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fix(ml,#19754): sweep 62 obsoletes 4.2[c-k] residuels (c.134) #19953

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myia-ai-01 merged 5 commits into from
Oct 10, 2026
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fix(ml,#19754): sweep 62 obsoletes 4.2[c-k] residuels (c.134) #19953

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0ba82bb
fix(ml,#19754): sweep 62 obsoletes 4.2[c-k] residuels -- 11 fichiers …
claude Oct 8, 2026
b44682e
Fix(ml,#19754): repair review sweep -- 9 source residuels, 3 format s…
jsboige Oct 8, 2026
3959c40
Merge branch 'main' into fix/19754-ml-42-obs-residuels
jsboige Oct 9, 2026
0d90865
Fix(vision,#19953): retire 3.6d du sweep -- churn de serialisation sa…
jsboige Oct 9, 2026
425aa1c
fix(ml,#19754): NB review -- MAP5095_42G renomme MAP5095_45B (mapping…
jsboige Oct 9, 2026
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14 changes: 7 additions & 7 deletions ...Notebooks/ML/DataScienceWithAgents/04-Vision/4.4b-Detection-AnchorFree-From-Scratch.ipynb
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@@ -1,3 +1,3 @@
{
"cells": [
{
Expand Down Expand Up @@ -193,7 +193,7 @@
"Xva, Bva = make_split(400, SEED + 2)\n",
"print(\"train:\", tuple(Xtr.shape), \"| val:\", tuple(Xva.shape),\n",
" \"| objets GT val:\", sum(len(b) for b in Bva))\n",
"print(\"train = %d objets, val = %d objets — les memes comptes que 4.2c\"\n",
"print(\"train = %d objets, val = %d objets — les memes comptes que 4.4\"\n",
" % (sum(len(b) for b in Btr), sum(len(b) for b in Bva)))"
]
},
Expand Down Expand Up @@ -1012,7 +1012,7 @@
"\n",
"\n",
"def nms(boxes, scores, thr=0.45):\n",
" \"\"\"NMS gloutonne : identique a 4.2c, pour que le harnais soit partage.\"\"\"\n",
" \"\"\"NMS gloutonne : identique a 4.4, pour que le harnais soit partage.\"\"\"\n",
" keep, suppressed = [], torch.zeros(len(boxes), dtype=torch.bool)\n",
" for i in scores.argsort(descending=True).tolist():\n",
" if suppressed[i]:\n",
Expand Down Expand Up @@ -1132,7 +1132,7 @@
"\n",
"\n",
"def ap_voc(tp_s, fp_s, ngt):\n",
" \"\"\"Identique a 4.2c : VOC07 11-point + VOC10 all-point.\"\"\"\n",
" \"\"\"Identique a 4.4 : VOC07 11-point + VOC10 all-point.\"\"\"\n",
" flags = np.array([1] * len(tp_s) + [0] * len(fp_s), dtype=np.float64)\n",
" scores = np.array(tp_s + fp_s, dtype=np.float64)\n",
" order = np.argsort(-scores)\n",
Expand Down Expand Up @@ -1229,7 +1229,7 @@
}
],
"source": [
"# chiffres committes dans les sorties de 4.2c (relus, non recalcules)\n",
"# chiffres committes dans les sorties de 4.4 (relus, non recalcules)\n",
"A1_MAP07, A1_MAP10 = 0.853, 0.914\n",
"A1_GRID_SQ07, A1_GRID_SQ10 = 0.864, 0.926\n",
"A1_ANC_FULL = 24 * 24 * 9 # 3 tailles x 3 ratios\n",
Expand All @@ -1238,11 +1238,11 @@
"print(\"=\" * 74)\n",
