update

docs/task/cni/results/facture_decisions.csv

@@ -0,0 +1,79 @@
+filename,deqa_score,label,decision_f1,decision_acc,decision_prec,decision_rec
+image (9)_0.jpg,3.9,High,implement,implement,implement,implement
+ITE55351156_Passeport_Completed_0.jpg,3.9,High,implement,implement,implement,implement
+image (3)_0.jpg,3.8,High,implement,implement,reject,implement
+image (18)_1.jpg,3.8,High,implement,implement,reject,implement
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+image (2)_0.jpg,3.8,High,implement,implement,reject,implement
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+image (23)_0.jpg,3.7,High,implement,implement,reject,implement
+image (21)_0.jpg,3.7,High,implement,implement,reject,implement
+image (15)_0.jpg,3.7,High,implement,implement,reject,implement
+ITE55354550_20250728_113359_Completed_0.jpg,3.7,High,implement,implement,reject,implement
+image (6)_2.jpg,3.7,High,implement,implement,reject,implement
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+ITE55348016_image_Completed_0.jpg,3.6,High,implement,implement,reject,implement
+image (6)_0.jpg,3.6,High,implement,implement,reject,implement
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+image (29)_0.jpg,3.6,High,implement,implement,reject,implement
+image (17)_0.jpg,3.6,High,implement,implement,reject,implement
+ITE55340822_passeport_pascal_2028_Completed_0.jpg,3.5,High,implement,implement,reject,implement
+image (27)_0.jpg,3.5,High,implement,implement,reject,implement
+image (18)_0.jpg,3.5,High,implement,implement,reject,implement
+image (24)_0.jpg,3.5,High,implement,implement,reject,implement
+image (12)_0.jpg,3.5,High,implement,implement,reject,implement
+image (6)_1.jpg,3.5,High,implement,implement,reject,implement
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+image (7)_0.jpg,3.4,High,implement,implement,reject,implement
+image (6)_3.jpg,3.4,Low,implement,implement,reject,implement
+image (22)_2.jpg,3.4,High,implement,implement,reject,implement
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+image (30)_0.jpg,3.3,High,implement,implement,reject,implement
+image (12)_1.jpg,3.3,High,implement,implement,reject,implement
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+image (17)_1.jpg,3.2,High,implement,implement,reject,implement
+ITE55341677_2023_-_CNI_Recto-Verso_Hassina_0.jpg,3.2,High,implement,implement,reject,implement
+image (22)_0.jpg,3.2,High,implement,implement,reject,implement
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+image (16)_0.jpg,3.1,High,implement,implement,reject,implement
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+image (1)_0.jpg,3.0,High,implement,implement,reject,implement
+image (22)_3.jpg,3.0,High,implement,implement,reject,implement
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+image (28)_0.jpg,3.0,High,implement,implement,reject,implement
+image (6)_4.jpg,2.9,High,implement,implement,reject,implement
+image (22)_1.jpg,2.9,High,implement,implement,reject,implement
+image (4)_0.jpg,2.9,High,implement,implement,reject,implement
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+image (6)_5.jpg,2.8,High,implement,implement,reject,implement
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+image (8)_0.jpg,2.7,High,implement,implement,reject,implement
+image (31)_0.jpg,2.7,High,implement,implement,reject,implement
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+image (1)_1.jpg,2.7,High,implement,implement,reject,implement
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+image (14)_0.jpg,2.7,High,implement,implement,reject,implement
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+ITE55351128_PLI_INCONNU_-_ADH_625123_42296103_DMS_1.jpg,1.9,Low,reject,reject,reject,reject

docs/task/cni/results/facture_thresholds_summary.json

@@ -0,0 +1,50 @@
+{
+  "positive_definition": "HIGH when score >= threshold",
+  "best_thresholds": {
+    "f1": {
+      "threshold": 2.6,
+      "value": 0.8872180451127819,
+      "confusion": {
+        "TP": 59,
+        "FP": 15,
+        "FN": 0,
+        "TN": 4
+      }
+    },
+    "accuracy": {
+      "threshold": 2.6,
+      "value": 0.8076923076923077,
+      "confusion": {
+        "TP": 59,
+        "FP": 15,
+        "FN": 0,
+        "TN": 4
+      }
+    },
+    "precision": {
+      "threshold": 3.9,
+      "value": 1.0,
+      "confusion": {
+        "TP": 2,
+        "FP": 0,
+        "FN": 57,
+        "TN": 19
+      }
+    },
+    "recall": {
+      "threshold": 2.6,
+      "value": 1.0,
+      "confusion": {
+        "TP": 59,
+        "FP": 15,
+        "FN": 0,
+        "TN": 4
+      }
+    }
+  },
+  "counts": {
+    "total": 78,
+    "positives": 59,
+    "negatives": 19
+  }
+}

