Colorizer round6: matched recurrent trial step 2000

experiments/recurrent-refinement-20260928/SHA256SUMS.json

@@ -1,5 +1,5 @@
 {
-  "control/optimizer.pt": "95ab2cfbee8a3c4648c7c8cfb0da25da2e70a3336c668cd5aeca6c09224de305",
+  "control/optimizer.pt": "ef8922577445f7e96f96282ab8b42841216dbe7123d0bc7fee900797f05aef16",
   "control/step_00500/README.md": "2508e51f14898f651c49adb184d1570b8adad3702313dee9bdac6fa0390d2bec",
   "control/step_00500/config.json": "eaeb49b39f9116bfed0d954e81852dc93476298752787dba88a63c0eec97ccec",
   "control/step_00500/model.safetensors": "71bd13e6357d578701b700ba6397c213beed956f4b0a42a0fe74ac0e1f2f1347",
@@ -9,14 +9,17 @@
   "control/step_01500/README.md": "2508e51f14898f651c49adb184d1570b8adad3702313dee9bdac6fa0390d2bec",
   "control/step_01500/config.json": "eaeb49b39f9116bfed0d954e81852dc93476298752787dba88a63c0eec97ccec",
   "control/step_01500/model.safetensors": "1bf9274ab1af80d7755e8d14599b3f424b675c6debaf0be1d63ab8c214be470d",
+  "control/step_02000/README.md": "2508e51f14898f651c49adb184d1570b8adad3702313dee9bdac6fa0390d2bec",
+  "control/step_02000/config.json": "eaeb49b39f9116bfed0d954e81852dc93476298752787dba88a63c0eec97ccec",
+  "control/step_02000/model.safetensors": "13b8e3a26dc2410605536c9b376f560500ccecfa7b42d5e1e19e48997303e701",
   "data_and_eval.py": "019ea992052f0233954b86e06e31c85c74ca313e67a55ff7fb03248f8874aaf5",
-  "history.json": "9918882412251b0b3b7d9c02c908682b5a2555d820e2b80fc1b64715c00a5166",
+  "history.json": "f13efc6abed5fc86800ddd1c2a51da3132deefb20196a1a0c41c9221fe6c492e",
   "initial_recurrent/README.md": "effe6813751284db86fdf09a1885156ede661692458b81cc8f8d0f8b89d1ade7",
   "initial_recurrent/config.json": "3a5e6985d1bf6266684fb5daaeb17a6b93879639b33ffd1dd08682281cfaeed8",
   "initial_recurrent/model.safetensors": "ec1f27d74533adc83f7ab3639a091fc4d8738a434dafc7d172c7873c28a9e715",
   "manifest.json": "7310305df891aec3c42f0a6d6299d15ae68b9fdfa8b3a40c22f2bf50afb13bba",
   "protocol.json": "849e0b7c57129ff625a5dc2053540b96e7c9d35b8e8b11ab2e19bc83f83273de",
-  "recurrent/optimizer.pt": "d0a6645b77d89bbf609ced7d362c25b238e7b6c790a3caa39da20bf735b93a5d",
+  "recurrent/optimizer.pt": "ae7997e7c07241ce2c00cd7277471660d14c4272f83f400bd8f0ccdfe1bddacb",
   "recurrent/step_00500/README.md": "effe6813751284db86fdf09a1885156ede661692458b81cc8f8d0f8b89d1ade7",
   "recurrent/step_00500/config.json": "3a5e6985d1bf6266684fb5daaeb17a6b93879639b33ffd1dd08682281cfaeed8",
   "recurrent/step_00500/model.safetensors": "e9c8c0e496a0c5cd2608267dfea0623d6bbdaef39d9b984ed119642497203999",
@@ -26,6 +29,9 @@
   "recurrent/step_01500/README.md": "effe6813751284db86fdf09a1885156ede661692458b81cc8f8d0f8b89d1ade7",
   "recurrent/step_01500/config.json": "3a5e6985d1bf6266684fb5daaeb17a6b93879639b33ffd1dd08682281cfaeed8",
   "recurrent/step_01500/model.safetensors": "15b19406295c595d6c1ab22cf9944956e180817809fffeb623fd22a84a8319e6",
+  "recurrent/step_02000/README.md": "effe6813751284db86fdf09a1885156ede661692458b81cc8f8d0f8b89d1ade7",
+  "recurrent/step_02000/config.json": "3a5e6985d1bf6266684fb5daaeb17a6b93879639b33ffd1dd08682281cfaeed8",
+  "recurrent/step_02000/model.safetensors": "0fe86ac4b831e4b9d21a36bd3b3c84288b3ea4f612927c933419f7e83da05db5",
   "smoke.json": "726c8a138881413981b06f80311ed14d1e4c65ba198f9ef5ee2fea0e0643364e",
   "source/data.py": "eb43b886fe45435841439c36646985f914386a48faa712fb6806accac2ad48e0",
   "source/legacy_eval.py": "dd7e8bd5f961dfb6f1c2ce98fac41824c5009188e306000b195e922e4ed03e58",
@@ -36,6 +42,6 @@
   "source/recurrent_model.py": "566c0b1a38522e1502f9df1e259d613a143243fa3e4db4522be09cbc0c0e9e9e",
   "source/semantic_model.py": "b019358cbcaa214743cbd244d20eb6e59eeff0ab5264093ec0927be8529b25a2",
   "source/spatial.py": "d13abe399ef049e21a6459a7003461afaae0c00e0c560262c6cf375de4c9884a",
-  "status.json": "6a2a66a54610c39de7671aa56245b3421c57415c52a69008886d485ef577e931",
+  "status.json": "4903f51903346b500e2940133e4e9b24a41a3b7d26957b00caa844cac38793a8",
   "train.py": "3f7f48d5251d2d793ec888a413a2db6671eb23ac8733e217dc099cf07dca55f9"
 }

