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{
 "model_name": "mlp",
 "split": "test",
 "loss": 2.6382831914232705,
 "metrics": {
 "accuracy": 0.23965141612200436,
 "macro_precision": 0.1691124485117467,
 "macro_recall": 0.15433803994026662,
 "macro_f1": 0.1335123629487021,
 "weighted_precision": 0.19933703394054736,
 "weighted_recall": 0.23965141612200436,
 "weighted_f1": 0.18996045642391454,
 "per_class": {
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 "classification_report": " precision recall f1-score support\n\n Alambadi Cow 0.00 0.00 0.00 14\n Amritmahal Cow 0.00 0.00 0.00 14\n Banni Buffalo 0.00 0.00 0.00 5\n Bargur Cow 0.00 0.00 0.00 9\n Dangi Cow 0.00 0.00 0.00 12\n Deoni Cow 0.00 0.00 0.00 16\n Gir Cow 0.38 0.53 0.44 38\n Hallikar Cow 0.24 0.32 0.28 28\nJaffrabadi Buffalo 0.23 0.31 0.26 16\n Kangayam Cow 0.25 0.06 0.09 18\n Kankrej Cow 0.18 0.22 0.20 27\n Kasaragod Cow 0.00 0.00 0.00 14\n Kenkatha Cow 0.25 0.12 0.17 8\n Kherigarh Cow 0.00 0.00 0.00 5\n Malnad gidda Cow 0.21 0.25 0.23 16\n Mehsana Buffalo 0.13 0.13 0.13 15\n Nagori Cow 1.00 0.08 0.14 13\n Nagpuri Buffalo 0.20 0.45 0.28 33\n Nili ravi Buffalo 0.00 0.00 0.00 14\n Nimari Cow 0.67 0.31 0.42 13\n Pulikulam Cow 0.00 0.00 0.00 19\n Rathi Cow 0.08 0.05 0.06 22\n Sahiwal Cow 0.29 0.67 0.41 36\n Shurti Buffalo 0.00 0.00 0.00 9\n Tharparkar Cow 0.28 0.52 0.37 33\n Umblachery Cow 0.00 0.00 0.00 12\n\n accuracy 0.24 459\n macro avg 0.17 0.15 0.13 459\n weighted avg 0.20 0.24 0.19 459\n"
 },
 "latency": {
 "avg_ms": 2.55,
 "min_ms": 2.53,
 "max_ms": 2.62,
 "device": "cuda"
 },
 "model_size_mb": 590.55,
 "num_parameters": 154808090
}