docs/task/facture/results/facture_decisions.csv

@@ -0,0 +1,201 @@
+filename,deqa_score,label,decision_f1,decision_acc,decision_prec,decision_rec
+ffc129de-c82b-4c0b-8ab8-2941fd28cda9_Facture_opticien-2.jpg,3.6,High,implement,implement,reject,implement
+9421b4f1-e9f7-42ea-8d9b-833361b16a20_image_1_scale_0.6.jpg,3.8,Low,implement,implement,reject,implement
+Facture ostéopathe-26908261_1_scale_1.5.jpg,3.4,High,implement,implement,reject,implement
+803f61ed-759c-4773-90f2-d8737ef911ab_image-1.jpg,3.9,Low,implement,implement,reject,implement
+72cb658b-3239-4479-918d-aac53bc4a552_Facture_ophtalmo_14-01-2025-1.jpg,3.5,High,implement,implement,reject,implement
+Facture ostéopathe-26761886_1_scale_1.5.jpg,3.0,Low,reject,reject,reject,implement
+Facture médecine douce-27778240_1_scale_1.5.jpg,2.7,Low,reject,reject,reject,implement
+8f0e72ca-64e5-4797-87bc-9378b842433a_CF_1676107-2.jpg,3.5,High,implement,implement,reject,implement
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+34918269-15fd-4e3d-9180-659530a82d35_OSTEO_01.25_1_scale_1.0.jpg,3.2,Low,implement,reject,reject,implement
+DDE prestation Facture des lentilles de contact-26772418_2_scale_1.5.jpg,2.5,High,reject,reject,reject,implement
+3500ba97-9402-46c4-a016-3a4abe35da60_Facture_osteo_1_2025_1_scale_1.5.jpg,3.5,Low,implement,implement,reject,implement
+6c311fa0-a4c5-435f-81c6-7dec91d04978_FACTURE_POTARD_-_BENEDET_1_scale_1.0.jpg,4.1,Low,implement,implement,implement,implement
+Facture-quittance toutes spécialités-27234926_1_scale_1.5.jpg,2.8,Low,reject,reject,reject,implement
+2f042a0e-e23e-4e4a-af6d-9b013f1509bd_facture_ostheo_1_1_scale_1.5.jpg,3.1,Low,implement,reject,reject,implement
+Facture médecine douce-27701675_1_scale_1.5.jpg,3.5,High,implement,implement,reject,implement
+Magasinier-Exemple-de-CV_1_scale_0.7.jpg,4.1,High,implement,implement,implement,implement
+Promesse-dembauche-212x300.jpg,2.7,Low,reject,reject,reject,implement
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+Facture des lentilles de contact-27671269_1_scale_1.5.jpg,3.4,High,implement,implement,reject,implement
+ce6ed73c-50e6-4684-9d91-26eba210ba43_Feuille_de_soins_Ostheo_2_scale_1.5.jpg,3.6,Low,implement,implement,reject,implement
+modele-bon-de-livraison-1.jpg,3.9,High,implement,implement,reject,implement
+c64ad8f0-0d5a-4a5b-aec5-62ec44099520_Screenshot_20250211_100040_Gallery_1_scale_1.0.jpg,3.9,Low,implement,implement,reject,implement
+77c1b3c3-234d-4431-8675-a5c1d12053e0_osteopathe_MOREAU_Coralie_10-02-25_1_scale_1.5.jpg,2.6,Low,reject,reject,reject,implement
+DDE prestation Facture des lentilles de contact-26740699_1_scale_1.5.jpg,3.6,Low,implement,implement,reject,implement
+ffc129de-c82b-4c0b-8ab8-2941fd28cda9_Facture_opticien_1_scale_1.5.jpg,3.9,High,implement,implement,reject,implement
+mobilenew2.jpg,3.6,Low,implement,implement,reject,implement
+a13a6613-da2d-48b7-96ef-412ba5a88af0_Factures_Clemence__1_scale_1.5.jpg,3.7,High,implement,implement,reject,implement
+043bf393-055f-451c-ba6f-f01cac5964f0_17370239165192445587441836799654_1_scale_0.6.jpg,3.7,Low,implement,implement,reject,implement
+4c7209da-d83b-416e-b7a2-2d3e409ad598_Schneider_Fred_250213_110050_1_scale_1.5.jpg,3.9,High,implement,implement,reject,implement
+Facture ostéopathe-25233927_1_scale_1.5.jpg,3.5,High,implement,implement,reject,implement
+Facture ostéopathe-25246953_1_scale_1.5.jpg,3.6,High,implement,implement,reject,implement
+Facture ostéopathe-26112050_1_scale_1.5.jpg,3.0,Low,reject,reject,reject,implement
+Facture médecine douce-27702201_1_scale_1.5.jpg,3.6,Low,implement,implement,reject,implement
+51c8093e-d8b7-4ac2-a57b-b0e7e8797143_20250206_111519_1_scale_1.0.jpg,3.8,High,implement,implement,reject,implement
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+80b79126-2cfa-41bd-83cd-81fa0efca7c3_facture_lentilles_Leila_ben_amor_REMADI__1_scale_1.5.jpg,3.7,Low,implement,implement,reject,implement
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+819f6faf-d9f3-4668-9000-e414d92c7f41_ROYER_LUDIVINE_FACTURE_OPHTALMO_1_scale_1.5.jpg,3.8,High,implement,implement,reject,implement
+f22ss_1_scale_1.5.jpg,3.0,Low,reject,reject,reject,implement
+Facture ostéopathe-25143750_1_scale_1.5.jpg,3.5,High,implement,implement,reject,implement
+7321fd47-74f0-44ac-b7d9-26c7bf9bdd84_image_1_scale_0.6.jpg,3.6,Low,implement,implement,reject,implement
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+Facture médecine douce-27718494_1_scale_1.5.jpg,3.1,High,implement,reject,reject,implement
+Facture ostéopathe-25382451_1_scale_1.5.jpg,3.4,High,implement,implement,reject,implement
+80b79126-2cfa-41bd-83cd-81fa0efca7c3_facture_lentilles_Leila_ben_amor_REMADI_-1.jpg,3.6,Low,implement,implement,reject,implement