experiments/recurrent-refinement-20260928/control/optimizer.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:95ab2cfbee8a3c4648c7c8cfb0da25da2e70a3336c668cd5aeca6c09224de305
+oid sha256:ef8922577445f7e96f96282ab8b42841216dbe7123d0bc7fee900797f05aef16
 size 32165643

experiments/recurrent-refinement-20260928/control/step_02000/README.md

@@ -0,0 +1,5 @@
+# Experimental semantic colorizer
+
+Head: palette. Parameters: 3994676.
+
+Not approved for production. Requires semantic_model.py; incompatible with the old U-Net loader. Input is Lab lightness normalized to [-1,1]; output is Lab ab. See the run protocol, provenance, selection and visual comparisons. Predictions are plausible colors, not recovered historical truth.

experiments/recurrent-refinement-20260928/control/step_02000/config.json

@@ -0,0 +1,7 @@
+{
+  "architecture": "SemanticColorizer",
+  "head": "palette",
+  "width": 128,
+  "queries": 16,
+  "format_version": 1
+}

experiments/recurrent-refinement-20260928/control/step_02000/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13b8e3a26dc2410605536c9b376f560500ccecfa7b42d5e1e19e48997303e701
+size 16112432

experiments/recurrent-refinement-20260928/history.json

@@ -193,5 +193,70 @@
       0.298503577709198
     ],
     "elapsed_seconds": 311.58550782999987
+  },
+  {
+    "step": 1600,
+    "control": [
+      0.3982578217983246
+    ],
+    "recurrent": [
+      0.39903050661087036,
+      0.3992690443992615,
+      0.4000121057033539,
+      0.40121719241142273
+    ],
+    "elapsed_seconds": 328.56815800999993
+  },
+  {
+    "step": 1700,
+    "control": [
+      0.31409478187561035
+    ],
+    "recurrent": [
+      0.3152289390563965,
+      0.3146025538444519,
+      0.31490465998649597,
+      0.3157505691051483
+    ],
+    "elapsed_seconds": 341.10662694999996
+  },
+  {
+    "step": 1800,
+    "control": [
+      0.3033559322357178
+    ],
+    "recurrent": [
+      0.3027579188346863,
+      0.3020375967025757,
+      0.3022533059120178,
+      0.3028109073638916
+    ],
+    "elapsed_seconds": 353.8785219659999
+  },
+  {
+    "step": 1900,
+    "control": [
+      0.2559379041194916
+    ],
+    "recurrent": [
+      0.25984421372413635,
+      0.2584242820739746,
+      0.2576269805431366,
+      0.25711339712142944
+    ],
+    "elapsed_seconds": 366.43284361199994
+  },
+  {
+    "step": 2000,
+    "control": [
+      0.306712806224823
+    ],
+    "recurrent": [
+      0.3068969249725342,
+      0.30691272020339966,
+      0.3072160482406616,
+      0.30757540464401245
+    ],
+    "elapsed_seconds": 379.0809403389999
   }
 ]

experiments/recurrent-refinement-20260928/recurrent/optimizer.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d0a6645b77d89bbf609ced7d362c25b238e7b6c790a3caa39da20bf735b93a5d
+oid sha256:ae7997e7c07241ce2c00cd7277471660d14c4272f83f400bd8f0ccdfe1bddacb
 size 32165643

experiments/recurrent-refinement-20260928/recurrent/step_02000/README.md

@@ -0,0 +1,3 @@
+# Experimental recurrent colorizer
+
+Requires source/recurrent_model.py and source/semantic_model.py. Load with load_recurrent(path). Parameters include encoder: 3,994,676. Shared decoder runs four passes by default; encoder runs once. Not production approved.

experiments/recurrent-refinement-20260928/recurrent/step_02000/config.json

@@ -0,0 +1,9 @@
+{
+  "architecture": "RecurrentColorizer",
+  "head": "palette",
+  "width": 128,
+  "queries": 16,
+  "format_version": 2,
+  "steps": 4,
+  "feedback": true
+}

experiments/recurrent-refinement-20260928/recurrent/step_02000/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0fe86ac4b831e4b9d21a36bd3b3c84288b3ea4f612927c933419f7e83da05db5
+size 16112432

experiments/recurrent-refinement-20260928/status.json

@@ -1,6 +1,6 @@
 {
   "status": "training",
-  "step": 1500,
-  "elapsed_seconds": 311.78318257499996,
+  "step": 2000,
+  "elapsed_seconds": 379.272534211,
   "production_approved": false
 }