Publish Forma3D Tiny 4 with validated M4 training results

.gitattributes

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+examples/abo-B073P1N45L-v0.gif filter=lfs diff=lfs merge=lfs -text
+examples/abo-B07MBFDHMH-v3.gif filter=lfs diff=lfs merge=lfs -text
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+examples/abo-B082XLWY2S-v1.gif filter=lfs diff=lfs merge=lfs -text
+examples/abo-B082XLWY2S-v3.gif filter=lfs diff=lfs merge=lfs -text
+splits/objects1024.jsonl filter=lfs diff=lfs merge=lfs -text

LICENSE

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NOTICE

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+Forma3D-Tiny v0.2.0, derived from Image3D Student v0.1.0
+Copyright 2026 Lucasgitguy-co.
+
+Original project source, documentation and procedural demonstration artifacts are released under Apache License 2.0.
+
+The optional Idol integration and Gaussian attribute conventions were developed with reference to
+speridlabs/idol-sdk, revision 4702a7f00850e34b6e83a2170cc123c3e9570744 (Apache-2.0).
+Its architecture is imported as an optional dependency; pretrained teacher weights are not redistributed.
+Adaptations in this project: staged loading, query-chunk attention, explicit real-valued RoPE selection,
+independent student training, coordinate-preserving Gaussian IO, and diagnostic rendering.
+Upstream attribution is retained in licenses/Idol-APACHE-2.0.txt and THIRD_PARTY.md.
+
+ABO continuation data: Amazon.com, Amazon Berkeley Objects contributors; converted to GLB by CodePawl. CC BY 4.0. Training preprocessing decompresses geometry, normalizes objects, samples surface colors and renders Gaussian proxies. See ATTRIBUTIONS.json and licenses/LICENSE-ABO-CC-BY-4.0.txt. No endorsement implied.