+Facture ostéopathe-25295241_1_scale_1.5.jpg,3.3,High,implement,implement,reject,implement
+1498c5f5-22cf-41d4-87f0-9b295ff481f4_IMG_5659-1.jpg,4.0,Low,implement,implement,reject,implement
+534b115b-9997-48c3-bc09-ffb89e5bddf3_Screenshot_20250212_150902_com.google.android.apps.docs_1_scale_1.0.jpg,3.6,Low,implement,implement,reject,implement
+Facture ostéopathe-26112692_1_scale_1.5.jpg,3.4,High,implement,implement,reject,implement
+facture-engie-page-3-349x500.jpg,3.7,Low,implement,implement,reject,implement
+b32c815a-62d9-40d2-b979-eb10e581f7a9_image-1.jpg,3.8,Low,implement,implement,reject,implement
+ba1b499f-199f-4106-bb2c-aac62a67c235_doc00448420250313084147-1.jpg,3.6,High,implement,implement,reject,implement
+Facture médecine douce-27778049_1_scale_1.5.jpg,3.7,Low,implement,implement,reject,implement
+Facture ostéopathe-27093974_1_scale_1.5.jpg,3.7,Low,implement,implement,reject,implement
+Claude_François_-_Certificat_de_scolarité_-_Lycée_français_du_Caire_-_1956_1_scale_1.0.jpg,3.8,Low,implement,implement,reject,implement
+4d9dad1f-732c-4580-8899-438c1eae07a3_FACTURE_LUNETTES_1_scale_1.5.jpg,3.6,High,implement,implement,reject,implement
+1724226151848OPPON_GEROGES_ORDO_LENT_2023_1_scale_1.5.jpg,2.8,Low,reject,reject,reject,implement
+Facture-quittance toutes spécialités-27638903_1_scale_1.5.jpg,3.8,High,implement,implement,reject,implement
+Facture médecine douce-27784856_1_scale_1.5.jpg,3.1,High,implement,reject,reject,implement
+fd8187e1-b9eb-4727-8041-9fc9e6f1affb_0556_001_1_scale_1.5.jpg,3.5,High,implement,implement,reject,implement
+Facture ostéopathe-1184306_1_scale_1.5.jpg,2.7,Low,reject,reject,reject,implement
+Facture ostéopathe-1204645_1_scale_1.5.jpg,3.4,Low,implement,implement,reject,implement
+6ee5b983-1954-4a68-9caf-1ea493b5dad4_IMG_6731-1.jpg,3.8,Low,implement,implement,reject,implement
+Facture ostéopathe-25848670_2_scale_1.5.jpg,1.7,High,reject,reject,reject,implement
+287b78bf-1861-467f-973f-21aad6be26f1_livret_de_famille_Humez_25022024-3_1_scale_1.0.jpg,3.2,Low,implement,reject,reject,implement
+8b6539db-21b4-41dc-a27f-db2cd351fe5f_Doc_34-1.jpg,4.0,Low,implement,implement,reject,implement
+Facture ostéopathe-25224007_1_scale_1.5.jpg,3.5,High,implement,implement,reject,implement
+Facture-quittance toutes spécialités-27146817_1_scale_1.5.jpg,3.4,High,implement,implement,reject,implement
+d63b7f97-36f9-4f4e-aa4d-64e2efb17678_Mon_scan_1_1_scale_1.5.jpg,3.8,High,implement,implement,reject,implement
+Facture ostéopathe-25341458_1_scale_1.5.jpg,3.5,High,implement,implement,reject,implement
+Facture ostéopathe-25073913_1_scale_1.5.jpg,3.4,High,implement,implement,reject,implement
+bb5879ba-ec78-475d-a21b-91ee17d66778_17390362559246788762525665199914_1_scale_0.9.jpg,3.8,Low,implement,implement,reject,implement
+Facture des lentilles de contact-27673604_1_scale_1.5.jpg,3.5,High,implement,implement,reject,implement
+91f98d72-999c-4799-b0e5-c918a7ed52b0_17393509620094620772493095873549_1_scale_1.0.jpg,3.5,Low,implement,implement,reject,implement
+2062d98b-529c-42d0-87bb-cc131ee36c1b_Factures_Osteo_1_scale_1.5.jpg,4.1,High,implement,implement,implement,implement
+e07571f8-3ba4-46fc-a37e-53bd6dd965c7_IMG20250123190152_1_scale_0.6.jpg,3.8,Low,implement,implement,reject,implement
+Facture ostéopathe-26398662_1_scale_1.5.jpg,2.9,Low,reject,reject,reject,implement
+Facture ostéopathe-1207573_1_scale_1.5.jpg,2.7,Low,reject,reject,reject,implement
+Facture des lentilles de contact-27663184_2_scale_1.5.jpg,2.5,High,reject,reject,reject,implement
+Facture médecine douce-27647591_1_scale_1.5.jpg,3.2,High,implement,reject,reject,implement
+5600db4f-5884-4f54-87ce-56c8b3f5f0a0_Aurelie_GALEOTTI_Ezio__1_scale_1.5.jpg,3.7,High,implement,implement,reject,implement
+6ffa2fe1-11be-4516-bd81-bef1824f5f8c_Ordonnances_et_factures_2_scale_1.5.jpg,3.7,Low,implement,implement,reject,implement
+Facture médecine douce-27710953_1_scale_1.5.jpg,3.5,Low,implement,implement,reject,implement
+17246582258432400585-goubier-sandrine-1835799692661e42158b3a5_1_scale_1.5.jpg,4.0,High,implement,implement,reject,implement
+821b6297-8c00-4f88-89a7-a9213c38c3e8_JEAN_PIERRE_001_1_scale_1.0.jpg,3.6,High,implement,implement,reject,implement
+Facture des lentilles de contact-27619324_1_scale_1.5.jpg,3.3,High,implement,implement,reject,implement
+Facture médecine douce-27708288_1_scale_1.5.jpg,4.0,Low,implement,implement,reject,implement
+Facture ostéopathe-26592511_1_scale_1.5.jpg,3.7,High,implement,implement,reject,implement
+Facture médecine douce-27776257_1_scale_1.5.jpg,3.3,Low,implement,implement,reject,implement
+7b830a57-2131-479c-bf31-1673e7308903_invoice_2025_1_31-1.jpg,3.8,High,implement,implement,reject,implement
