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  1. modelos_entrenados/.gitkeep +0 -0
  2. modelos_entrenados/conteo/informe.md +36 -0
  3. modelos_entrenados/conteo/umbral.json +1 -0
  4. modelos_entrenados/material_estado/config_backbone.json +40 -0
  5. modelos_entrenados/material_estado/historial.json +422 -0
  6. modelos_entrenados/material_estado/informe.md +110 -0
  7. modelos_entrenados/material_estado/metadatos.json +330 -0
  8. modelos_entrenados/material_estado/modelo.safetensors +3 -0
  9. modelos_entrenados/material_estado_17fuentes/config_backbone.json +40 -0
  10. modelos_entrenados/material_estado_17fuentes/historial.json +347 -0
  11. modelos_entrenados/material_estado_17fuentes/informe.md +110 -0
  12. modelos_entrenados/material_estado_17fuentes/metadatos.json +320 -0
  13. modelos_entrenados/material_estado_17fuentes/modelo.safetensors +3 -0
  14. modelos_entrenados/material_estado_47fuentes/config_backbone.json +40 -0
  15. modelos_entrenados/material_estado_47fuentes/historial.json +422 -0
  16. modelos_entrenados/material_estado_47fuentes/informe.md +110 -0
  17. modelos_entrenados/material_estado_47fuentes/metadatos.json +330 -0
  18. modelos_entrenados/material_estado_47fuentes/modelo.safetensors +3 -0
modelos_entrenados/.gitkeep ADDED
File without changes
modelos_entrenados/conteo/informe.md ADDED
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+ # Evaluación del conteo (SAM 3, sin entrenar)
2
+
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+ - Fotos: 150 · Tiempo medio por foto (CPU): 91.1 s
4
+ - **Umbral elegido: 0.55** (menor error medio)
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+
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+ | Material | Fotos | Error medio (piezas) | Exacto | ±1 pieza |
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+ |---|---|---|---|---|
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+ | carton | 25 | 1.20 | 36% | 80% |
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+ | plastico | 25 | 0.68 | 52% | 84% |
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+ | vidrio | 25 | 1.32 | 40% | 80% |
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+ | metal | 25 | 2.36 | 40% | 72% |
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+ | tela | 25 | 0.56 | 48% | 96% |
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+ | madera | 25 | 8.80 | 4% | 16% |
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+ | **Total** | 150 | 2.49 | 37% | 71% |
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+
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+ Error medio según el umbral:
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+
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+ | Umbral | Error medio |
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+ |---|---|
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+ | 0.1 | 15.39 |
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+ | 0.15 | 8.19 |
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+ | 0.2 | 5.35 |
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+ | 0.25 | 4.03 |
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+ | 0.3 | 3.20 |
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+ | 0.7 | 2.81 |
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+ | 0.8 | 3.51 |
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+ | 0.9 | 4.62 |
modelos_entrenados/conteo/umbral.json ADDED
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+ {"umbral": 0.55}
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+ {
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modelos_entrenados/material_estado/informe.md ADDED
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1
+ # Informe del modelo de material y estado
2
+
3
+ - Fecha: 2026-11-09
4
+ - Backbone: `facebook/dinov3-convnext-tiny-pretrain-lvd1689m (congelado)` + 2 cabezas (material y defectos)
5
+ - Tamaño de imagen: 384 px · Temperatura: 0.504
6
+ - Imágenes: 26934 entrenamiento · 3318 validación · 3364 prueba
7
+
8
+ ## Material (conjunto de prueba)
9
+
10
+ **Exactitud: 98.7 %** · F1 macro: 98.7 %
11
+
12
+ | Material | Precisión | Recall | F1 | Fotos de prueba |
13
+ |---|---|---|---|---|
14
+ | madera | 99.5 % | 99.8 % | 99.6 % | 426 |
15
+ | carton | 99.1 % | 99.4 % | 99.2 % | 316 |
16
+ | plastico | 97.8 % | 97.6 % | 97.7 % | 370 |
17
+ | tela | 99.3 % | 100.0 % | 99.6 % | 401 |
18
+ | metal | 99.5 % | 98.0 % | 98.8 % | 406 |
19
+ | vidrio | 97.4 % | 98.8 % | 98.1 % | 421 |
20
+ | papel | 98.6 % | 97.7 % | 98.2 % | 437 |
21
+ | otro | 98.3 % | 98.3 % | 98.3 % | 343 |
22
+
23
+ ## Defectos (conjunto de prueba)
24
+
25
+ AUC: probabilidad de que una foto con el defecto puntúe más que una sin él (50 % = azar, 100 % = perfecto).
