoint-184.pth.tar +3 -0
- adamw/cadamw-lr_001-clip-checkpoint-203.pth.tar +3 -0
- adamw/eval_loss_comparison.png +0 -0
- adamw/eval_top1_comparison.png +0 -0
- adamw/eval_top5_comparison.png +0 -0
- adamw/final_results_summary.csv +9 -0
- adamw/train_loss_comparison.png +0 -0
- laprop/claprop-lr_0002-checkpoint-195.pth.tar +3 -0
- laprop/claprop-lr_0005-checkpoint-183.pth.tar +3 -0
- laprop/claprop-lr_001-checkpoint-204.pth.tar +3 -0
- laprop/claprop-lr_002-checkpoint-193.pth.tar +3 -0
- laprop/eval_loss_comparison.png +0 -0
- laprop/eval_top1_comparison.png +0 -0
- laprop/eval_top5_comparison.png +0 -0
- laprop/final_results_summary.csv +9 -0
- laprop/laprop-lr_0002-checkpoint-204.pth.tar +3 -0
- laprop/laprop-lr_0005-checkpoint-198.pth.tar +3 -0
- laprop/laprop-lr_001-checkpoint-179.pth.tar +3 -0
- laprop/laprop-lr_002-checkpoint-200.pth.tar +3 -0
- laprop/train_loss_comparison.png +0 -0
README.md
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---
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license: apache-2.0
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datasets:
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- timm/mini-imagenet
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---
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# Comparisons of timm Optimizers w/ Caution
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This repo contains summaries of several sets of experiments comparing a number of optimizers with and without caution (https://huggingface.co/papers/2411.16085) enabled.
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The runs were all performed training from scratch on the `timm` 'mini-imagenet' dataset, a 100 class subset of imagenet with same image sizes as originals.
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So far I have results for `adamw` and `laprop` but have some `mars` on the way. You can find full results in sub-folders by optimizer names.
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# LaProp
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# AdamW
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adamw/adamw-lr_0002-checkpoint-203.pth.tar
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adamw/adamw-lr_001-checkpoint-199.pth.tar
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adamw/adamw-lr_001-clip-checkpoint-195.pth.tar
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adamw/cadamw-lr_0002-checkpoint-189.pth.tar
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adamw/cadamw-lr_001-checkpoint-184.pth.tar
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adamw/cadamw-lr_001-clip-checkpoint-203.pth.tar
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adamw/eval_loss_comparison.png
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adamw/eval_top1_comparison.png
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adamw/eval_top5_comparison.png
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adamw/final_results_summary.csv
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,epoch,train_loss,eval_loss,eval_top1,eval_top5,lr
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"cadamw, lr=1e-03",184.0,2.2688851356506348,1.0868136840820313,73.52000141601563,91.60000036621092,2.4471741852423235e-05
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"cadamw, lr=5e-04",199.0,2.163278102874756,1.0976034646987916,73.3900005859375,91.31000137939454,7.70666566718009e-07
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"cadamw, lr=1e-03, clip grads",203.0,2.1360626220703125,1.1043113907814026,73.33000158691407,91.41000042724608,6.168375916970616e-08
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"adamw, lr=1e-03, clip grads",195.0,2.2746386528015137,1.142998440361023,72.11000151367188,90.47000052490236,4.988171141721176e-06
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"adamw, lr=5e-04",185.0,2.3040246963500977,1.1535791856765747,71.50000120849609,90.4800001953125,1.1051746300417442e-05
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"adamw, lr=1e-03",199.0,2.223684310913086,1.1657958560943604,71.22999993896484,90.30999958496092,1.541333133436018e-06
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"cadamw, lr=2e-04",189.0,2.538627862930298,1.2325929063796996,68.94999995117188,89.61000139160156,2.763007960232344e-06
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"adamw, lr=2e-04",203.0,2.579624652862549,1.3085522148132325,67.11000026855469,88.66000164794922,1.2336751833941227e-08
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adamw/train_loss_comparison.png
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laprop/claprop-lr_0002-checkpoint-195.pth.tar
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laprop/claprop-lr_0005-checkpoint-183.pth.tar
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laprop/claprop-lr_001-checkpoint-204.pth.tar
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laprop/claprop-lr_002-checkpoint-193.pth.tar
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laprop/eval_loss_comparison.png
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laprop/eval_top1_comparison.png
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laprop/eval_top5_comparison.png
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laprop/final_results_summary.csv
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,epoch,train_loss,eval_loss,eval_top1,eval_top5,lr
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"claprop, lr=1e-03",204.0,2.2173619270324707,1.0931779468536378,73.920000390625,91.33000009765624,0.0
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"claprop, lr=5e-04",183.0,2.262192726135254,1.0912627222061158,73.77000073242188,91.22000260009766,1.3478660293113704e-05
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"laprop, lr=5e-04",198.0,2.2425642013549805,1.1426102781295775,71.73000213623047,90.55000146484376,1.109508849230001e-06
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"laprop, lr=1e-03",179.0,2.290040969848633,1.168387135314941,71.15000104980469,90.18000189208983,3.806023374435663e-05
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"claprop, lr=2e-04",195.0,2.546172380447388,1.2475446645736694,68.30000163574219,89.15000153808593,9.97634228344235e-07
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"laprop, lr=2e-04",204.0,2.6702351570129395,1.309178423690796,67.07999990234374,88.67000270996094,0.0
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"claprop, lr=2e-03",193.0,2.678058862686157,1.5239886917114258,62.08000177001953,84.8,1.4890673845226132e-05
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"laprop, lr=2e-03",200.0,2.70467209815979,1.522907255935669,61.46000135498047,85.28000162353516,1.9732715717284413e-06
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laprop/laprop-lr_0002-checkpoint-204.pth.tar
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laprop/laprop-lr_0005-checkpoint-198.pth.tar
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laprop/laprop-lr_001-checkpoint-179.pth.tar
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laprop/train_loss_comparison.png
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