+Facture ostéopathe-25182408_2_scale_1.5.jpg,1.9,High,reject,reject,reject,implement
+Facture médecine douce-27784971_1_scale_1.5.jpg,3.2,Low,implement,reject,reject,implement
+c7704de1-820a-44bf-aef9-f7b185f94a26_facture_ophtalmo-2.jpg,3.3,High,implement,implement,reject,implement
+bf09a877-0705-4c79-b908-6e8da44e44c7_17490641879424403676372514736136_1_scale_0.6.jpg,3.6,Low,implement,implement,reject,implement
+189462d7-2c53-403e-b27f-156f210c29e0_Facture_osteo__1_scale_1.5.jpg,3.6,High,implement,implement,reject,implement
+cf4fa87b-649f-4c6b-8a70-3ce5755256fa_Attestation_20250205104300934_1_scale_1.5.jpg,3.7,High,implement,implement,reject,implement
+Facture ostéopathe-25265881_1_scale_1.5.jpg,3.5,Low,implement,implement,reject,implement
+Facture ostéopathe-26964863_1_scale_1.5.jpg,3.5,High,implement,implement,reject,implement
+Facture ostéopathe-26010134_1_scale_1.5.jpg,3.4,High,implement,implement,reject,implement
+d319c0f9-9a87-4e75-90b2-a92836caf034_20250208_101526_1_scale_0.6.jpg,4.0,High,implement,implement,reject,implement
+sncf_e-billet.jpg,3.1,Low,implement,reject,reject,implement
+Facture ostéopathe-1102494_1_scale_1.5.jpg,3.5,High,implement,implement,reject,implement
+certificate-1_1_scale_1.0.jpg,4.0,High,implement,implement,reject,implement
+Facture ostéopathe-25082790_1_scale_1.5.jpg,3.7,High,implement,implement,reject,implement
+c0625556-2cb1-42c8-9433-e31274c121a3_Livret_de_famille_hamidouna-1.jpg,3.1,Low,implement,reject,reject,implement
+84ee3980-6f2b-4943-9b11-1996dce1039f_Dossier_lentilles_Stephanie_M_2_scale_1.5.jpg,3.2,Low,implement,reject,reject,implement
+Facture des lentilles de contact-27570312_1_scale_1.5.jpg,3.5,Low,implement,implement,reject,implement
+7402b29f-bcc9-42ed-a5a9-a8ec6468ad90_17392879320695618357009043029309_1_scale_0.6.jpg,4.0,Low,implement,implement,reject,implement
+707771f6-bb3b-4e5e-8d7d-acd584a23009_IMG_20240726_142929_1_scale_1.0.jpg,3.7,Low,implement,implement,reject,implement
+Facture ostéopathe-25198581_1_scale_1.5.jpg,3.1,High,implement,reject,reject,implement
+Facture médecine douce-27705774_1_scale_1.5.jpg,3.9,High,implement,implement,reject,implement
+02c6c398-ddf5-47e9-be88-a27742a2683c_recu-fac-2025-02-002979_1_scale_1.5.jpg,3.8,High,implement,implement,reject,implement
+Facture médecine douce-27780823_1_scale_1.5.jpg,3.9,Low,implement,implement,reject,implement
+803f61ed-759c-4773-90f2-d8737ef911ab_image_1_scale_0.6.jpg,3.9,Low,implement,implement,reject,implement
+881d86b7-5bdb-4a16-bcbc-57202e0879b1_osteo_fevrier_2025_1_scale_1.0.jpg,3.8,Low,implement,implement,reject,implement
+imageaaaaaaaafddddddddddfgdaafds_1_scale_1.0.jpg,3.6,Low,implement,implement,reject,implement
+17243973662012024.04.16_-_Osteo_Elrik_1_scale_0.9.jpg,3.2,Low,implement,reject,reject,implement
+Facture médecine douce-27760177_1_scale_1.5.jpg,3.9,Low,implement,implement,reject,implement
+644c17e0-cced-46f8-87e7-231c6ac0f5b5_ostheo_04.02.25_1_scale_1.5.jpg,3.6,Low,implement,implement,reject,implement
+8794ba0a-5d58-4d01-a1be-71f7d5c4bfe2_IMG_20250207_185936_1_scale_0.6.jpg,4.0,High,implement,implement,reject,implement
+Facture des lentilles de contact-27621196_1_scale_1.5.jpg,3.5,High,implement,implement,reject,implement
+879cf76a-68c1-40d7-b444-0ff3341c97de_facture-zami-isabelle-20250130-1703_1-1.jpg,3.7,High,implement,implement,reject,implement
+ticket-PAYFIP0000000014966980-1.jpg,3.9,High,implement,implement,reject,implement
+c865b3cf-c251-465d-97e2-ada7a618dc56_Numerisation_20250207_1_scale_1.0.jpg,3.8,High,implement,implement,reject,implement
+Facture ostéopathe-25182408_1_scale_1.5.jpg,3.4,High,implement,implement,reject,implement
+Facture des lentilles de contact-27551187_1_scale_1.5.jpg,3.7,Low,implement,implement,reject,implement
+Facture médecine douce-27705389_1_scale_1.5.jpg,3.7,High,implement,implement,reject,implement
+01f6cc06-5720-4836-a490-1521aa928049_image_1_scale_0.6.jpg,3.5,Low,implement,implement,reject,implement
+Facture médecine douce-27700785_1_scale_1.5.jpg,3.2,High,implement,reject,reject,implement
+bdb308da-16f5-4370-b1a4-8ffe4f063d84_2025-02-06-13-21-14-309_1_scale_0.6.jpg,4.0,Low,implement,implement,reject,implement
+8f0e72ca-64e5-4797-87bc-9378b842433a_CF_1676107_2_scale_1.5.jpg,3.5,High,implement,implement,reject,implement
+03deb470-7e1b-4ef6-9c03-5e2c4f9aa83d_facture_2023-20-13_1_scale_1.5.jpg,3.8,High,implement,implement,reject,implement
+Facture ostéopathe-25375641_2_scale_1.5.jpg,2.7,High,reject,reject,reject,implement
+Facture médecine douce-27701380_1_scale_1.5.jpg,3.6,High,implement,implement,reject,implement
+d5a8f5e5-7755-4791-910a-9c4079fa07f4_Facture_osteopathe_Madame_Gregoire_290125_1_scale_1.5.jpg,2.7,Low,reject,reject,reject,implement
