Añadir modelo y artefactos de la ejecución: n_2025_W41_8v45i4

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n_2025_W41_8v45i4/model.onnx

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n_2025_W41_8v45i4/onnx_validation_report.txt

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+=== Reporte de Validación Cruzada (PyTorch vs ONNX) ===
+Modelo: n_2025_W41_8v45i4
+Fecha: 2025-10-13 00:33:13
+=======================================================
+Precisión del modelo PyTorch (Trainer): 0.9350
+Precisión del modelo ONNX:              0.9350
+Diferencia: 0.0000
+=======================================================
+Total de imágenes evaluadas: 9000
+✅ La precisión se mantiene consistente.

n_2025_W41_8v45i4/train_report.txt

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+=== Reporte de Entrenamiento ===
+Fecha y hora: 2025-10-13T00:01:46.437924
+
+>> Métricas iniciales (baseline)
+{
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+  "eval_runtime": 193.5134,
+  "eval_samples_per_second": 46.508,
+  "eval_steps_per_second": 2.909
+}
+
+>> Métricas finales
+{
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+  "eval_runtime": 135.0182,
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+  "epoch": 5.0
+}

n_2025_W41_8v45i4/training_summary.txt

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+=== Resumen de Entrenamiento ===
+ID de ejecución: n_2025_W41_8v45i4
+Modelo base: google/vit-base-patch16-224-in21k
+Fecha: 2025-10-13 00:33:14
+
+Total de imágenes en dataset: 60000
+ - Entrenamiento: 51000
+ - Prueba: 9000
+
+Distribución de etiquetas:
+ - real: 20000
+ - semisynthetic: 20000
+ - synthetic: 20000