"print(\"%-30s %10s %10s %10s\" % (\"\", \"mAP07\", \"mAP10\", \"prior geometrique\"))\n",
"print(\"-\" * 74)\n",
"print(\"%-30s %10.3f %10.3f %10s\" % (\"4.2c anchor-based (grille 3x3)\", A1_MAP07, A1_MAP10,\n",
"print(\"%-30s %10.3f %10.3f %10s\" % (\"4.4 anchor-based (grille 3x3)\", A1_MAP07, A1_MAP10,\n",
" f\"{A1_ANC_FULL} anchors\"))\n",
"print(\"%-30s %10.3f %10.3f %10s\" % (\"4.2c anchor-based (grille carree)\", A1_GRID_SQ07,\n",
"print(\"%-30s %10.3f %10.3f %10s\" % (\"4.4 anchor-based (grille carree)\", A1_GRID_SQ07,\n",
" A1_GRID_SQ10, f\"{A1_ANC_SQ} anchors\"))\n",
"print(\"%-30s %10.3f %10.3f %10s\" % (\"4.2d anchor-free (CenterNet-like)\", ap07, ap10, \"0 anchor\"))\n",
"print(\"%-30s %10.3f %10.3f %10s\" % (\"4.4b anchor-free (CenterNet-like)\", ap07, ap10, \"0 anchor\"))\n",
"print(\"=\" * 74)\n",
"print(\"ecart anchor-free - anchor-based (grille 3x3) : %+.1f pts de mAP07, %+.1f pts de mAP10\"\n",
" % (100 * (ap07 - A1_MAP07), 100 * (ap10 - A1_MAP10)))\n",
Expand Down
2 changes: 1 addition & 1 deletion ....Notebooks/ML/DataScienceWithAgents/04-Vision/4.4c-Detection-FocalLoss-From-Scratch.ipynb
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Original file line number Diff line number Diff line change
@@ -1,3 +1,3 @@
{
"cells": [
{
Expand Down Expand Up @@ -915,7 +915,7 @@
}
],
"source": [
"print(\"Exercice 3 à compléter — reprendre AnchorNet du 4.2c et remplacer la BCE par focal_loss\")"
"print(\"Exercice 3 à compléter — reprendre AnchorNet du 4.4 et remplacer la BCE par focal_loss\")"
]
},
{
Expand Down
10 changes: 5 additions & 5 deletions MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.5-Detection-SOTA-Torchvision.ipynb
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Original file line number Diff line number Diff line change
@@ -1,3 +1,3 @@
{
"cells": [
{
Expand Down Expand Up @@ -113,7 +113,7 @@
"print(\"device:\", DEVICE.type,\n",
" \"| torch\", torch.__version__, \"| torchvision\", torchvision.__version__)\n",
"\n",
"IMG = 96 # cote de l'image : identique au 4.2c"
"IMG = 96 # cote de l'image : identique au 4.4"
]
},
{
Expand Down Expand Up @@ -435,7 +435,7 @@
],
"source": [
"def build(name):\n",
" \"\"\"Usine uniforme : tete 2 classes, backbone ImageNet, seuils du 4.2c,\n",
" \"\"\"Usine uniforme : tete 2 classes, backbone ImageNet, seuils du 4.4,\n",
" redimensionnement d'entree identique (320) pour les trois familles.\"\"\"\n",
" if name == \"faster-rcnn\":\n",
" return fasterrcnn_mobilenet_v3_large_320_fpn(\n",
Expand Down Expand Up @@ -545,7 +545,7 @@
" return time.time() - t0\n",
"\n",
"\n",
"print(\"budget commun :\", N_TRAIN, \"images x\", EPOCHS, \"epoques (le 4.2c : 2000 x 12)\")"
"print(\"budget commun :\", N_TRAIN, \"images x\", EPOCHS, \"epoques (le 4.4 : 2000 x 12)\")"
]
},
{
Expand Down Expand Up @@ -851,7 +851,7 @@
"\n",
"\n",
"def collect_pr_tv(model, X, B, iou_thr=0.5):\n",