+Facture ostéopathe-25021617_1_scale_1.5.jpg,3.3,High,implement,implement,reject,implement
+Facture ostéopathe-1210226_1_scale_1.5.jpg,3.4,High,implement,implement,reject,implement
+1724397366263FACTURES_OSTEOPATHE_1_scale_1.5.jpg,2.8,Low,reject,reject,reject,implement
+b14402b2-1846-4abb-b17e-31a538802ae8_Facture_Osteopathe_10022025__1_scale_1.5.jpg,3.8,High,implement,implement,reject,implement
+0785b11d-69b5-4f5f-b75d-9caf82642652_LENTILLES_1_scale_1.0.jpg,3.7,Low,implement,implement,reject,implement
+72347d5c-1c18-4a42-8b6c-f0a365dbaf97_FACTURE_N_14_1_scale_1.5.jpg,2.5,Low,reject,reject,reject,implement
+Attestation d'emploi-1_1_scale_1.0.jpg,3.9,High,implement,implement,reject,implement
+588bd118-1770-4c2a-8d86-a6a6cbed79b2_osteopathe_1_scale_1.5.jpg,3.9,Low,implement,implement,reject,implement
+Facture médecine douce-27701076_1_scale_1.5.jpg,2.9,Low,reject,reject,reject,implement
+Facture ostéopathe-1183991_1_scale_1.5.jpg,2.6,Low,reject,reject,reject,implement
+1724399050337CamScanner_24-10-2023_16.211_1_scale_1.5.jpg,3.5,High,implement,implement,reject,implement
+Extrait acte de naissance - 26773427.jpg,3.6,High,implement,implement,reject,implement
+Facture ostéopathe-26141775_1_scale_1.5.jpg,3.4,Low,implement,implement,reject,implement
+c052b9e0-c0d4-473f-93a9-a5dc396a0d86_Facture_ophtalmologique_-1.jpg,3.1,High,implement,reject,reject,implement
+88248db2-efbd-42c8-aacf-4e32f2b5b309_17392731219852541052997326186649_1_scale_0.6.jpg,3.6,Low,implement,implement,reject,implement
+Facture ostéopathe-25292676_1_scale_1.5.jpg,3.2,High,implement,reject,reject,implement
+8e86f61e-284f-4984-84ba-4de5883b0bc8_recu-nh-2025-02-015583_1_scale_1.5.jpg,3.8,High,implement,implement,reject,implement
+Facture-quittance toutes spécialités-27396735_1_scale_1.5.jpg,3.7,High,implement,implement,reject,implement
+Facture médecine douce-27753021_3_scale_1.5.jpg,3.5,High,implement,implement,reject,implement
+aze_cargmoe017762-61_001_1_scale_1.0.jpg,3.6,Low,implement,implement,reject,implement
+48e19752-3534-4539-8905-e09bdeb9fddd_CamScanner_11-02-2025_21.51_1_1_scale_1.0.jpg,3.6,Low,implement,implement,reject,implement
+Facture ostéopathe-25847017_1_scale_1.5.jpg,3.4,High,implement,implement,reject,implement
+1724156469378osteopathe_renau_anais_le_31-07-2024_1_scale_1.5.jpg,3.6,High,implement,implement,reject,implement
+Facture ostéopathe-26699279_1_scale_1.5.jpg,3.4,Low,implement,implement,reject,implement
+b46e8411-29d3-44b6-ae65-55f11bf360bd_FACTURE_OCULISTE_JANE-1.jpg,3.4,High,implement,implement,reject,implement
+7ad6d85f-7245-4c05-8c79-512ed13fe0d5_lunettes_001-1.jpg,3.1,Low,implement,reject,reject,implement
+5dab2435-c975-40bc-89d4-fadf59c19553_image_1_scale_0.6.jpg,3.8,High,implement,implement,reject,implement
+Facture ostéopathe-25198581_2_scale_1.5.jpg,2.0,Low,reject,reject,reject,implement
+1724399050372FA_24-1136_1_scale_1.5.jpg,3.8,High,implement,implement,reject,implement
+44f39110-8f97-415d-8cc1-989f0a6df226_Ostheo_22_01_25_1_scale_1.5.jpg,2.6,Low,reject,reject,reject,implement
+8fffd4ea-784b-49df-9f7e-780eed5664c8_facturation_osteo-1_1_scale_1.5.jpg,3.8,High,implement,implement,reject,implement
+Facture médecine douce-27776613_1_scale_1.5.jpg,2.5,Low,reject,reject,reject,implement
+3f09fc00-ffd5-498a-adb8-b6805519ad3a_20250212_133526_1_scale_0.6.jpg,4.0,Low,implement,implement,reject,implement
+9838138f-ae12-4664-831e-ae5f0b9c14fb_IMG_5959-1.jpg,3.4,Low,implement,implement,reject,implement
+Facture des lentilles de contact-27719018_1_scale_1.5.jpg,3.9,Low,implement,implement,reject,implement
+Facture médecine douce-27703164_1_scale_1.5.jpg,3.1,Low,implement,reject,reject,implement
+cc0c0b33-1a94-469b-bd88-ca534e257d33_Osteo_1_scale_1.5.jpg,3.6,High,implement,implement,reject,implement
+8c3abdc3-796f-4764-9cc8-4da2481f36e8_IMG_2534_1_scale_0.6.jpg,4.0,High,implement,implement,reject,implement
+Facture des lentilles de contact-27620389_1_scale_1.5.jpg,3.6,High,implement,implement,reject,implement
+DDE prestation Facture des lentilles de contact-26751453_1_scale_1.5.jpg,3.8,High,implement,implement,reject,implement
+Facture ostéopathe-1078555_1_scale_1.5.jpg,3.9,High,implement,implement,reject,implement
+ce769ca9-924b-4355-bb1f-ce98c6a7049b_IMG_5741_1_scale_1.0.jpg,3.7,High,implement,implement,reject,implement
+RF MO ODO 2018 2 8.jpg,3.5,Low,implement,implement,reject,implement
+DDE prestation Facture des lentilles de contact-26733889_1_scale_1.5.jpg,3.5,Low,implement,implement,reject,implement
+172415667786117241475973227470455758207037834_1_scale_1.0.jpg,3.9,High,implement,implement,reject,implement
+0c9e3552-1f5f-4779-8c8b-1481c6b7ab2b_20250113_092847-1.jpg,3.6,Low,implement,implement,reject,implement
