| | --- |
| | datasets: |
| | - timm/mini-imagenet |
| | license: apache-2.0 |
| | pipeline_tag: image-classification |
| | library_name: timm |
| | --- |
| | |
| | # Comparisons of timm Optimizers w/ Caution |
| |
|
| | This repository contains summaries of several sets of experiments comparing a number of optimizers with and without **Caution**, as introduced in the paper [Cautious Optimizers: Improving Training with One Line of Code](https://huggingface.co/papers/2411.16085). |
| |
|
| | **Official Code**: [kyleliang919/C-Optim](https://github.com/kyleliang919/c-optim) |
| |
|
| | The runs were all performed training a smaller ViT (`vit_wee_patch16_reg1_gap_256`) for 200 epochs (10M samples seen) from scratch on the `timm` 'mini-imagenet' dataset, a 100 class subset of imagenet with same image sizes as originals. |
| |
|
| | So far I have results for `adamw`, `laprop`, and `mars` (https://huggingface.co/papers/2411.10438). You can find full results in sub-folders by optimizer names. In all of these runs, the experiments with 'c' prefix in the name have caution enabled. |
| |
|
| | This is what the 'caution' addition looks like in an optimizer: |
| | ```python |
| | mask = (exp_avg * grad > 0).to(grad.dtype) |
| | mask.div_(mask.mean().clamp_(min=1e-3)) |
| | exp_avg = exp_avg * mask |
| | ``` |
| |
|
| | Train args: |
| |
|
| | ```bash |
| | ./distributed_train.sh 2 --dataset hfds/timm/mini-imagenet --num-classes 100 --model vit_wee_patch16_reg1_gap_256 -j 8 --epochs 200 --warmup-prefix --sched-on-updates --warmup-lr 0 --mixup .2 --model-ema --model-ema-decay 0.999 --model-ema-warmup --aa rand-m9-mstd0.5-inc1 --remode pixel --reprob 0.25 --amp --weight-decay .05 --drop 0.1 --drop-path .1 -b 288 --opt cadamw --lr 1e-3 |
| | ``` |
| |
|
| | # LaProp |
| |
|
| | |optim |best_epoch|train_loss |eval_loss |eval_top1 |eval_top5 |lr | |
| | |----------------------------|----------|------------------|------------------|-----------------|-----------------|----------------------| |
| | |claprop, lr=1e-03 |204.0 |2.2173619270324707|1.0931779468536378|73.920000390625 |91.33000009765624|0.0 | |
| | |claprop, lr=5e-04 |183.0 |2.262192726135254 |1.0912627222061158|73.77000073242188|91.22000260009766|1.3478660293113704e-05| |
| | |laprop, lr=5e-04 |198.0 |2.2425642013549805|1.1426102781295775|71.73000213623047|90.55000146484376|1.109508849230001e-06 | |
| | |laprop, lr=1e-03 |179.0 |2.290040969848633 |1.168387135314941 |71.15000104980469|90.18000189208983|3.806023374435663e-05 | |
| | |claprop, lr=2e-04 |195.0 |2.546172380447388 |1.2475446645736694|68.30000163574219|89.15000153808593|9.97634228344235e-07 | |
| | |laprop, lr=2e-04 |204.0 |2.6702351570129395|1.309178423690796 |67.07999990234374|88.67000270996094|0.0 | |
| | |claprop, lr=2e-03 |193.0 |2.678058862686157 |1.5239886917114258|62.08000177001953|84.8 |1.4890673845226132e-05| |
| | |laprop, lr=2e-03 |200.0 |2.70467209815979 |1.522907255935669 |61.46000135498047|85.28000162353516|1.9732715717284413e-06| |
| | |
| | ## LaProp Top-1 Evaluation Accuracy on Mini-ImageNet |
| |  |
| | |
| | ## LaProp Train Loss |
| |  |
| | |
| | # AdamW |
| | |
| | |optim |best_epoch|train_loss |eval_loss |eval_top1 |eval_top5 | |
| | |----------------------------|-----|------------------|------------------|-----------------|-----------------| |
| | |cadamw, lr=1e-03 |184.0|2.2688851356506348|1.0868136840820313|73.52000141601563|91.60000036621092| |
| | |cadamw, lr=5e-04 |199.0|2.163278102874756 |1.0976034646987916|73.3900005859375 |91.31000137939454| |
| | |cadamw, lr=1e-03, clip grads|203.0|2.1360626220703125|1.1043113907814026|73.33000158691407|91.41000042724608| |
| | |adamw, lr=1e-03, clip grads |195.0|2.2746386528015137|1.142998440361023 |72.11000151367188|90.47000052490236| |
| | |adamw, lr=5e-04 |185.0|2.3040246963500977|1.1535791856765747|71.50000120849609|90.4800001953125 |\ |
| | |adamw, lr=1e-03 |199.0|2.223684310913086 |1.1657958560943604|71.22999993896484|90.30999958496092| |
| | |cadamw, lr=2e-04 |189.0|2.538627862930298 |1.2325929063796996|68.94999995117188|89.61000139160156| |
| | |adamw, lr=2e-04 |203.0|2.579624652862549 |1.3085522148132325|67.11000026855469|88.66000164794922| |
| |
|
| | ## AdamW Top-1 Evaluation Accuracy on Mini-ImageNet |
| |  |
| |
|
| | ## AdamW Train Loss |
| |  |
| |
|
| | # MARS |
| |
|
| | |optim |best_epoch|train_loss |eval_loss |eval_top1 |eval_top5 | |
| | |---------------|----------|------------------|------------------|-----------------|-----------------| |
| | |cmars, lr=1e-03|198.0 |2.054780960083008 |1.0435627010345458|74.91000185546875|92.08000146484376| |
| | |cmars, lr=2e-03|203.0 |2.0272469520568848|1.0705795244216918|74.31000185546876|91.54000092773435| |
| | |mars, lr=1e-03 |184.0 |2.219767808914185 |1.07215625667572 |74.06000178222656|91.6200013671875 |\ |
| | |mars, lr=2e-03 |197.0 |2.1453990936279297|1.0963781481742858|73.73000098876953|91.1500006225586 |\ |
| | |cmars, lr=5e-04|198.0 |2.2018630504608154|1.083557384109497 |73.32000045166015|91.67000092773438|\ |
| | |mars, lr=5e-04 |189.0 |2.322845220565796 |1.1199828132629397|72.02999995117187|90.86000173339843| |
| | |
| | |
| | ## MARS Top-1 Evaluation Accuracy on Mini-ImageNet |
| |  |
| | |
| | ## MARS Train Loss |
| |  |