26
+
27
+ Solo se usan en la nota los defectos con ≥ 30 ejemplos con y sin el defecto en entrenamiento.
28
+
29
+ | Defecto | Se usa | AUC | Precisión media | Positivos / conocidos (prueba) |
30
+ |---|---|---|---|---|
31
+ | grieta | sí | 89.6 % | 87.8 % | 173 / 409 |
32
+ | rotura | sí | 98.0 % | 96.3 % | 177 / 699 |
33
+ | oxido | sí | 100.0 % | 100.0 % | 161 / 354 |
34
+ | mancha | sí | 86.1 % | 78.8 % | 221 / 632 |
35
+ | podredumbre | no | — | — | 0 / 193 |
36
+ | deformacion | sí | 96.0 % | 95.9 % | 402 / 910 |
37
+ | rayon | sí | 79.1 % | 73.4 % | 65 / 138 |
38
+ | nudo | sí | 96.6 % | 98.6 % | 343 / 491 |
39
+
40
+ ## Nota de estado por material
41
+
42
+ Se marca como validada si hay ≥ 30 fotos de prueba con defectos etiquetados y la AUC media de sus defectos es ≥ 80%. Si no, la API devuelve `fiable: false`.
43
+
44
+ | Material | Validada | AUC media | Fotos | AUC por defecto |
45
+ |---|---|---|---|---|
46
+ | madera | sí | 88.5 % | 403 | grieta 89.2 %, rotura 96.4 %, mancha 72.7 %, nudo 95.8 % |
47
+ | carton | sí | 98.8 % | 296 | deformacion 98.8 % |
48
+ | plastico | no | — | 0 | — |
49
+ | tela | sí | 95.4 % | 299 | rotura 97.0 %, mancha 93.1 %, deformacion 96.1 % |
50
+ | metal | sí | 90.6 % | 481 | rotura 100.0 %, oxido 100.0 %, mancha 80.2 %, deformacion 93.8 %, rayon 79.1 % |
51
+ | vidrio | sí | 100.0 % | 88 | rotura 100.0 % |
52
+ | papel | no | — | 70 | — |
53
+ | otro | no | — | 11 | — |
54
+
55
+ ## Fuentes de datos
56
+
57
+ | Fuente | Estado | Licencia | Web |
58
+ |---|---|---|---|
59
+ | trashnet | descargada | MIT | https://huggingface.co/datasets/garythung/trashnet |
60
+ | realwaste | descargada | CC BY 4.0 | https://archive.ics.uci.edu/dataset/908/realwaste |
61
+ | rf-madera-tablas | descargada | CC BY 4.0 | https://universe.roboflow.com/masood/wood-w4h97 |
62
+ | rf-madera-planchas | descargada | CC BY 4.0 | https://universe.roboflow.com/rolos/wood_plank_final-69kb7 |
63
+ | rf-madera-estibas | descargada | CC BY 4.0 | https://universe.roboflow.com/pallets-dzqdc/only-wood |
64
+ | rf-madera-varios | omitida: el proyecto no tiene versiones publicadas | (se comprueba al descargar) | https://universe.roboflow.com/ttt4/wood-lvmwk |
65
+ | rf-madera-defectos-kodytek | descargada | CC BY 4.0 | https://universe.roboflow.com/tim-4ijf0/wood-surface-defect-detection |
66
+ | rf-madera-defectos-11 | descargada | CC BY 4.0 | https://universe.roboflow.com/alima-aife2/wood-defect-detection-dxzor |
67
+ | 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 |
68
+ | rf-madera-defectos-5 | descargada | CC BY 4.0 | https://universe.roboflow.com/detect-detect/new-defects-in-wood |
69
+ | rf-oxido-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/qp-55vgp/rust-2-qtreu |
70
+ | rf-oxido-2 | descargada | CC BY 4.0 | https://universe.roboflow.com/personal-iisqb/rust-detection-xj8bu |
71
+ | rf-oxido-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/workspacesteijn/rust-hbbwg |
72
+ | rf-abolladuras-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/mesors-workspace/dent-ggvqt |
73
+ | rf-abolladuras-2 | descargada | CC BY 4.0 | https://universe.roboflow.com/roboflow-demo/car-dents-and-scratches |
74
+ | rf-tela-defectos-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/fabric-defect-detection-jq7gf/fabric-defect-detection-v2 |
