2025-06-13 15:40:46,087 [INFO ] [logger.py:dumpArgs:73]	 Starting with arguments
{"name": "starcraft", "dataset": {"path": "../../datasets/starcraftimage", "image_format": "cifar10", "image_size": 224}, "output": "./results/ece/starcraft/", "classifier": "./models/dirichlet-starcraft-cifar10/starcraft-20250613-033643.pklz", "cuda_index": 1, "batch_size": 100, "buckets": 10, "seed": 1337, "verbose": 1}
2025-06-13 15:40:46,144 [INFO ] [util.py:selectDevice:88]	 Using cuda:1 for tensor calculations
2025-06-13 15:40:46,454 [INFO ] [ece_dataset.py:<module>:48]	 Running on cuda:1
2025-06-13 15:40:46,454 [INFO ] [loader.py:getDatasetSplits:24]	 Loading StarCraft dataset from ../../datasets/starcraftimage
2025-06-13 15:40:46,454 [INFO ] [starcraft.py:createStarCraftDataset:97]	 Using 10 StarCraft targets: ['Acolyte LE at Beginning', 'Acolyte LE at End', 'Abyssal Reef LE at Beginning', 'Abyssal Reef LE at End', 'Ascension to Aiur LE at Beginning', 'Ascension to Aiur LE at End', 'Mech Depot LE at Beginning', 'Mech Depot LE at End', 'Odyssey LE at Beginning', 'Odyssey LE at End']
2025-06-13 15:40:46,485 [INFO ] [baselines.py:getTransform:24]	 Resizing images from 32 to 224
2025-06-13 15:40:46,486 [INFO ] [baselines.py:getTransform:40]	 Applying 0.5 normalization
2025-06-13 15:40:47,461 [INFO ] [torch_utils.py:split:72]	 Split training from 50000 into 35000 training images and 15000 validation images. Found 10000 testing images.
2025-06-13 15:40:47,461 [INFO ] [ece_dataset.py:<module>:56]	 Using dataset starcraft with 10000 test samples and 10 classes
2025-06-13 15:40:47,673 [INFO ] [ece_dataset.py:<module>:63]	 Using dirichlet decision maker
2025-06-13 15:40:57,446 [INFO ] [calibration.py:computeECE:244]	 
        Computed ECE 0.02974240668118 in 9.771522666094825 seconds with 10 buckets:
        * Non-zero buckets: tensor([4, 5, 6, 7, 8, 9])
        * Final bucket sizes: tensor([ 221, 1264, 1320, 1361, 1714, 4120], dtype=torch.int32) totaling 10000 samples
        * Final bucket confidences: tensor([0.4891, 0.5496, 0.6493, 0.7523, 0.8526, 0.9630])
        * Final bucket accuracy: tensor([0.5113, 0.5736, 0.6659, 0.7700, 0.8256, 0.9218])
        * Label counts: tensor([1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000],
       dtype=torch.int32)
        * Prediction Action Counts: tensor([1294,  706, 1203,  798, 1206,  793, 1067,  933, 1309,  691],
       dtype=torch.int32)
        * Average accuracy: 0.7978000044822693
    
