material_estado: 9 clases (con no_material), ajuste fino completo; F1 macro 98,55 %, exactitud 98,58 %, AUC media defectos 93,3 %. El de 8 clases queda en material_estado_47fuentes

modelos_entrenados/material_estado/informe.md

@@ -1,24 +1,25 @@
 # Informe del modelo de material y estado
 
-- Fecha: 2026-11-09
-- Backbone: `facebook/dinov3-convnext-tiny-pretrain-lvd1689m (congelado)` + 2 cabezas (material y defectos)
-- Tamaño de imagen: 384 px · Temperatura: 0.504
-- Imágenes: 26934 entrenamiento · 3318 validación · 3364 prueba
+- Fecha: 2026-11-11
+- Backbone: `facebook/dinov3-convnext-tiny-pretrain-lvd1689m (ajuste fino completo)` + 2 cabezas (material y defectos)
+- Tamaño de imagen: 384 px · Temperatura: 0.558
+- Imágenes: 28592 entrenamiento · 3464 validación · 3566 prueba
 
 ## Material (conjunto de prueba)
 
-**Exactitud: 98.7 %** · F1 macro: 98.7 %
+**Exactitud: 98.6 %** · F1 macro: 98.5 %
 
 | Material | Precisión | Recall | F1 | Fotos de prueba |
 |---|---|---|---|---|
-| madera | 99.5 % | 99.8 % | 99.6 % | 426 |
-| carton | 99.1 % | 99.4 % | 99.2 % | 316 |
-| plastico | 97.8 % | 97.6 % | 97.7 % | 370 |
-| tela | 99.3 % | 100.0 % | 99.6 % | 401 |
-| metal | 99.5 % | 98.0 % | 98.8 % | 406 |
-| vidrio | 97.4 % | 98.8 % | 98.1 % | 421 |
-| papel | 98.6 % | 97.7 % | 98.2 % | 437 |
-| otro | 98.3 % | 98.3 % | 98.3 % | 343 |
+| madera | 100.0 % | 99.8 % | 99.9 % | 410 |
+| carton | 97.4 % | 99.7 % | 98.5 % | 297 |
+| plastico | 98.8 % | 94.7 % | 96.7 % | 437 |
+| tela | 99.7 % | 98.7 % | 99.2 % | 381 |
+| metal | 99.0 % | 99.3 % | 99.2 % | 417 |
+| vidrio | 97.0 % | 99.8 % | 98.4 % | 428 |
+| papel | 99.0 % | 97.8 % | 98.4 % | 418 |
+| otro | 96.7 % | 98.5 % | 97.6 % | 206 |
+| no_material | 98.4 % | 99.7 % | 99.0 % | 312 |
 
 ## Defectos (conjunto de prueba)
 
@@ -28,14 +29,14 @@ Solo se usan en la nota los defectos con ≥ 30 ejemplos con y sin el defecto en
 
 | Defecto | Se usa | AUC | Precisión media | Positivos / conocidos (prueba) |
 |---|---|---|---|---|
-| grieta | sí | 89.6 % | 87.8 % | 173 / 409 |
-| rotura | sí | 98.0 % | 96.3 % | 177 / 699 |
-| oxido | sí | 100.0 % | 100.0 % | 161 / 354 |
-| mancha | sí | 86.1 % | 78.8 % | 221 / 632 |
-| podredumbre | no | — | — | 0 / 193 |
-| deformacion | sí | 96.0 % | 95.9 % | 402 / 910 |
-| rayon | sí | 79.1 % | 73.4 % | 65 / 138 |
-| nudo | sí | 96.6 % | 98.6 % | 343 / 491 |
+| grieta | sí | 91.7 % | 89.6 % | 155 / 387 |
+| rotura | sí | 99.0 % | 97.9 % | 188 / 695 |
+| oxido | sí | 100.0 % | 100.0 % | 161 / 353 |
+| mancha | sí | 91.8 % | 89.3 % | 226 / 620 |
+| podredumbre | no | — | — | 0 / 192 |
+| deformacion | sí | 96.2 % | 96.1 % | 400 / 906 |
+| rayon | sí | 76.1 % | 72.2 % | 61 / 130 |
+| nudo | sí | 98.4 % | 99.3 % | 319 / 464 |
 