75
+ | 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 |
76
+ | rf-tela-defectos-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/datasetannot/fabric-defect-detection-lfyix |
77
+ | rf-tela-limpia | omitida: el proyecto no tiene versiones publicadas | (se comprueba al descargar) | https://universe.roboflow.com/textile-defects/defect-free-fabric2 |
78
+ | rf-cajas-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/bekarys0504-gmail-com/damaged_box |
79
+ | rf-cajas-2 | omitida: el proyecto no tiene versiones publicadas | (se comprueba al descargar) | https://universe.roboflow.com/mostafa-msp/damaged-package-detection-p4hiu |
80
+ | rf-cajas-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/project-33xgh/damaged-box-auh4b |
81
+ | rf-cajas-4 | descargada | CC BY 4.0 | https://universe.roboflow.com/robot-arm-xlohb/boxcondition |
82
+ | rf-vidrio-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/focus/detect_broken_glass |
83
+ | rf-vidrio-2 | descargada | CC BY 4.0 | https://universe.roboflow.com/nhabduls-workspace/cracked-glass-detection |
84
+ | rf-vidrio-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/glassdefectsdetection/glassdefectscheckingmodel |
85
+ | garbage-v2 | descargada | MIT | https://www.kaggle.com/datasets/sumn2u/garbage-classification-v2 |
86
+ | rf-cajas-carton | descargada | CC BY 4.0 | https://universe.roboflow.com/ece4191-xcxot/cardboard-box-detection-mxqjh |
87
+ | rf-plastico-residuos | descargada | CC BY 4.0 | https://universe.roboflow.com/edwin-daza-saavedra-s-workspace/plastic-waste-ag4eg |
88
+ | rf-botellas-plastico | descargada | CC BY 4.0 | https://universe.roboflow.com/fyp-li8zz/plastic-bottle-2.0 |
89
+ | rf-frascos-vidrio | descargada | CC BY 4.0 | https://universe.roboflow.com/gdp27/glass-jars |
90
+ | rf-vidrio-residuos | descargada | CC BY 4.0 | https://universe.roboflow.com/waste-seregation/glass-detection-1jobo |
91
+ | rf-latas | descargada | CC BY 4.0 | https://universe.roboflow.com/albaath2/can-o9wiu |
92
+ | rf-tubos | descargada | CC BY 4.0 | https://universe.roboflow.com/john-tai-fa0rl/pipe-detector3 |
93
+ | rf-chatarra | descargada | CC BY 4.0 | https://universe.roboflow.com/moses-orjyo/steel-scrap-sowop |
94
+ | rf-papel | descargada | CC BY 4.0 | https://universe.roboflow.com/trashdetection-bhjmn/paper-2jpbv |
95
+ | rf-papel-periodico | descargada | CC BY 4.0 | https://universe.roboflow.com/mmt-kcsv2/paper-nitaa |
96
+ | rf-materiales-mixtos | descargada | CC BY 4.0 | https://universe.roboflow.com/material-identification/cardboard-1hdgz |
97
+ | rf-ropa | descargada | CC BY 4.0 | https://universe.roboflow.com/grad-proj-nakmt/clothes-liagy |
98
+ | rf-tablas-madera | descargada | CC BY 4.0 | https://universe.roboflow.com/davids-workspace-rc8oh/lumber-board-ident |
99
+ | rf-residuos-electronicos | descargada | CC BY 4.0 | https://universe.roboflow.com/mehmet-wswds/e-waste-qmxtt |
100
+ | rf-madera-defectos-10k | descargada | CC BY 4.0 | https://universe.roboflow.com/ye-csex5/wood-defect-o06zb |
101
+ | 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 |
102
+ | rf-madera-sana | descargada | CC BY 4.0 | https://universe.roboflow.com/school-mys25/wood-7ao2p |
103
+ | rf-madera-estibas-agujeros | descargada | CC BY 4.0 | https://universe.roboflow.com/deep-learning-exxwi/wood-pallet-deep-learning |