+1724425079171Facture_PSY_11042024_1_scale_1.0.jpg,3.6,Low,implement,implement,reject,implement
+Facture ostéopathe-26349654_1_scale_1.5.jpg,3.3,Low,implement,implement,reject,implement
+64fa4c19-efa4-48f1-acf8-8c2e8f573b8b_Facture_osteopathe__1_scale_1.5.jpg,3.4,Low,implement,implement,reject,implement
+1724397125682facture_1_scale_1.5.jpg,3.5,High,implement,implement,reject,implement
+17b264a2-4881-49a4-8326-48ace17dd86f_factures_ophtalmo_3_scale_1.5.jpg,3.5,High,implement,implement,reject,implement
+484d05c2-b89b-4ec0-8968-9ae6a5360f6a_DetailDesPaiements_1_scale_1.5.jpg,3.7,High,implement,implement,reject,implement
+1724226587979Seances_ostheo_02_1_scale_1.5.jpg,2.6,Low,reject,reject,reject,implement
+a593830c-dcbb-4553-860f-b0925a4c30ef_image-1.jpg,3.7,Low,implement,implement,reject,implement
+Facture ostéopathe-1062996_1_scale_1.5.jpg,3.4,High,implement,implement,reject,implement
+Facture ostéopathe-26595219_2_scale_1.5.jpg,2.7,High,reject,reject,reject,implement
+b86e78c9-db57-42d0-ab4e-5c3175228663_20250207_134758_1_scale_0.6.jpg,4.0,Low,implement,implement,reject,implement
+287b78bf-1861-467f-973f-21aad6be26f1_livret_de_famille_Humez_25022024-2_1_scale_1.0.jpg,3.4,Low,implement,implement,reject,implement
+de6a66cd-d729-479c-a119-8b9e442702db_Screenshot_20250227_112521_Gallery-1.jpg,3.4,High,implement,implement,reject,implement
+Facture ostéopathe-1148278_1_scale_1.5.jpg,3.6,High,implement,implement,reject,implement
+Untitleda02d1dfdad-50111dfafdfd.jpg,4.0,High,implement,implement,reject,implement
+Facture ostéopathe-25279751_1_scale_1.5.jpg,3.4,High,implement,implement,reject,implement
+Extrait acte de naissance - 27029579.jpg,3.2,Low,implement,reject,reject,implement
+a90d5fa1-fca8-4995-b894-aff2e2449ee4_20250207_183312_1_scale_0.6.jpg,3.9,Low,implement,implement,reject,implement

docs/task/facture/results/facture_thresholds_summary.json

@@ -0,0 +1,50 @@
+{
+  "positive_definition": "HIGH when score >= threshold",
+  "best_thresholds": {
+    "f1": {
+      "threshold": 3.1,
+      "value": 0.6886446886446885,
+      "confusion": {
+        "TP": 94,
+        "FP": 79,
+        "FN": 6,
+        "TN": 21
+      }
+    },
+    "accuracy": {
+      "threshold": 3.3,
+      "value": 0.595,
+      "confusion": {
+        "TP": 87,
+        "FP": 68,
+        "FN": 13,
+        "TN": 32
+      }
+    },
+    "precision": {
+      "threshold": 4.1,
+      "value": 0.6666666666666666,
+      "confusion": {
+        "TP": 2,
+        "FP": 1,
+        "FN": 98,
+        "TN": 99
+      }
+    },
+    "recall": {
+      "threshold": 1.7,
+      "value": 1.0,
+      "confusion": {
+        "TP": 100,
+        "FP": 100,
+        "FN": 0,
+        "TN": 0
+      }
+    }
+  },
+  "counts": {
+    "total": 200,
+    "positives": 100,
+    "negatives": 100
+  }
+}

requirements.txt

@@ -10,3 +10,4 @@ tqdm==4.65.0
 torch
 transformers
 pyiqa
+openpyxl==3.1.5

results/cni.txt

@@ -1,81 +0,0 @@
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-3.3  - ITE55343716_CNI_Completed_0.png
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scripts/threshold_analysis.py

@@ -0,0 +1,327 @@
+#!/usr/bin/env python3
+"""
+Threshold analysis for DeQA scores vs. human labels (High/Low).
+
+Inputs (defaults for facture task):
+- results/facture.txt        # lines like: "4.2  - filename.jpg"
+- data/facture/labels.csv    # columns: filename,label with label in {High,Low}
+
+Outputs:
+- results/facture_thresholds_summary.json   # best thresholds for accuracy/precision/recall/F1
+- results/facture_metric_curves.png         # metrics vs threshold
+- results/facture_score_distributions.png   # score histograms by label
+- results/facture_decisions.csv             # per-image decisions at each operating point
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+
+def read_deqa_results_txt(path: Path) -> pd.DataFrame:
+    """Read TXT results of the form "<score>  - <filename>" into a DataFrame."""
+    rows: List[Dict[str, str | float]] = []
+    with open(path, "r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            # Expect pattern: "<score>  - <filename>"
+            try:
+                score_part, fname = line.split("  - ", 1)
+                score = float(score_part)
+                rows.append({"filename": fname, "score": score})
+            except Exception:
+                # Skip malformed lines silently
+                continue
+    df = pd.DataFrame(rows)
+    if not df.empty:
+        df["filename"] = df["filename"].astype(str)
+        df["stem"] = df["filename"].apply(lambda x: Path(x).stem.lower())
+    return df
+
+
+def read_labels_csv(path: Path) -> pd.DataFrame:
+    """Read labels CSV with columns: filename,label (High/Low)."""