 ## Nota de estado por material
 
@@ -43,14 +44,15 @@ Se marca como validada si hay ≥ 30 fotos de prueba con defectos etiquetados y
 
 | Material | Validada | AUC media | Fotos | AUC por defecto |
 |---|---|---|---|---|
-| madera | sí | 88.5 % | 403 | grieta 89.2 %, rotura 96.4 %, mancha 72.7 %, nudo 95.8 % |
-| carton | sí | 98.8 % | 296 | deformacion 98.8 % |
+| madera | sí | 94.1 % | 380 | grieta 91.4 %, rotura 98.0 %, mancha 88.9 %, nudo 98.1 % |
+| carton | sí | 99.5 % | 283 | deformacion 99.5 % |
 | plastico | no | — | 0 | — |
-| tela | sí | 95.4 % | 299 | rotura 97.0 %, mancha 93.1 %, deformacion 96.1 % |
-| metal | sí | 90.6 % | 481 | rotura 100.0 %, oxido 100.0 %, mancha 80.2 %, deformacion 93.8 %, rayon 79.1 % |
+| tela | sí | 97.5 % | 284 | rotura 98.8 %, mancha 95.5 %, deformacion 98.0 % |
+| metal | sí | 90.5 % | 503 | rotura 98.2 %, oxido 100.0 %, mancha 85.1 %, deformacion 93.2 %, rayon 76.1 % |
 | vidrio | sí | 100.0 % | 88 | rotura 100.0 % |
 | papel | no | — | 70 | — |
 | otro | no | — | 11 | — |
+| no_material | no | — | 0 | — |
 