" \"\"\"Flags TP/FP des detections torchvision, meme matching glouton que 4.2c.\"\"\"\n",
" \"\"\"Flags TP/FP des detections torchvision, meme matching glouton que 4.4.\"\"\"\n",
" tp_s, fp_s, ngt = [], [], 0\n",
" model.eval()\n",
" with torch.no_grad():\n",
Expand Down Expand Up @@ -1036,7 +1036,7 @@
"\n",
"\n",
"TRAIN_T = {\"faster-rcnn\": T_FRCNN, \"retinanet\": T_RETINA, \"fcos\": T_FCOS}\n",
"rows = [(\"4.2c AnchorNet (from scratch)\", 74_717, 0.853, 0.914, None,\n",
"rows = [(\"4.4 AnchorNet (from scratch)\", 74_717, 0.853, 0.914, None,\n",
" \"2000 img x 12 ep en 79 s\")]\n",
"for name in MODELS:\n",
" rows.append((name, sum(p.numel() for p in MODELS[name].parameters()),\n",
Expand Down
24 changes: 12 additions & 12 deletions MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.5b-Detection-SOTA-Ultralytics.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -102,7 +102,7 @@
"print(\"device:\", \"cuda\" if DEVICE == 0 else \"cpu\",\n",
" \"| torch\", torch.__version__, \"| ultralytics\", ultralytics.__version__)\n",
"\n",
"IMG = 96 # cote de l'image : identique au 4.2c (multiple de 32, impose par imgsz)"
"IMG = 96 # cote de l'image : identique au 4.4 (multiple de 32, impose par imgsz)"
]
},
{
Expand Down Expand Up @@ -393,8 +393,8 @@
],
"source": [
"N_TRAIN, EPOCHS = 1000, 6\n",
"print(\"budget commun aux notebooks 4.2f et 4.2g :\",\n",
" N_TRAIN, \"images x\", EPOCHS, \"epoques (le 4.2c : 2000 x 12)\")\n",
"print(\"budget commun aux notebooks 4.5 et 4.5b :\",\n",
" N_TRAIN, \"images x\", EPOCHS, \"epoques (le 4.4 : 2000 x 12)\")\n",
"\n",
"from ultralytics.utils import LOGGER\n",
"LOGGER.setLevel(\"WARNING\") # tue les barres de progression et les chemins du scan ; les chiffres utiles sont captures par nos propres prints\n",
Expand Down Expand Up @@ -639,7 +639,7 @@
"\n",
"\n",
"def predict_boxes(model, idx):\n",
" \"\"\"Boites (N,4) xyxy + scores d'une image de validation, seuils du 4.2c.\"\"\"\n",
" \"\"\"Boites (N,4) xyxy + scores d'une image de validation, seuils du 4.4.\"\"\"\n",
" r = model.predict(str(DS / \"images\" / \"val\" / f\"{idx:05d}.png\"),\n",
" conf=0.5, iou=0.45, imgsz=IMG, verbose=False,\n",
" device=DEVICE)[0].boxes\n",
Expand All @@ -648,7 +648,7 @@
"\n",
"\n",
"def collect_pr_yolo(model, B, iou_thr=0.5):\n",
" \"\"\"Flags TP/FP des detections YOLO, meme matching glouton que 4.2c.\"\"\"\n",
" \"\"\"Flags TP/FP des detections YOLO, meme matching glouton que 4.4.\"\"\"\n",
" tp_s, fp_s, ngt = [], [], 0\n",
" for i in range(len(B)):\n",
" dets, dscores = predict_boxes(model, i)\n",
Expand Down Expand Up @@ -844,21 +844,21 @@
" return (time.time() - t0) * 1000.0 / n\n",
"\n",
"\n",
"ROWS_B = [ # bloc A puis bloc B -- nombres 4.2c/4.2f committes, YOLO mesures ci-dessus\n",
" (\"4.2c AnchorNet (from scratch)\", 74_717, None, 0.853, 0.914, None,\n",