+    df = pd.read_csv(path)
+    # Normalize
+    df["filename"] = df["filename"].astype(str)
+    df["label"] = df["label"].astype(str).str.strip().str.capitalize()
+    # Map High->1, Low->0
+    label_map = {"High": 1, "Low": 0}
+    df["y_true"] = df["label"].map(label_map)
+    df["stem"] = df["filename"].apply(lambda x: Path(x).stem.lower())
+    return df[["filename", "label", "y_true", "stem"]]
+
+
+def confusion_from_threshold(scores: np.ndarray, y_true: np.ndarray, thr: float) -> Tuple[int, int, int, int]:
+    pred = (scores >= thr).astype(int)
+    tp = int(np.sum((pred == 1) & (y_true == 1)))
+    fp = int(np.sum((pred == 1) & (y_true == 0)))
+    fn = int(np.sum((pred == 0) & (y_true == 1)))
+    tn = int(np.sum((pred == 0) & (y_true == 0)))
+    return tp, fp, fn, tn
+
+
+def metric_from_confusion(tp: int, fp: int, fn: int, tn: int, metric: str) -> float:
+    if metric == "accuracy":
+        denom = tp + fp + fn + tn
+        return (tp + tn) / denom if denom > 0 else 0.0
+    if metric == "precision":
+        denom = tp + fp
+        return tp / denom if denom > 0 else 0.0
+    if metric == "recall":
+        denom = tp + fn
+        return tp / denom if denom > 0 else 0.0
+    if metric == "f1":
+        p_denom = tp + fp
+        r_denom = tp + fn
+        precision = tp / p_denom if p_denom > 0 else 0.0
+        recall = tp / r_denom if r_denom > 0 else 0.0
+        denom = precision + recall
+        return (2 * precision * recall / denom) if denom > 0 else 0.0
+    raise ValueError(f"Unsupported metric: {metric}")
+
+
+def pick_threshold(scores: np.ndarray, y_true: np.ndarray, metric: str = "f1") -> Tuple[float, float, Dict[str, int]]:
+    thr_candidates = np.unique(scores)
+    best_thr: float | None = None
+    best_val: float = -1.0
+    best_conf: Tuple[int, int, int, int] | None = None
+
+    for t in thr_candidates:
+        tp, fp, fn, tn = confusion_from_threshold(scores, y_true, t)
+        val = metric_from_confusion(tp, fp, fn, tn, metric)
+        # Tie-breaker: prefer higher threshold if metric ties (safer for downstream)
+        if (val > best_val) or (np.isclose(val, best_val) and (best_thr is None or t > best_thr)):
+            best_val = val
+            best_thr = t
+            best_conf = (tp, fp, fn, tn)
+
+    assert best_thr is not None and best_conf is not None
+    tp, fp, fn, tn = best_conf
+    return float(best_thr), float(best_val), {"TP": tp, "FP": fp, "FN": fn, "TN": tn}
+
+
+def compute_metric_curves(scores: np.ndarray, y_true: np.ndarray) -> pd.DataFrame:
+    data: List[Dict[str, float]] = []
+    for t in np.unique(scores):
+        tp, fp, fn, tn = confusion_from_threshold(scores, y_true, t)
+        row = {
+            "threshold": float(t),
+            "accuracy": metric_from_confusion(tp, fp, fn, tn, "accuracy"),
+            "precision": metric_from_confusion(tp, fp, fn, tn, "precision"),
+            "recall": metric_from_confusion(tp, fp, fn, tn, "recall"),
+            "f1": metric_from_confusion(tp, fp, fn, tn, "f1"),
+            "TP": tp,
+            "FP": fp,
+            "FN": fn,
+            "TN": tn,
+        }
+        data.append(row)
+    return pd.DataFrame(data).sort_values("threshold").reset_index(drop=True)
+
+
+def plot_distributions(df: pd.DataFrame, out_path: Path) -> None:
+    plt.figure(figsize=(8, 5))
+    sns.histplot(data=df, x="score", hue="label", bins=30, kde=True, stat="density", common_norm=False)
+    plt.title("DeQA score distributions by label")
+    plt.xlabel("DeQA score")
+    plt.ylabel("Density")
+    plt.tight_layout()
+    plt.savefig(out_path, dpi=150)
+    plt.close()
+
+
+def plot_metric_curves(curve_df: pd.DataFrame, out_path: Path) -> None:
+    plt.figure(figsize=(8, 5))
+    for metric in ["accuracy", "precision", "recall", "f1"]:
+        plt.plot(curve_df["threshold"], curve_df[metric], label=metric)
+    plt.xlabel("Threshold (score >= t => HIGH)")
+    plt.ylabel("Metric value")
+    plt.ylim(0.0, 1.05)
+    plt.title("Metrics vs threshold")
+    plt.legend()
+    plt.grid(True, alpha=0.3)
+    plt.tight_layout()
+    plt.savefig(out_path, dpi=150)
+    plt.close()
+
+def plot_sorted_scores_with_threshold(df: pd.DataFrame, thr: float, out_path: Path) -> None:
+    tmp = df.sort_values("score").reset_index(drop=True)
+    x = np.arange(len(tmp))
+    y = tmp["score"].to_numpy()
+    plt.figure(figsize=(9, 4))
+    plt.scatter(x, y, s=6, alpha=0.6)
+    plt.axhline(thr, color="red", linestyle="--", label=f"threshold={thr:.3f}")
+    plt.xlabel("Images sorted by score")
+    plt.ylabel("DeQA score")
+    plt.title("Sorted scores with operating threshold")
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig(out_path, dpi=150)
+    plt.close()
+
+def plot_pr_curve(curves: pd.DataFrame, out_path: Path) -> None:
+    plt.figure(figsize=(6, 5))
+    plt.plot(curves["recall"], curves["precision"], marker="o", ms=3, lw=1)
+    plt.xlabel("Recall")
+    plt.ylabel("Precision")
+    plt.title("Precision-Recall across thresholds")
+    plt.grid(True, alpha=0.3)
+    plt.tight_layout()
+    plt.savefig(out_path, dpi=150)
+    plt.close()
+
+def plot_roc_like(curves: pd.DataFrame, out_path: Path) -> None:
+    # TPR=recall, FPR=FP/(FP+TN)
+    denom = (curves["FP"] + curves["TN"]).replace(0, np.nan)
+    fpr = curves["FP"] / denom
+    tpr = curves["recall"]
+    plt.figure(figsize=(6, 5))
+    plt.plot(fpr.fillna(0), tpr, marker="o", ms=3, lw=1)
+    plt.xlabel("False Positive Rate (FPR)")
+    plt.ylabel("True Positive Rate (TPR)")
+    plt.title("ROC-like curve across thresholds")
+    plt.grid(True, alpha=0.3)
+    plt.tight_layout()
+    plt.savefig(out_path, dpi=150)
+    plt.close()
+
+def plot_confusion_heatmap(tp: int, fp: int, fn: int, tn: int, out_path: Path) -> None:
+    cm = np.array([[tp, fp],[fn, tn]])
+    plt.figure(figsize=(4, 4))
+    sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", cbar=False,
+                xticklabels=["Pred High","Pred Low"], yticklabels=["True High","True Low"])
+    plt.title("Confusion matrix at operating threshold")
+    plt.tight_layout()
+    plt.savefig(out_path, dpi=150)
+    plt.close()
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Threshold analysis for DeQA scores vs labels")
+    parser.add_argument("--scores", type=str, default="results/facture.txt", help="Path to deqa scores txt")
+    parser.add_argument("--labels", type=str, default="data/facture/labels.csv", help="Path to labels csv")
+    parser.add_argument("--outdir", type=str, default="results", help="Directory to write outputs")
+    parser.add_argument("--sample-per-class", type=int, default=0,
+                        help="If >0, randomly sample N High and N Low for a quick benchmark")
+    parser.add_argument("--seed", type=int, default=42, help="Random seed for sampling")
+    args = parser.parse_args()
+
+    scores_path = Path(args.scores)
+    labels_path = Path(args.labels)
+    outdir = Path(args.outdir)
+    outdir.mkdir(parents=True, exist_ok=True)
+
+    # Load
+    df_scores = read_deqa_results_txt(scores_path)
+    df_labels = read_labels_csv(labels_path)
+
+    # Join on lowercase stem to tolerate extension differences
+    df = df_scores.merge(df_labels, on="stem", how="inner", suffixes=("_score", "_label"))
+    # Prefer label-side filename when available
+    df["filename"] = df["filename_label"].where(df["filename_label"].notna(), df["filename_score"])
+    df.drop(columns=[c for c in ["filename_label", "filename_score"] if c in df.columns], inplace=True)
+    if df.empty:
+        raise RuntimeError("No overlap between scores and labels. Check filenames.")
