2025-06-11 23:20:00,905 [INFO ] [logger.py:dumpArgs:73]	 Starting with arguments
{"name": "cifar10", "dataset": {"path": "../../datasets/cifar10", "image_size": 224}, "output": "./results/ece/cifar10/", "classifier": "./models/dirichlet-cifar10/cifar10-20250611-062342.pklz", "cuda_index": 0, "batch_size": 100, "buckets": 10, "seed": 1337, "verbose": 1}
2025-06-11 23:20:00,967 [INFO ] [util.py:selectDevice:88]	 Using cuda:0 for tensor calculations
2025-06-11 23:20:01,279 [INFO ] [ece_dataset.py:<module>:48]	 Running on cuda:0
2025-06-11 23:20:01,279 [INFO ] [loader.py:getDatasetSplits:21]	 Loading CIFAR10 dataset from ../../datasets/cifar10
2025-06-11 23:20:01,279 [INFO ] [baselines.py:createCIFAR10Dataset:69]	 Using 10 CIFAR10 targets: ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
2025-06-11 23:20:01,311 [INFO ] [baselines.py:getTransform:24]	 Resizing images from 32 to 224
2025-06-11 23:20:01,311 [INFO ] [baselines.py:getTransform:37]	 Applying CIFAR10 standard normalization
2025-06-11 23:20:02,378 [INFO ] [torch_utils.py:split:72]	 Split training from 50000 into 35000 training images and 15000 validation images. Found 10000 testing images.
2025-06-11 23:20:02,378 [INFO ] [ece_dataset.py:<module>:56]	 Using dataset cifar10 with 10000 test samples and 10 classes
2025-06-11 23:20:02,576 [INFO ] [ece_dataset.py:<module>:63]	 Using dirichlet decision maker
2025-06-11 23:20:13,161 [INFO ] [calibration.py:computeECE:244]	 
        Computed ECE 0.05610329657793045 in 10.583910065935925 seconds with 10 buckets:
        * Non-zero buckets: tensor([1, 2, 3, 4, 5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([  58,  547,  805,  936,  786,  711,  811, 1032, 4314],
       dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.1827, 0.2573, 0.3497, 0.4496, 0.5483, 0.6515, 0.7494, 0.8547, 0.9736])
        * Final bucket accuracy: tensor([0.2931, 0.3638, 0.4770, 0.5662, 0.6527, 0.7539, 0.8237, 0.9060, 0.9775])
        * Label counts: tensor([1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000],
       dtype=torch.int32)
        * Prediction Action Counts: tensor([1162,  891,  821,  906,  955,  898, 1309,  837, 1176, 1045],
       dtype=torch.int32)
        * Average accuracy: 0.7998999953269958
    
