Environment:
	Python: 3.10.19
	PyTorch: 2.8.0+cu128
	Torchvision: 0.23.0+cu128
	CUDA: 12.8
	CUDNN: 91002
	NumPy: 2.2.6
	PIL: 12.3.0
Args:
	algorithm: ERM
	checkpoint_freq: None
	data_dir: /home/kaustubh/Datasets
	dataset: TerraIncognita
	holdout_fraction: 0.2
	hparams: {"resnet50_augmix": false, "freeze_bn": true}
	hparams_seed: 0
	output_dir: /home/kaustubh/Projects/icml_repo/rdi-repro/runs/ti/ERM_env3_s0
	save_model_every_checkpoint: False
	seed: 0
	skip_model_save: True
	steps: None
	task: domain_generalization
	test_envs: [3]
	trial_seed: 0
	uda_holdout_fraction: 0
HParams:
	batch_size: 32
	class_balanced: False
	data_augmentation: True
	dinov2: False
	freeze_bn: True
	lars: False
	linear_steps: 500
	lr: 5e-05
	nonlinear_classifier: False
	resnet18: False
	resnet50_augmix: False
	resnet_dropout: 0.0
	vit: False
	vit_attn_tune: False
	vit_dropout: 0.0
	weight_decay: 0.0
/home/kaustubh/miniconda3/envs/torch_128/lib/python3.10/site-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(
/home/kaustubh/miniconda3/envs/torch_128/lib/python3.10/site-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=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.
  warnings.warn(msg)
env0_in_acc   env0_out_acc  env1_in_acc   env1_out_acc  env2_in_acc   env2_out_acc  env3_in_acc   env3_out_acc  epoch         loss          mem_gb        step          step_time    
0.2023565574  0.2041025641  0.4575121577  0.4736303123  0.1902985075  0.2052238806  0.2202939976  0.2132352941  0.0000000000  2.3565170765  7.9323825836  0             0.7989165783 
0.8639856557  0.8625641026  0.7992065523  0.8074756784  0.7384950249  0.7189054726  0.3703552470  0.3750000000  2.9850746269  0.7471074875  8.1373462677  300           0.1778967810 
0.9044569672  0.8748717949  0.8354235987  0.8376856119  0.7810945274  0.7674129353  0.4258881176  0.4501633987  5.9701492537  0.4755672404  8.1373462677  600           0.1774869768 
0.9141905738  0.8800000000  0.8585871513  0.8627752176  0.8202736318  0.7885572139  0.3574928542  0.3676470588  8.9552238806  0.4017992033  8.1373462677  900           0.1778154302 
0.9482581967  0.9128205128  0.8666496033  0.8637992832  0.8622512438  0.8121890547  0.4024091466  0.4133986928  11.940298507  0.3424900154  8.1373462677  1200          0.1774987944 
0.9403176230  0.9066666667  0.8511645764  0.8325652842  0.8659825871  0.8221393035  0.2531645570  0.2614379085  14.925373134  0.2936137713  8.1373462677  1500          0.1772592433 
0.9615778689  0.9476923077  0.8951881239  0.8847926267  0.9020522388  0.8445273632  0.3664761127  0.3758169935  17.910447761  0.2686055949  8.1373462677  1800          0.1785808508 
0.9595286885  0.9425641026  0.8922446890  0.8975934460  0.8921019900  0.8507462687  0.3593303389  0.3488562092  20.895522388  0.2560067124  8.1373462677  2100          0.1770132732 
0.9667008197  0.9312820513  0.9027386742  0.8934971838  0.9135572139  0.8557213930  0.4048591262  0.4125816993  23.880597014  0.2118818355  8.1373462677  2400          0.1758385483 
0.9636270492  0.9364102564  0.9125927822  0.9103942652  0.9032960199  0.8457711443  0.3968966925  0.4133986928  26.865671641  0.2070354386  8.1373462677  2700          0.1751067265 
0.9679815574  0.9364102564  0.9170719222  0.9124423963  0.9306592040  0.8718905473  0.3699469171  0.3643790850  29.850746268  0.1918644975  8.1373462677  3000          0.1737478813 
0.9631147541  0.9374358974  0.9253903251  0.9155145929  0.9319029851  0.8793532338  0.3564720294  0.3602941176  32.835820895  0.1728494795  8.1373462677  3300          0.1712989537 
0.9741290984  0.9476923077  0.9296135142  0.8996415771  0.9337686567  0.8905472637  0.3819926501  0.3880718954  35.820895522  0.1682739259  8.1373462677  3600          0.1705061269 
0.9518442623  0.9343589744  0.9205272588  0.9027137737  0.9160447761  0.8669154229  0.2980808493  0.2834967320  38.805970149  0.1593329457  8.1373462677  3900          0.1685506360 
0.9743852459  0.9446153846  0.9321730228  0.9124423963  0.9200870647  0.8868159204  0.3905675786  0.3880718954  41.791044776  0.1560514912  8.1373462677  4200          0.1653193323 
0.9761782787  0.9487179487  0.9412592782  0.9226830517  0.9315920398  0.8768656716  0.4109840751  0.4166666667  44.776119403  0.1401332440  8.1373462677  4500          0.1644755967 
0.9807889344  0.9548717949  0.9325569491  0.9165386585  0.9203980100  0.8656716418  0.4124132299  0.4183006536  47.761194029  0.1345123955  8.1373462677  4800          0.1646195682 
0.9720799180  0.9394871795  0.9255183005  0.9047619048  0.9409203980  0.8805970149  0.3207431605  0.3104575163  49.751243781  0.1276105177  8.1373462677  5000          0.1510106242