104
+ | rf-oxido-niveles | descargada | CC BY 4.0 | https://universe.roboflow.com/zenil/rust-detection-4cr1b |
105
+ | rf-metal-superficie | descargada | CC BY 4.0 | https://universe.roboflow.com/university-of-bristol-kxv0a/metal-surface-7ng08 |
106
+ | rf-metal-superficie-2 | descargada | CC BY 4.0 | https://universe.roboflow.com/vision-oi1kw/metal-surface-defect-detection-n7x0l |
107
+ | rf-tela-agujeros | descargada | CC BY 4.0 | https://universe.roboflow.com/textile-defect-detection-plje9/textile-defect-detection-yvgmo |
108
+ | rf-tela-agujeros-manchas | descargada | CC BY 4.0 | https://universe.roboflow.com/iicl-31hjn/textile-ofqd1 |
109
+ | rf-tela-manchas-arrugas | descargada | CC BY 4.0 | https://universe.roboflow.com/zco/textile-defect-detection-ixt2r |
110
+ | rf-cajas-5 | descargada | CC BY 4.0 | https://universe.roboflow.com/project-33xgh/damaged-box-detection |
modelos_entrenados/material_estado/metadatos.json ADDED
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1
+ {
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+ "materiales": [
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+ "madera",
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+ "carton",
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+ "plastico",
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+ "tela",
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+ "metal",
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+ "vidrio",
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+ "defectos_entrenados": [
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+ "rayon",
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+ "madera",
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+ "carton",
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+ "tela",
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+ "metal",
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+ "nombre": "convnext-dinov3",
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modelos_entrenados/material_estado/modelo.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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modelos_entrenados/material_estado_17fuentes/informe.md ADDED
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1
+ # Informe del modelo de material y estado
2
+
3
+ - Fecha: 2026-11-08
4
+ - Backbone: `facebook/dinov3-convnext-tiny-pretrain-lvd1689m (congelado)` + 2 cabezas (material y defectos)
5
+ - Tamaño de imagen: 384 px · Temperatura: 0.500
6
+ - Imágenes: 14444 entrenamiento · 1750 validación · 1680 prueba
7
+
8
+ ## Material (conjunto de prueba)
9
+
10
+ **Exactitud: 98.0 %** · F1 macro: 96.7 %
11
+
12
+ | Material | Precisión | Recall | F1 | Fotos de prueba |
13
+ |---|---|---|---|---|
14
+ | madera | 99.7 % | 99.4 % | 99.6 % | 356 |
15
+ | carton | 99.1 % | 99.1 % | 99.1 % | 339 |
16
+ | plastico | 93.2 % | 95.8 % | 94.5 % | 143 |
17
+ | tela | 99.2 % | 99.7 % | 99.5 % | 380 |
18
+ | metal | 93.9 % | 94.7 % | 94.3 % | 113 |
19
+ | vidrio | 98.2 % | 97.6 % | 97.9 % | 168 |
20
+ | papel | 96.8 % | 96.8 % | 96.8 % | 125 |
21
+ | otro | 96.1 % | 87.5 % | 91.6 % | 56 |
22
+
23
+ ## Defectos (conjunto de prueba)
24
+
25
+ AUC: probabilidad de que una foto con el defecto puntúe más que una sin él (50 % = azar, 100 % = perfecto).