"ROWS_B = [ # bloc A puis bloc B -- nombres 4.4/4.5 committes, YOLO mesures ci-dessus\n",
" (\"4.4 AnchorNet (from scratch)\", 74_717, None, 0.853, 0.914, None,\n",
" \"2000 x 12 en 79 s\", 404),\n",
" (\"4.2f faster-rcnn\", 18_930_229, None, 0.909, 0.997, 17.7,\n",
" (\"4.5 faster-rcnn\", 18_930_229, None, 0.909, 0.997, 17.7,\n",
" \"1000 x 6 en 73 s\", 297),\n",
" (\"4.2f retinanet\", 32_168_694, None, 0.997, 1.000, 29.0,\n",
" (\"4.5 retinanet\", 32_168_694, None, 0.997, 1.000, 29.0,\n",
" \"1000 x 6 en 109 s\", 297),\n",
" (\"4.2f fcos\", 32_064_455, None, 0.909, 0.998, 36.3,\n",
" (\"4.5 fcos\", 32_064_455, None, 0.909, 0.998, 36.3,\n",
" \"1000 x 6 en 107 s\", 297),\n",
"]\n",
"for name, model, params, flops, t in ((\"yolo11n\", MODEL_N, params_n, flops_n, T_N),\n",
" (\"yolo11s\", MODEL_S, params_s, flops_s, T_S),\n",
" (\"yolov5nu\", MODEL_V5N, params_v5n, flops_v5n, T_V5N),\n",
" (\"yolov8s\", MODEL_V8S, params_v8s, flops_v8s, T_V8S)):\n",
" ROWS_B.append((f\"4.2g {name}\", params, flops,\n",
" ROWS_B.append((f\"4.5b {name}\", params, flops,\n",
" RESULTS[name][\"ap07\"], RESULTS[name][\"ap10\"],\n",
" latency_ms_yolo(model),\n",
" f\"{N_TRAIN} x {EPOCHS} en {t:.0f} s\", 267))\n",
Expand Down Expand Up @@ -935,7 +935,7 @@
" \"\"\"Precision / rappel / nb de detections a un seuil de confiance donne.\n",
"\n",
" # Etape 1 : predict_boxes avec le parametre conf voulu (les seuils se passent\n",
" # a l'appel ici, pas a la construction comme en 4.2f)\n",
" # a l'appel ici, pas a la construction comme en 4.5)\n",
" # Etape 2 : meme matching glouton, retour (precision, rappel, n_detections)\n",
" \"\"\"\n",
" return None # TODO etudiant\n",
Expand Down
16 changes: 8 additions & 8 deletions MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.5c-YOLOv5-Bench-Ultralytics.ipynb
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Expand Up @@ -74,7 +74,7 @@
"print(\"device:\", \"cuda\" if DEVICE == 0 else \"cpu\",\n",
" \"| torch\", torch.__version__, \"| ultralytics\", ultralytics.__version__)\n",
"\n",
"IMG = 96 # terrain meme cote qu'en 4.2c/4.2f/4.2g (multiple de 32)"
"IMG = 96 # terrain meme cote qu'en 4.4/4.5/4.5b (multiple de 32)"
]
},
{
Expand Down Expand Up @@ -313,7 +313,7 @@
],
"source": [
"N_TRAIN, EPOCHS = 1000, 6\n",
"print(\"budget commun 4.2f / 4.2g / 4.2h :\", N_TRAIN, \"images x\", EPOCHS, \"epoques\")\n",
"print(\"budget commun 4.5 / 4.5b / 4.5c :\", N_TRAIN, \"images x\", EPOCHS, \"epoques\")\n",
"\n",
"from ultralytics.utils import LOGGER\n",
"LOGGER.setLevel(\"ERROR\")\n",
Expand Down Expand Up @@ -402,7 +402,7 @@
"\n",
"\n",
"def predict_boxes(model, idx):\n",
" \"\"\"Boites (N,4) xyxy + scores d'une image, seuils du 4.2c.\"\"\"\n",
" \"\"\"Boites (N,4) xyxy + scores d'une image, seuils du 4.4.\"\"\"\n",
" r = model.predict(str(DS / \"images\" / \"val\" / f\"{idx:05d}.png\"),\n",
