2025-05-22 10:51:01,349 [INFO ] [logger.py:dumpArgs:73]	 Starting with arguments
{"name": "cifar10", "dataset": {"path": "../../datasets/cifar10", "image_size": 224}, "output": "./results/ece/cifar10/", "classifier": "./models/cifar10/cifar10-20250515-150106.pklz", "cuda_index": 0, "batch_size": 100, "buckets": 10, "seed": 1337, "verbose": 1}
2025-05-22 10:51:01,383 [INFO ] [util.py:selectDevice:87]	 Using cuda:0 for tensor calculations
2025-05-22 10:51:01,535 [INFO ] [ece_dataset.py:<module>:61]	 Running on cuda:0
2025-05-22 10:51:01,535 [INFO ] [loader.py:getDatasetSplits:20]	 Loading CIFAR10 dataset from ../../datasets/cifar10
2025-05-22 10:51:01,535 [INFO ] [baselines.py:createCIFAR10Dataset:113]	 Using 10 CIFAR10 targets: ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
2025-05-22 10:51:01,570 [INFO ] [baselines.py:getTransform:62]	 Resizing images from 32 to 224
2025-05-22 10:51:01,570 [INFO ] [baselines.py:getTransform:75]	 Applying CIFAR10 standard normalization
2025-05-22 10:51:02,726 [INFO ] [baselines.py:splitTrainingValidation:47]	 Split training from 50000 into 35000 training images and 15000 validation images. Found 10000 testing images.
2025-05-22 10:51:02,726 [INFO ] [ece_dataset.py:<module>:69]	 Using dataset cifar10 with 10000 test samples and 10 classes
2025-05-22 10:51:02,982 [INFO ] [ece_dataset.py:<module>:83]	 Setting classifier activation function to softmax for multiclass
2025-05-22 10:51:17,355 [INFO ] [calibration.py:computeECE:245]	 
        Computed ECE 0.07527998089790344 in 14.37144280411303 seconds with 10 buckets:
        * Non-zero buckets: tensor([1, 2, 3, 4, 5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([   2,  106,  475,  793,  967,  870,  936, 1202, 4649],
       dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.1887, 0.2683, 0.3540, 0.4529, 0.5480, 0.6501, 0.7507, 0.8536, 0.9772])
        * Final bucket accuracy: tensor([0.5000, 0.2925, 0.3011, 0.3884, 0.4571, 0.5529, 0.6175, 0.7246, 0.9297])
        * Label counts: tensor([1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000],
       dtype=torch.int32)
        * Prediction Action Counts: tensor([1298,  941,  797,  733,  893,  900, 1363,  815, 1300,  960],
       dtype=torch.int32)
        * Average accuracy: 0.7177000045776367
    