26
+
27
+ Solo se usan en la nota los defectos con ≥ 30 ejemplos con y sin el defecto en entrenamiento.
28
+
29
+ | Defecto | Se usa | AUC | Precisión media | Positivos / conocidos (prueba) |
30
+ |---|---|---|---|---|
31
+ | grieta | sí | 96.4 % | 94.9 % | 135 / 331 |
32
+ | rotura | sí | 99.2 % | 98.0 % | 177 / 758 |
33
+ | oxido | no | — | — | 0 / 229 |
34
+ | mancha | sí | 98.4 % | 95.8 % | 68 / 311 |
35
+ | podredumbre | no | — | — | 0 / 229 |
36
+ | deformacion | sí | 98.3 % | 98.7 % | 166 / 314 |
37
+ | rayon | no | — | — | 0 / 0 |
38
+ | nudo | sí | 99.4 % | 99.6 % | 329 / 603 |
39
+
40
+ ## Nota de estado por material
41
+
42
+ Se marca como validada si hay ≥ 30 fotos de prueba con defectos etiquetados y la AUC media de sus defectos es ≥ 80%. Si no, la API devuelve `fiable: false`.
43
+
44
+ | Material | Validada | AUC media | Fotos | AUC por defecto |
45
+ |---|---|---|---|---|
46
+ | madera | sí | 91.8 % | 325 | grieta 96.3 %, rotura 98.4 %, mancha 73.0 %, nudo 99.6 % |
47
+ | carton | sí | 98.3 % | 314 | deformacion 98.3 % |
48
+ | plastico | no | — | 0 | — |
49
+ | tela | sí | 99.2 % | 346 | rotura 99.0 %, mancha 99.5 % |
50
+ | metal | no | — | 39 | — |
51
+ | vidrio | sí | 100.0 % | 88 | rotura 100.0 % |
52
+ | papel | no | — | 70 | — |
53
+ | otro | no | — | 11 | — |
54
+
55
+ ## Fuentes de datos
56
+
57
+ | Fuente | Estado | Licencia | Web |
58
+ |---|---|---|---|
59
+ | trashnet | descargada | MIT | https://huggingface.co/datasets/garythung/trashnet |
60
+ | realwaste | descargada | CC BY 4.0 | https://archive.ics.uci.edu/dataset/908/realwaste |
61
+ | rf-madera-tablas | descargada | CC BY 4.0 | https://universe.roboflow.com/masood/wood-w4h97 |
62
+ | rf-madera-planchas | descargada | CC BY 4.0 | https://universe.roboflow.com/rolos/wood_plank_final-69kb7 |
63
+ | rf-madera-estibas | descargada | CC BY 4.0 | https://universe.roboflow.com/pallets-dzqdc/only-wood |
64
+ | rf-madera-varios | omitida: el proyecto no tiene versiones publicadas | (se comprueba al descargar) | https://universe.roboflow.com/ttt4/wood-lvmwk |
65
+ | rf-madera-defectos-kodytek | descargada | CC BY 4.0 | https://universe.roboflow.com/tim-4ijf0/wood-surface-defect-detection |
66
+ | rf-madera-defectos-11 | descargada | CC BY 4.0 | https://universe.roboflow.com/alima-aife2/wood-defect-detection-dxzor |
67
+ | 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 |
68
+ | rf-madera-defectos-5 | descargada | CC BY 4.0 | https://universe.roboflow.com/detect-detect/new-defects-in-wood |
69
+ | rf-oxido-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/qp-55vgp/rust-2-qtreu |
70
+ | rf-oxido-2 | descargada | CC BY 4.0 | https://universe.roboflow.com/personal-iisqb/rust-detection-xj8bu |
71
+ | rf-oxido-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/workspacesteijn/rust-hbbwg |
72
+ | rf-abolladuras-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/mesors-workspace/dent-ggvqt |
73
+ | rf-abolladuras-2 | descargada | CC BY 4.0 | https://universe.roboflow.com/roboflow-demo/car-dents-and-scratches |
74