" conf=0.5, iou=0.45, imgsz=IMG, verbose=False,\n",
" device=DEVICE)[0].boxes\n",
Expand Down Expand Up @@ -573,7 +573,7 @@
" return out\n",
"\n",
"\n",
"# Mesure mAP50-95 COCO integre sur le split val du 4.2h\n",
"# Mesure mAP50-95 COCO integre sur le split val du 4.5c\n",
"map_coco = ap_coco_per_image(MODEL_5N, [b.tolist() for b in Bva])\n",
"print(\"mAP@0.5:0.95 (COCO integre, 10 IoU) -- yolov5nu :\")\n",
"for thr in np.linspace(0.50, 0.95, 10):\n",
Expand Down Expand Up @@ -662,11 +662,11 @@
"\n",
"lat_5n = latency_ms_yolo(MODEL_5N)\n",
"\n",
"# valeurs committées dans le 4.2g -- source: tableau bloc B l.723-724\n",
"# valeurs committées dans le 4.5b -- source: tableau bloc B l.723-724\n",
"ROWS_45H = [\n",
" (\"4.2g yolo11n\", 2_590_035, 0.1, 0.909, 0.989, 10.3, f\"{N_TRAIN} x {EPOCHS} en 57 s\"),\n",
" (\"4.2g yolo11s\", 9_428_179, 0.5, 0.909, 0.993, 10.8, f\"{N_TRAIN} x {EPOCHS} en 54 s\"),\n",
" (\"4.2h yolov5nu\", params_5n, flops_5n, ap07, ap10, lat_5n, f\"{N_TRAIN} x {EPOCHS} en {T_5N:.0f} s\"),\n",
" (\"4.5b yolo11n\", 2_590_035, 0.1, 0.909, 0.989, 10.3, f\"{N_TRAIN} x {EPOCHS} en 57 s\"),\n",
" (\"4.5b yolo11s\", 9_428_179, 0.5, 0.909, 0.993, 10.8, f\"{N_TRAIN} x {EPOCHS} en 54 s\"),\n",
" (\"4.5c yolov5nu\", params_5n, flops_5n, ap07, ap10, lat_5n, f\"{N_TRAIN} x {EPOCHS} en {T_5N:.0f} s\"),\n",
"]\n",
"print(f\"\\n{'modele':12s} {'params':>11s} {'GFLOPs':>7s} {'VOC07':>6s} {'VOC10':>6s} {'ms/img':>7s} budget\")\n",
"for r in ROWS_45H:\n",
Expand Down
30 changes: 15 additions & 15 deletions MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.5d-Detection-SOTA-LibreYOLO.ipynb
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Expand Up @@ -99,7 +99,7 @@
"print(\"device:\", \"cuda\" if DEVICE == 0 else \"cpu\",\n",
" \"| torch\", torch.__version__, \"| libreyolo\", _pkg_version(\"libreyolo\"))\n",
"\n",
"IMG = 96 # cote de l'image : identique au 4.2c (multiple de 32, impose par imgsz)"
"IMG = 96 # cote de l'image : identique au 4.4 (multiple de 32, impose par imgsz)"
]
},
{
Expand Down Expand Up @@ -802,8 +802,8 @@
],
"source": [
"N_TRAIN, EPOCHS = 1000, 6\n",
"print(\"budget commun aux carnets 4.2f/4.2g/4.2j :\",\n",
" N_TRAIN, \"images x\", EPOCHS, \"epoques (le 4.2c : 2000 x 12)\")\n",
"print(\"budget commun aux carnets 4.5/4.5b/4.5d :\",\n",
" N_TRAIN, \"images x\", EPOCHS, \"epoques (le 4.4 : 2000 x 12)\")\n",
"\n",
"\n",
"def train_ly(weights, run, nb_classes=1):\n",
Expand Down Expand Up @@ -1288,7 +1288,7 @@
"\n",
"\n",
"def predict_boxes_ly(model, idx):\n",
" \"\"\"Boites (N,4) xyxy + scores d'une image de validation, seuils du 4.2c.\"\"\"\n",
" \"\"\"Boites (N,4) xyxy + scores d'une image de validation, seuils du 4.4.\"\"\"\n",
" r = model.predict(str(DS / \"images\" / \"val\" / f\"{idx:05d}.png\"),\n",
" conf=0.5, iou=0.45, imgsz=IMG, device=DEVICE)\n",