 ## Fuentes de datos
 
@@ -58,31 +60,7 @@ Se marca como validada si hay ≥ 30 fotos de prueba con defectos etiquetados y
 |---|---|---|---|
 | trashnet | descargada | MIT | https://huggingface.co/datasets/garythung/trashnet |
 | realwaste | descargada | CC BY 4.0 | https://archive.ics.uci.edu/dataset/908/realwaste |
-| rf-madera-tablas | descargada | CC BY 4.0 | https://universe.roboflow.com/masood/wood-w4h97 |
-| rf-madera-planchas | descargada | CC BY 4.0 | https://universe.roboflow.com/rolos/wood_plank_final-69kb7 |
-| rf-madera-estibas | descargada | CC BY 4.0 | https://universe.roboflow.com/pallets-dzqdc/only-wood |
-| rf-madera-varios | omitida: el proyecto no tiene versiones publicadas | (se comprueba al descargar) | https://universe.roboflow.com/ttt4/wood-lvmwk |
-| rf-madera-defectos-kodytek | descargada | CC BY 4.0 | https://universe.roboflow.com/tim-4ijf0/wood-surface-defect-detection |
-| rf-madera-defectos-11 | descargada | CC BY 4.0 | https://universe.roboflow.com/alima-aife2/wood-defect-detection-dxzor |
-| rf-madera-defectos-3 | omitida: el proyecto no tiene versiones publicadas | (se comprueba al descargar) | https://universe.roboflow.com/wood-detection-omca6/detect-defect-of-wooden |
-| rf-madera-defectos-5 | descargada | CC BY 4.0 | https://universe.roboflow.com/detect-detect/new-defects-in-wood |
-| rf-oxido-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/qp-55vgp/rust-2-qtreu |
-| rf-oxido-2 | descargada | CC BY 4.0 | https://universe.roboflow.com/personal-iisqb/rust-detection-xj8bu |
-| rf-oxido-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/workspacesteijn/rust-hbbwg |
-| rf-abolladuras-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/mesors-workspace/dent-ggvqt |
-| rf-abolladuras-2 | descargada | CC BY 4.0 | https://universe.roboflow.com/roboflow-demo/car-dents-and-scratches |
-| rf-tela-defectos-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/fabric-defect-detection-jq7gf/fabric-defect-detection-v2 |
-| rf-tela-defectos-2 | omitida: el proyecto no tiene versiones publicadas | (se comprueba al descargar) | https://universe.roboflow.com/fabric-defect-detection-6xxtk/fabric-data-set-eojqe |
-| rf-tela-defectos-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/datasetannot/fabric-defect-detection-lfyix |
-| rf-tela-limpia | omitida: el proyecto no tiene versiones publicadas | (se comprueba al descargar) | https://universe.roboflow.com/textile-defects/defect-free-fabric2 |
-| rf-cajas-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/bekarys0504-gmail-com/damaged_box |
-| rf-cajas-2 | omitida: el proyecto no tiene versiones publicadas | (se comprueba al descargar) | https://universe.roboflow.com/mostafa-msp/damaged-package-detection-p4hiu |
-| rf-cajas-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/project-33xgh/damaged-box-auh4b |
-| rf-cajas-4 | descargada | CC BY 4.0 | https://universe.roboflow.com/robot-arm-xlohb/boxcondition |
-| rf-vidrio-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/focus/detect_broken_glass |
-| rf-vidrio-2 | descargada | CC BY 4.0 | https://universe.roboflow.com/nhabduls-workspace/cracked-glass-detection |
-| rf-vidrio-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/glassdefectsdetection/glassdefectscheckingmodel |
-| garbage-v2 | descargada | MIT | https://www.kaggle.com/datasets/sumn2u/garbage-classification-v2 |
+| garbage-v2 | omitida: faltan las credenciales de Kaggle (KAGGLE_API_TOKEN o kaggle.json) | (se comprueba al descargar) | https://www.kaggle.com/datasets/sumn2u/garbage-classification-v2 |
 | rf-cajas-carton | descargada | CC BY 4.0 | https://universe.roboflow.com/ece4191-xcxot/cardboard-box-detection-mxqjh |
 | rf-plastico-residuos | descargada | CC BY 4.0 | https://universe.roboflow.com/edwin-daza-saavedra-s-workspace/plastic-waste-ag4eg |
 | rf-botellas-plastico | descargada | CC BY 4.0 | https://universe.roboflow.com/fyp-li8zz/plastic-bottle-2.0 |
@@ -97,14 +75,42 @@ Se marca como validada si hay ≥ 30 fotos de prueba con defectos etiquetados y
 | rf-ropa | descargada | CC BY 4.0 | https://universe.roboflow.com/grad-proj-nakmt/clothes-liagy |
 | rf-tablas-madera | descargada | CC BY 4.0 | https://universe.roboflow.com/davids-workspace-rc8oh/lumber-board-ident |
 | rf-residuos-electronicos | descargada | CC BY 4.0 | https://universe.roboflow.com/mehmet-wswds/e-waste-qmxtt |
+| rf-varillas-countbars | descargada | CC BY 4.0 | https://universe.roboflow.com/steel-bar/countbars |