+ | rf-tela-defectos-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/fabric-defect-detection-jq7gf/fabric-defect-detection-v2 |
75
+ | 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 |
76
+ | rf-tela-defectos-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/datasetannot/fabric-defect-detection-lfyix |
77
+ | rf-tela-limpia | omitida: el proyecto no tiene versiones publicadas | (se comprueba al descargar) | https://universe.roboflow.com/textile-defects/defect-free-fabric2 |
78
+ | rf-cajas-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/bekarys0504-gmail-com/damaged_box |
79
+ | rf-cajas-2 | omitida: el proyecto no tiene versiones publicadas | (se comprueba al descargar) | https://universe.roboflow.com/mostafa-msp/damaged-package-detection-p4hiu |
80
+ | rf-cajas-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/project-33xgh/damaged-box-auh4b |
81
+ | rf-cajas-4 | descargada | CC BY 4.0 | https://universe.roboflow.com/robot-arm-xlohb/boxcondition |
82
+ | rf-vidrio-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/focus/detect_broken_glass |
83
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modelos_entrenados/material_estado_47fuentes/informe.md ADDED
@@ -0,0 +1,110 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Informe del modelo de material y estado
2
+
3
+ - Fecha: 2026-11-09
4
+ - Backbone: `facebook/dinov3-convnext-tiny-pretrain-lvd1689m (congelado)` + 2 cabezas (material y defectos)
5
+ - Tamaño de imagen: 384 px · Temperatura: 0.504
6
+ - Imágenes: 26934 entrenamiento · 3318 validación · 3364 prueba
7
+
8
+ ## Material (conjunto de prueba)
9
+
10
+ **Exactitud: 98.7 %** · F1 macro: 98.7 %
11
+
12
+ | Material | Precisión | Recall | F1 | Fotos de prueba |
13
+ |---|---|---|---|---|
14
+ | madera | 99.5 % | 99.8 % | 99.6 % | 426 |
15
+ | carton | 99.1 % | 99.4 % | 99.2 % | 316 |
16
+ | plastico | 97.8 % | 97.6 % | 97.7 % | 370 |
17
+ | tela | 99.3 % | 100.0 % | 99.6 % | 401 |
18
+ | metal | 99.5 % | 98.0 % | 98.8 % | 406 |
19
+ | vidrio | 97.4 % | 98.8 % | 98.1 % | 421 |
20
+ | papel | 98.6 % | 97.7 % | 98.2 % | 437 |
21
+ | otro | 98.3 % | 98.3 % | 98.3 % | 343 |
22
+
23
+ ## Defectos (conjunto de prueba)
24
+
25
+ AUC: probabilidad de que una foto con el defecto puntúe más que una sin él (50 % = azar, 100 % = perfecto).
26
+
27
+ Solo se usan en la nota los defectos con ≥ 30 ejemplos con y sin el defecto en entrenamiento.
28
+
29
+ | Defecto | Se usa | AUC | Precisión media | Positivos / conocidos (prueba) |
30
+ |---|---|---|---|---|
31
+ | grieta | sí | 89.6 % | 87.8 % | 173 / 409 |
32
+ | rotura | sí | 98.0 % | 96.3 % | 177 / 699 |
33
+ | oxido | sí | 100.0 % | 100.0 % | 161 / 354 |
34
+ | mancha | sí | 86.1 % | 78.8 % | 221 / 632 |
35
+ | podredumbre | no | — | — | 0 / 193 |
36
+ | deformacion | sí | 96.0 % | 95.9 % | 402 / 910 |
37
+ | rayon | sí | 79.1 % | 73.4 % | 65 / 138 |
38
+ | nudo | sí | 96.6 % | 98.6 % | 343 / 491 |
39
+
40
+ ## Nota de estado por material
41
+
42
+ Se marca como validada si hay ≥ 30 fotos de prueba con defectos etiquetados y la AUC media de sus defectos es ≥ 80%. Si no, la API devuelve `fiable: false`.