" res = r[0] if isinstance(r, list) else r\n",
Expand All @@ -1297,7 +1297,7 @@
"\n",
"\n",
"def collect_pr_ly(model, B, iou_thr=0.5):\n",
" \"\"\"Flags TP/FP des detections, meme matching glouton que 4.2c/4.2g.\"\"\"\n",
" \"\"\"Flags TP/FP des detections, meme matching glouton que 4.4/4.5b.\"\"\"\n",
" tp_s, fp_s, ngt = [], [], 0\n",
" for i in range(len(B)):\n",
" dets, dscores = predict_boxes_ly(model, i)\n",
Expand Down Expand Up @@ -1512,27 +1512,27 @@
" if ln.strip() and not ln.strip().startswith(\"#\"))\n",
"print(\"lignes effectives de ce carnet :\", LOC_42J)\n",
"\n",
"ROWS = [ # bloc A puis bloc B -- nombres 4.2c/4.2f/4.2g committes, 4.2j mesures ci-dessus\n",
" (\"4.2c AnchorNet (from scratch)\", 74_717, None, 0.853, 0.914, None,\n",
"ROWS = [ # bloc A puis bloc B -- nombres 4.4/4.5/4.5b committes, 4.5d mesures ci-dessus\n",
" (\"4.4 AnchorNet (from scratch)\", 74_717, None, 0.853, 0.914, None,\n",
" \"2000 x 12 en 79 s\", 404, \"n.r\", \"aucune (notre code)\"),\n",
" (\"4.2f faster-rcnn\", 18_930_229, None, 0.909, 0.997, 17.7,\n",
" (\"4.5 faster-rcnn\", 18_930_229, None, 0.909, 0.997, 17.7,\n",
" \"1000 x 6 en 73 s\", 297, \"BSD-3\", \"BSD-3\"),\n",
" (\"4.2f retinanet\", 32_168_694, None, 0.997, 1.000, 29.0,\n",
" (\"4.5 retinanet\", 32_168_694, None, 0.997, 1.000, 29.0,\n",
" \"1000 x 6 en 109 s\", 297, \"BSD-3\", \"BSD-3\"),\n",
" (\"4.2f fcos\", 32_064_455, None, 0.909, 0.998, 36.3,\n",
" (\"4.5 fcos\", 32_064_455, None, 0.909, 0.998, 36.3,\n",
" \"1000 x 6 en 107 s\", 297, \"BSD-3\", \"BSD-3\"),\n",
" (\"4.2g yolo11n\", 2_590_035, 0.1, 0.909, 0.989, 20.6,\n",
" (\"4.5b yolo11n\", 2_590_035, 0.1, 0.909, 0.989, 20.6,\n",
" \"1000 x 6 en 111 s\", 267, \"AGPL-3.0\", \"AGPL-3.0\"),\n",
" (\"4.2g yolo11s\", 9_428_179, 0.5, 0.909, 0.995, 23.0,\n",
" (\"4.5b yolo11s\", 9_428_179, 0.5, 0.909, 0.995, 23.0,\n",
" \"1000 x 6 en 92 s\", 267, \"AGPL-3.0\", \"AGPL-3.0\"),\n",
" (\"4.2g yolov5nu\", 2_508_659, 0.2, 0.909, 0.986, 18.5,\n",
" (\"4.5b yolov5nu\", 2_508_659, 0.2, 0.909, 0.986, 18.5,\n",
" \"1000 x 6 en 93 s\", 267, \"AGPL-3.0\", \"GPL-3.0\"),\n",
" (\"4.2g yolov8s\", 11_135_987, 0.6, 0.909, 0.996, 18.1,\n",
" (\"4.5b yolov8s\", 11_135_987, 0.6, 0.909, 0.996, 18.1,\n",
" \"1000 x 6 en 90 s\", 267, \"AGPL-3.0\", \"AGPL-3.0\"),\n",
"]\n",
"for name, model, info, t, ck in ((\"yolov9t\", MODEL_Y9T, info_y9t, T_Y9T, \"LibreYOLO9t\"),\n",
" (\"yolov9t-e2e\", MODEL_E2E, info_e2e, T_E2E, \"LibreYOLO9E2ET\")):\n",
" ROWS.append((f\"4.2j {name}\", info[\"parameters\"], GFLOPS[name],\n",
" ROWS.append((f\"4.5d {name}\", info[\"parameters\"], GFLOPS[name],\n",
" RESULTS[name][\"ap07\"], RESULTS[name][\"ap10\"],\n",
" latency_ms_ly(model),\n",
" f\"{N_TRAIN} x {EPOCHS} en {t:.0f} s\", LOC_42J,\n",
Expand Down
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