+| rf-varillas-conteo | descargada | CC BY 4.0 | https://universe.roboflow.com/aparna-duggirala-2i6kx/steel-bar-counting-9eetj |
+| rf-varillas-tmt | descargada | CC BY 4.0 | https://universe.roboflow.com/tmt-rod/counting-steel-bars |
+| rf-varillas-deteccion | omitida: el proyecto no tiene versiones publicadas | (se comprueba al descargar) | https://universe.roboflow.com/steel-sense/rebar-detection-ipemn |
+| rf-madera-conteo | descargada | CC BY 4.0 | https://universe.roboflow.com/ivone-riza-ugbmf/lumber-counting |
+| rf-obra-mixta | descargada | CC BY 4.0 | https://universe.roboflow.com/hackaton-qgozg/custom_final_dataset_copy |
+| rf-obra-madera-varilla | descargada | CC BY 4.0 | https://universe.roboflow.com/bricks10/timber_brick_log_rebar |
+| coco-no-material | descargada | CC BY 2.0 / sin restricciones (se filtra foto a foto) | https://cocodataset.org |
+| propias | omitida: todavía no hay fotos propias | propia (fotos de los usuarios de REVALORA) |  |
+| rf-madera-tablas | descargada | CC BY 4.0 | https://universe.roboflow.com/masood/wood-w4h97 |
+| rf-madera-planchas | descargada | CC BY 4.0 | https://universe.roboflow.com/rolos/wood_plank_final-69kb7 |
+| rf-madera-estibas | descargada | CC BY 4.0 | https://universe.roboflow.com/pallets-dzqdc/only-wood |
+| rf-madera-defectos-kodytek | descargada | CC BY 4.0 | https://universe.roboflow.com/tim-4ijf0/wood-surface-defect-detection |
+| rf-madera-defectos-11 | descargada | CC BY 4.0 | https://universe.roboflow.com/alima-aife2/wood-defect-detection-dxzor |
+| rf-madera-defectos-5 | descargada | CC BY 4.0 | https://universe.roboflow.com/detect-detect/new-defects-in-wood |
 | rf-madera-defectos-10k | descargada | CC BY 4.0 | https://universe.roboflow.com/ye-csex5/wood-defect-o06zb |
 | rf-madera-defectos-tarumt | descargada | CC BY 4.0 | https://universe.roboflow.com/tunku-abdul-rahman-university-of-management-and-technology-ttt1n/wood-defects-8lcdv |
 | rf-madera-sana | descargada | CC BY 4.0 | https://universe.roboflow.com/school-mys25/wood-7ao2p |
 | rf-madera-estibas-agujeros | descargada | CC BY 4.0 | https://universe.roboflow.com/deep-learning-exxwi/wood-pallet-deep-learning |
+| rf-oxido-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/qp-55vgp/rust-2-qtreu |
+| rf-oxido-2 | descargada | CC BY 4.0 | https://universe.roboflow.com/personal-iisqb/rust-detection-xj8bu |
+| rf-oxido-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/workspacesteijn/rust-hbbwg |
 | rf-oxido-niveles | descargada | CC BY 4.0 | https://universe.roboflow.com/zenil/rust-detection-4cr1b |
 | rf-metal-superficie | descargada | CC BY 4.0 | https://universe.roboflow.com/university-of-bristol-kxv0a/metal-surface-7ng08 |
 | rf-metal-superficie-2 | descargada | CC BY 4.0 | https://universe.roboflow.com/vision-oi1kw/metal-surface-defect-detection-n7x0l |
+| rf-abolladuras-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/mesors-workspace/dent-ggvqt |
+| rf-abolladuras-2 | descargada | CC BY 4.0 | https://universe.roboflow.com/roboflow-demo/car-dents-and-scratches |
+| rf-tela-defectos-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/fabric-defect-detection-jq7gf/fabric-defect-detection-v2 |
+| rf-tela-defectos-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/datasetannot/fabric-defect-detection-lfyix |
 | rf-tela-agujeros | descargada | CC BY 4.0 | https://universe.roboflow.com/textile-defect-detection-plje9/textile-defect-detection-yvgmo |
 | rf-tela-agujeros-manchas | descargada | CC BY 4.0 | https://universe.roboflow.com/iicl-31hjn/textile-ofqd1 |
 | rf-tela-manchas-arrugas | descargada | CC BY 4.0 | https://universe.roboflow.com/zco/textile-defect-detection-ixt2r |
+| rf-cajas-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/bekarys0504-gmail-com/damaged_box |
+| rf-cajas-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/project-33xgh/damaged-box-auh4b |
+| rf-cajas-4 | descargada | CC BY 4.0 | https://universe.roboflow.com/robot-arm-xlohb/boxcondition |
 | rf-cajas-5 | descargada | CC BY 4.0 | https://universe.roboflow.com/project-33xgh/damaged-box-detection |
+| rf-vidrio-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/focus/detect_broken_glass |
+| rf-vidrio-2 | descargada | CC BY 4.0 | https://universe.roboflow.com/nhabduls-workspace/cracked-glass-detection |
+| rf-vidrio-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/glassdefectsdetection/glassdefectscheckingmodel |