43
+
44
+ | Material | Validada | AUC media | Fotos | AUC por defecto |
45
+ |---|---|---|---|---|
46
+ | madera | sí | 88.5 % | 403 | grieta 89.2 %, rotura 96.4 %, mancha 72.7 %, nudo 95.8 % |
47
+ | carton | sí | 98.8 % | 296 | deformacion 98.8 % |
48
+ | plastico | no | — | 0 | — |
49
+ | tela | sí | 95.4 % | 299 | rotura 97.0 %, mancha 93.1 %, deformacion 96.1 % |
50
+ | metal | sí | 90.6 % | 481 | rotura 100.0 %, oxido 100.0 %, mancha 80.2 %, deformacion 93.8 %, rayon 79.1 % |
51
+ | vidrio | sí | 100.0 % | 88 | rotura 100.0 % |
52
+ | papel | no | — | 70 | — |
53
+ | otro | no | — | 11 | — |
54
+
55
+ ## Fuentes de datos
56
+
57
+ | Fuente | Estado | Licencia | Web |
58
+ |---|---|---|---|
59
+ | trashnet | descargada | MIT | https://huggingface.co/datasets/garythung/trashnet |
60
+ | realwaste | descargada | CC BY 4.0 | https://archive.ics.uci.edu/dataset/908/realwaste |
61
+ | rf-madera-tablas | descargada | CC BY 4.0 | https://universe.roboflow.com/masood/wood-w4h97 |
62
+ | rf-madera-planchas | descargada | CC BY 4.0 | https://universe.roboflow.com/rolos/wood_plank_final-69kb7 |
63
+ | rf-madera-estibas | descargada | CC BY 4.0 | https://universe.roboflow.com/pallets-dzqdc/only-wood |
64
+ | rf-madera-varios | omitida: el proyecto no tiene versiones publicadas | (se comprueba al descargar) | https://universe.roboflow.com/ttt4/wood-lvmwk |
65
+ | rf-madera-defectos-kodytek | descargada | CC BY 4.0 | https://universe.roboflow.com/tim-4ijf0/wood-surface-defect-detection |
66
+ | rf-madera-defectos-11 | descargada | CC BY 4.0 | https://universe.roboflow.com/alima-aife2/wood-defect-detection-dxzor |
67
+ | 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 |
68
+ | rf-madera-defectos-5 | descargada | CC BY 4.0 | https://universe.roboflow.com/detect-detect/new-defects-in-wood |
69
+ | rf-oxido-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/qp-55vgp/rust-2-qtreu |
70
+ | rf-oxido-2 | descargada | CC BY 4.0 | https://universe.roboflow.com/personal-iisqb/rust-detection-xj8bu |
71
+ | rf-oxido-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/workspacesteijn/rust-hbbwg |
72
+ | rf-abolladuras-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/mesors-workspace/dent-ggvqt |
73
+ | rf-abolladuras-2 | descargada | CC BY 4.0 | https://universe.roboflow.com/roboflow-demo/car-dents-and-scratches |
74
+ | rf-tela-defectos-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/fabric-defect-detection-jq7gf/fabric-defect-detection-v2 |
75
+ | 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 |
76
+ | rf-tela-defectos-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/datasetannot/fabric-defect-detection-lfyix |
77
+ | rf-tela-limpia | omitida: el proyecto no tiene versiones publicadas | (se comprueba al descargar) | https://universe.roboflow.com/textile-defects/defect-free-fabric2 |
78
+ | rf-cajas-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/bekarys0504-gmail-com/damaged_box |
79
+ | rf-cajas-2 | omitida: el proyecto no tiene versiones publicadas | (se comprueba al descargar) | https://universe.roboflow.com/mostafa-msp/damaged-package-detection-p4hiu |
80
+ | rf-cajas-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/project-33xgh/damaged-box-auh4b |
81
+ | rf-cajas-4 | descargada | CC BY 4.0 | https://universe.roboflow.com/robot-arm-xlohb/boxcondition |
82
+ | rf-vidrio-1 | descargada | CC BY 4.0 | https://universe.roboflow.com/focus/detect_broken_glass |