+
+    # Optional sampling per class
+    if args.sample_per_class and args.sample_per_class > 0:
+        rng = np.random.default_rng(args.seed)
+        high_df = df[df["y_true"] == 1]
+        low_df = df[df["y_true"] == 0]
+        n_high = min(args.sample_per_class, len(high_df))
+        n_low = min(args.sample_per_class, len(low_df))
+        high_sample = high_df.sample(n=n_high, random_state=args.seed)
+        low_sample = low_df.sample(n=n_low, random_state=args.seed)
+        df = pd.concat([high_sample, low_sample], ignore_index=True)
+        df = df.sample(frac=1.0, random_state=args.seed).reset_index(drop=True)
+
+    scores = df["score"].to_numpy(dtype=float)
+    y_true = df["y_true"].to_numpy(dtype=int)
+
+    # Compute best thresholds
+    thr_f1, best_f1, conf_f1 = pick_threshold(scores, y_true, metric="f1")
+    thr_acc, best_acc, conf_acc = pick_threshold(scores, y_true, metric="accuracy")
+    thr_prec, best_prec, conf_prec = pick_threshold(scores, y_true, metric="precision")
+    thr_rec, best_rec, conf_rec = pick_threshold(scores, y_true, metric="recall")
+
+    summary = {
+        "positive_definition": "HIGH when score >= threshold",
+        "best_thresholds": {
+            "f1": {"threshold": thr_f1, "value": best_f1, "confusion": conf_f1},
+            "accuracy": {"threshold": thr_acc, "value": best_acc, "confusion": conf_acc},
+            "precision": {"threshold": thr_prec, "value": best_prec, "confusion": conf_prec},
+            "recall": {"threshold": thr_rec, "value": best_rec, "confusion": conf_rec},
+        },
+        "counts": {
+            "total": int(len(df)),
+            "positives": int(df["y_true"].sum()),
+            "negatives": int(len(df) - int(df["y_true"].sum())),
+        },
+    }
+
+    # Metric curves and figures
+    curves = compute_metric_curves(scores, y_true)
+    plot_distributions(df, outdir / "facture_score_distributions.png")
+    plot_metric_curves(curves, outdir / "facture_metric_curves.png")
+    # Extra plots
+    plot_sorted_scores_with_threshold(df, thr_f1, outdir / "facture_sorted_scores_with_thr.png")
+    plot_pr_curve(curves, outdir / "facture_precision_recall_curve.png")
+    plot_roc_like(curves, outdir / "facture_roc_like_curve.png")
+    plot_confusion_heatmap(conf_f1["TP"], conf_f1["FP"], conf_f1["FN"], conf_f1["TN"], outdir / "facture_confusion_matrix.png")
+
+    # Decisions CSV (for three operating points + F1)
+    def decide(thr: float) -> np.ndarray:
+        return (scores >= thr).astype(int)
+
+    df_out = df.copy()
+    df_out["decision_f1"] = decide(thr_f1)
+    df_out["decision_acc"] = decide(thr_acc)
+    df_out["decision_prec"] = decide(thr_prec)
+    df_out["decision_rec"] = decide(thr_rec)
+    # Map 1/0 to textual action
+    to_action = {1: "implement", 0: "reject"}
+    for col in ["decision_f1", "decision_acc", "decision_prec", "decision_rec"]:
+        df_out[col] = df_out[col].map(to_action)
+    df_out.rename(columns={"score": "deqa_score"}, inplace=True)
+    df_out = df_out[["filename", "deqa_score", "label", "decision_f1", "decision_acc", "decision_prec", "decision_rec"]]
+    df_out.to_csv(outdir / "facture_decisions.csv", index=False)
+
+    # Save summary JSON
+    with open(outdir / "facture_thresholds_summary.json", "w", encoding="utf-8") as f:
+        json.dump(summary, f, indent=2)
+
+    # Save a single Excel file with one sheet containing all rows and decisions (F1 operating point)
+    try:
+        excel_path = outdir / "facture_deqa_images.xlsx"
+        one_sheet_df = df_out.copy()
+        # Keep core columns only
+        keep_cols = ["filename", "deqa_score", "label", "decision_f1"]
+        one_sheet_df = one_sheet_df[keep_cols]
+        one_sheet_df.rename(columns={"decision_f1": "decision"}, inplace=True)
+        with pd.ExcelWriter(excel_path, engine='openpyxl') as writer:
+            one_sheet_df.to_excel(writer, sheet_name="DeQA_Images", index=False)
+    except Exception as e:
+        print(f"Warning: Failed to write Excel file: {e}")
+
+    # Also print a concise console summary
+    print("Best thresholds (score >= thr => HIGH):")
+    for k in ["f1", "accuracy", "precision", "recall"]:
+        info = summary["best_thresholds"][k]
+        print(f"- {k}: thr={info['threshold']:.3f}, value={info['value']:.3f}, conf={info['confusion']}")
+
+
+if __name__ == "__main__":
+    main()
+
+