modelos_entrenados/material_estado/metadatos.json

@@ -7,7 +7,8 @@
     "metal",
     "vidrio",
     "papel",
-    "otro"
+    "otro",
+    "no_material"
   ],
   "defectos": [
     "grieta",
@@ -22,8 +23,8 @@
   "tamano_imagen": 384,
   "dim_caracteristicas": 1536,
   "dim_oculta": 512,
-  "backbone_origen": "facebook/dinov3-convnext-tiny-pretrain-lvd1689m (congelado)",
-  "temperatura_material": 0.5042169094085693,
+  "backbone_origen": "facebook/dinov3-convnext-tiny-pretrain-lvd1689m (ajuste fino completo)",
+  "temperatura_material": 0.5579002499580383,
   "defectos_entrenados": [
     "grieta",
     "rotura",
@@ -41,66 +42,72 @@
     "vidrio"
   ],
   "nombre": "convnext-dinov3",
-  "version": "1",
+  "version": "2",
   "evaluacion": {
-    "fecha": "2026-11-09",
+    "fecha": "2026-11-11",
     "imagenes": {
-      "entrenamiento": 26934,
-      "validacion": 3318,
-      "prueba": 3364
+      "entrenamiento": 28592,
+      "validacion": 3464,
+      "prueba": 3566
     },
     "material": {
-      "exactitud": 0.9868589743589744,
-      "f1_macro": 0.9868368688769972,
-      "confianza_media": 0.9862286448478699,
+      "exactitud": 0.985783424077435,
+      "f1_macro": 0.9854921599356941,
+      "confianza_media": 0.9915129542350769,
       "por_clase": {
         "madera": {
-          "precision": 0.9953161592505855,
-          "recall": 0.9976525821596244,
-          "f1": 0.9964830011723329,
-          "muestras": 426
+          "precision": 1.0,
+          "recall": 0.9975609756097561,
+          "f1": 0.9987789987789988,
+          "muestras": 410
         },
         "carton": {
-          "precision": 0.9905362776025236,
-          "recall": 0.9936708860759493,
-          "f1": 0.9921011058451816,
-          "muestras": 316
+          "precision": 0.9736842105263158,
+          "recall": 0.9966329966329966,
+          "f1": 0.9850249584026622,
+          "muestras": 297
         },
         "plastico": {
-          "precision": 0.978319783197832,
-          "recall": 0.9756756756756757,
-          "f1": 0.9769959404600812,
-          "muestras": 370
+          "precision": 0.9880668257756563,
+          "recall": 0.9473684210526315,
+          "f1": 0.9672897196261683,
+          "muestras": 437
         },
         "tela": {
-          "precision": 0.9925742574257426,
-          "recall": 1.0,
-          "f1": 0.9962732919254659,
-          "muestras": 401
+          "precision": 0.9973474801061007,
+          "recall": 0.9868766404199475,
+          "f1": 0.9920844327176781,
+          "muestras": 381
         },
         "metal": {
-          "precision": 0.995,
-          "recall": 0.9802955665024631,
-          "f1": 0.9875930521091811,
-          "muestras": 406
+          "precision": 0.9904306220095693,
+          "recall": 0.9928057553956835,
+          "f1": 0.9916167664670659,
+          "muestras": 417
         },
         "vidrio": {
-          "precision": 0.9742388758782201,
-          "recall": 0.9881235154394299,
-          "f1": 0.9811320754716981,
-          "muestras": 421
+          "precision": 0.9704545454545455,
+          "recall": 0.9976635514018691,
+          "f1": 0.9838709677419355,
+          "muestras": 428
         },
         "papel": {
-          "precision": 0.9861431870669746,
-          "recall": 0.977116704805492,
-          "f1": 0.9816091954022989,
-          "muestras": 437
+          "precision": 0.9903147699757869,
+          "recall": 0.9784688995215312,
+          "f1": 0.9843561973525873,
+          "muestras": 418
         },
         "otro": {
-          "precision": 0.9825072886297376,
-          "recall": 0.9825072886297376,
-          "f1": 0.9825072886297376,
-          "muestras": 343
+          "precision": 0.9666666666666667,
+          "recall": 0.9854368932038835,
+          "f1": 0.9759615384615384,
+          "muestras": 206
+        },
+        "no_material": {
+          "precision": 0.9841772151898734,
+          "recall": 0.9967948717948718,
+          "f1": 0.9904458598726115,
+          "muestras": 312
         }
       },
       "matriz_confusion": {
@@ -112,11 +119,13 @@
           "metal",
           "vidrio",
           "papel",
-          "otro"
+          "otro",
+          "no_material"
         ],
         "valores": [
           [
-            425,
+            409,
+            0,
             0,
             0,
             1,
@@ -127,153 +136,171 @@
           ],
           [
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+            296,
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         ]
       }
     },
     "defectos": {
       "grieta": {
-        "conocidos": 409,
-        "positivos": 173,
-        "auc": 0.8956598412853923,
-        "precision_media": 0.8782687577272618,
-        "exactitud_0_5": 0.8215158924205379
+        "conocidos": 387,
+        "positivos": 155,
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         "auc_por_defecto": {
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       "plastico": {
@@ -283,25 +310,25 @@
         "validado": false
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       "tela": {
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         "auc_por_defecto": {
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       "vidrio": {
@@ -323,8 +350,14 @@
         "auc_por_defecto": {},
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     },
-    "epocas": 84
+    "epocas": 12
   }
 }

modelos_entrenados/material_estado/modelo.safetensors

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