83
+ | rf-vidrio-2 | descargada | CC BY 4.0 | https://universe.roboflow.com/nhabduls-workspace/cracked-glass-detection |
84
+ | rf-vidrio-3 | descargada | CC BY 4.0 | https://universe.roboflow.com/glassdefectsdetection/glassdefectscheckingmodel |
85
+ | garbage-v2 | descargada | MIT | https://www.kaggle.com/datasets/sumn2u/garbage-classification-v2 |
86
+ | rf-cajas-carton | descargada | CC BY 4.0 | https://universe.roboflow.com/ece4191-xcxot/cardboard-box-detection-mxqjh |
87
+ | rf-plastico-residuos | descargada | CC BY 4.0 | https://universe.roboflow.com/edwin-daza-saavedra-s-workspace/plastic-waste-ag4eg |
88
+ | rf-botellas-plastico | descargada | CC BY 4.0 | https://universe.roboflow.com/fyp-li8zz/plastic-bottle-2.0 |
89
+ | rf-frascos-vidrio | descargada | CC BY 4.0 | https://universe.roboflow.com/gdp27/glass-jars |
90
+ | rf-vidrio-residuos | descargada | CC BY 4.0 | https://universe.roboflow.com/waste-seregation/glass-detection-1jobo |
91
+ | rf-latas | descargada | CC BY 4.0 | https://universe.roboflow.com/albaath2/can-o9wiu |
92
+ | rf-tubos | descargada | CC BY 4.0 | https://universe.roboflow.com/john-tai-fa0rl/pipe-detector3 |
93
+ | rf-chatarra | descargada | CC BY 4.0 | https://universe.roboflow.com/moses-orjyo/steel-scrap-sowop |
94
+ | rf-papel | descargada | CC BY 4.0 | https://universe.roboflow.com/trashdetection-bhjmn/paper-2jpbv |
95
+ | rf-papel-periodico | descargada | CC BY 4.0 | https://universe.roboflow.com/mmt-kcsv2/paper-nitaa |
96
+ | rf-materiales-mixtos | descargada | CC BY 4.0 | https://universe.roboflow.com/material-identification/cardboard-1hdgz |
97
+ | rf-ropa | descargada | CC BY 4.0 | https://universe.roboflow.com/grad-proj-nakmt/clothes-liagy |
98
+ | rf-tablas-madera | descargada | CC BY 4.0 | https://universe.roboflow.com/davids-workspace-rc8oh/lumber-board-ident |
99
+ | rf-residuos-electronicos | descargada | CC BY 4.0 | https://universe.roboflow.com/mehmet-wswds/e-waste-qmxtt |
100
+ | rf-madera-defectos-10k | descargada | CC BY 4.0 | https://universe.roboflow.com/ye-csex5/wood-defect-o06zb |
101
+ | 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 |
102
+ | rf-madera-sana | descargada | CC BY 4.0 | https://universe.roboflow.com/school-mys25/wood-7ao2p |
103
+ | rf-madera-estibas-agujeros | descargada | CC BY 4.0 | https://universe.roboflow.com/deep-learning-exxwi/wood-pallet-deep-learning |
104
+ | rf-oxido-niveles | descargada | CC BY 4.0 | https://universe.roboflow.com/zenil/rust-detection-4cr1b |
105
+ | rf-metal-superficie | descargada | CC BY 4.0 | https://universe.roboflow.com/university-of-bristol-kxv0a/metal-surface-7ng08 |
106
+ | rf-metal-superficie-2 | descargada | CC BY 4.0 | https://universe.roboflow.com/vision-oi1kw/metal-surface-defect-detection-n7x0l |
107
+ | rf-tela-agujeros | descargada | CC BY 4.0 | https://universe.roboflow.com/textile-defect-detection-plje9/textile-defect-detection-yvgmo |
108
+ | rf-tela-agujeros-manchas | descargada | CC BY 4.0 | https://universe.roboflow.com/iicl-31hjn/textile-ofqd1 |
109
+ | rf-tela-manchas-arrugas | descargada | CC BY 4.0 | https://universe.roboflow.com/zco/textile-defect-detection-ixt2r |
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+ | rf-cajas-5 | descargada | CC BY 4.0 | https://universe.roboflow.com/project-33xgh/damaged-box-detection |
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