Add model sidecars: KvasirSEG.pth.config.json, KvasirSEG.pth.provenance.json (tools/hf_sidecars.py)

KvasirSEG.pth.config.json

@@ -0,0 +1,31 @@
+{
+ "schema": "medotter/model-sidecar/v1",
+ "model": {
+  "name": "PraNet",
+  "args": {
+   "input_channel": 3,
+   "num_classes": 1
+  }
+ },
+ "inference": {
+  "size": [
+   256,
+   256
+  ],
+  "scale": 255,
+  "normalize": {
+   "mean": [
+    0.485,
+    0.456,
+    0.406
+   ],
+   "std": [
+    0.229,
+    0.224,
+    0.225
+   ]
+  },
+  "threshold": 0.5,
+  "output_select": "last"
+ }
+}

KvasirSEG.pth.provenance.json

@@ -0,0 +1,38 @@
+{
+ "schema": "medotter/hf-sidecars-provenance/v1",
+ "repo": "MedOtter/PraNet",
+ "revision": "37b8705d9b4b1bb3847d3ac97de3c36e47e6094e",
+ "filename": "KvasirSEG.pth",
+ "sha256": "9bd93b13b13048c29b2fb4bdba73fdf32c5c82b8db4c2a69db7146ef6a5e73c4",
+ "size": 209213935,
+ "upstream": {
+  "repo": "FengheTan9/U-Bench",
+  "path": "PraNet/Kvasir-SEG/checkpoint_best.pth",
+  "sha256_match": true
+ },
+ "training": {
+  "dataset_name": "Kvasir-SEG",
+  "img_size": 256,
+  "max_epochs": 300,
+  "do_deeps": 1,
+  "model_id": 39,
+  "seed": 41,
+  "base_lr": 0.01,
+  "batch_size": 8,
+  "epoch": 263
+ },
+ "preprocessing_class": "B",
+ "intensity_written": true,
+ "reason": "class B (Kvasir-SEG) intensity written",
+ "verification": {
+  "strict_load": "pass",
+  "device": "cpu",
+  "torch": "2.13.0+cpu",
+  "medotter": "0.1.0",
+  "parameters": 52054887,
+  "intensity_overridden_for_probe": [],
+  "verified_at": "2026-09-06T15:05:55+00:00"
+ },
+ "generator": "tools/hf_sidecars.py",
+ "generated_at": "2026-09-06T15:05:55+00:00"
+}