README.md

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+---
+license: apache-2.0
+pipeline_tag: image-to-3d
+base_model: Lucasgitguy-co/forma3d-tiny-3
+base_model_relation: finetune
+model_name: Forma3D Tiny 4
+datasets:
+- codepawl/abo-glb
+tags:
+- pytorch
+- gaussian-splatting
+- mesh-proxy-supervision
+- research-prototype
+---
+# Forma3D Tiny 4
+
+A 306,321-parameter image-to-Gaussian research model continued from
+[Forma3D Tiny 3](https://huggingface.co/Lucasgitguy-co/forma3d-tiny-3) at
+`3469f527c5f6833d654f62653ee92e0f73854b33`. The architecture remains a 16³ grid,
+width 64 and at most 4,096 Gaussian slots. Trained locally on an M4 MacBook Air
+with 16 GB unified memory using PyTorch MPS, float32 and batch size 8.
+
+## Use
+
+Clone and install with Python 3.12. Git LFS is needed to fetch the checkpoint:
+
+```bash
+git lfs install
+git clone https://huggingface.co/Lucasgitguy-co/forma3d-tiny-4
+cd forma3d-tiny-4
+python3.12 -m venv .venv
+source .venv/bin/activate
+pip install .
+image3d generate --checkpoint model.pt --image examples/abo-B071FJR479-v3-input.png --out object.ply --device mps --threshold 0.6
+image3d preview --ply object.ply --out preview.png
+```
+
+Use `--device cpu` on systems without Metal. Replace the sample input with an actual
+image path to try other inputs. The recommended threshold is **0.6**; the inherited
+CLI defaults to 0.5, so pass the flag explicitly. `generation_config.json` is
+informational and is not automatically read by the CLI. Output is a Gaussian PLY,
+not a textured mesh. No text prompting is supported.
+
+## Training and selection
+
+The expanded dataset contains 1,024 ABO objects across all splits, not 1,024 training
+objects. Training uses 677 objects with 12 views each (8,124 inputs): four azimuths at
+three elevations. The original 512-object split assignments are preserved; duplicate
+identities are grouped before new objects are assigned. The common validation set
+has 87 objects / 348 views. An additional 100 objects / 400 views are reserved for
+final confirmation. The historical test set is excluded from tuning.
+
+Three variants start from the same v3 checkpoint: original data, 12-view data,
+and expanded-object data. Each receives 6,000 AdamW steps at learning rate 5e-5.
+Selection maximizes validation silhouette IoU subject to nonworsening occupancy IoU
+and Chamfer distance, with thresholds 0.3, 0.4, 0.5 and 0.6 tuned on validation.
+The expanded-object variant won. `model.pt` is seed 44, step 6000; `repeat/model.pt`
+is its independently trained seed-45 confirmation. The initial winner was fixed
+before inspecting final confirmation results.
+
+## Held-out confirmation results
+
+All rows use the same 100 held-out objects / 400 proxy views. Higher IoU and lower
+Chamfer distance are better. The first two rows isolate the checkpoint change at
+threshold 0.5; the third includes the validation-selected threshold change.
+
+| Checkpoint | Threshold | Silhouette IoU | Occupancy IoU | Chamfer distance |
+|---|---:|---:|---:|---:|
+| Original v3 | 0.5 | 0.463688 | 0.337398 | 0.069585 |
+| Tiny 4, seed 44 | 0.5 | 0.512971 | 0.424836 | 0.053612 |
+| Tiny 4, seed 44 (recommended) | 0.6 | 0.540550 | 0.434657 | 0.052380 |
+| Tiny 4, seed 45 (repeat) | 0.6 | 0.546573 | 0.412526 | 0.054105 |
+
+For recommended seed 44 versus original v3 at 0.5, paired object-cluster bootstrap
+95% intervals (2,000 resamples) for candidate-minus-baseline changes are:
+silhouette IoU **+0.07686 [0.06621, 0.08926]**, occupancy IoU
+**+0.09726 [0.07289, 0.12272]**, and Chamfer
+**−0.01721 [−0.03762, −0.00622]**. These intervals include the threshold change.
+Second-seed intervals also exclude zero in the favorable direction for all three
+metrics. Detailed scores and per-example results are in `evaluation/` and
+`evaluation.json`; splits and source metadata are in `splits/`.
+
+The 15-minute batch-8 training benchmark measured 650.02 samples/s; this excludes
+preparation and validation overhead. It recorded about 496 MiB peak process RSS
+and 141 MiB MPS driver allocation; these overlap in unified memory and are not a
+measurement of total system memory. CPU/MPS loss and gradient parity passed.
+
+## Examples and limitations
+
+Each turntable shows the predicted Gaussian preview on the left and a reference
+mesh silhouette on the right. The six examples include the two lowest, two median
+and two highest silhouette-IoU views in the confirmation set; they are not random
+samples. The reference image is a silhouette, not a photorealistic rendering.
+
+![abo-B071FJR479-v3: prediction and reference silhouette](examples/abo-B071FJR479-v3.gif)
+
+![abo-B073P1N45L-v0: prediction and reference silhouette](examples/abo-B073P1N45L-v0.gif)
+
+![abo-B07MBFDHMH-v3: prediction and reference silhouette](examples/abo-B07MBFDHMH-v3.gif)
+
+![abo-B07TX2D39D-v3: prediction and reference silhouette](examples/abo-B07TX2D39D-v3.gif)
+
+![abo-B082XLWY2S-v1: prediction and reference silhouette](examples/abo-B082XLWY2S-v1.gif)
+
+![abo-B082XLWY2S-v3: prediction and reference silhouette](examples/abo-B082XLWY2S-v3.gif)
+
+Inputs are orthographic Gaussian-proxy renders on black. Natural photographs,
+photorealistic rendering, arbitrary viewpoints, thin structures and hidden surfaces
+remain unvalidated. Centering an ordinary photo does not make it an in-domain input.
+Chamfer measures symmetric mean Euclidean distance between predicted Gaussian centers
+and mesh-surface samples in normalized coordinates, not reconstructed mesh quality.
+Silhouettes compare alpha>=0.5 splat rasterization with triangle rasterization at
+96 pixels from four fixed unseen viewpoints. Near duplicates may remain; ancestor
+training exposure is not fully known. These measurements do not establish broad
+real-world reconstruction quality.
+
+## Reproduction and attribution
+
+`recipe/` contains the training/evaluation implementation and dependency lock.
+Original source and weight lineage is Apache 2.0; retain LICENSE, NOTICE and
+THIRD_PARTY.md. ABO assets and derived example imagery are credited to Amazon.com
+and Amazon Berkeley Objects contributors under CC BY 4.0, with GLB conversion by
+CodePawl. Geometry was normalized, sampled and rendered into proxy inputs and
+silhouettes. See ATTRIBUTIONS.json, attribution/INHERITED.json,
+licenses/LICENSE-ABO-CC-BY-4.0.txt and the
+[dataset](https://huggingface.co/datasets/codepawl/abo-glb).
+Raw GLB meshes and the full training image cache are not bundled.

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THIRD_PARTY.md

@@ -0,0 +1,13 @@
+# Third-party scope
+
+This training kit extends Lucasgitguy-co/image3d-student, Apache-2.0. The original
+checkpoint is downloaded separately. No new trained weights are bundled in the kit.
+
+Optional Idol SDK integration is inherited from the original project, pinned to
+`4702a7f00850e34b6e83a2170cc123c3e9570744`; its Apache notice is retained in
+`licenses/Idol-APACHE-2.0.txt`. The Mac recipe does not install or run Idol, Hi3D,
+InstantMesh, Hunyuan, or gated encoders. Their weights are not redistributed.
+
+Dependencies retain their respective licenses. Third-party dataset terms are
+documented in DATASETS.md; per-asset attribution is generated during preparation
+and copied into any published release. Code licensing does not relicense datasets.

config.json

@@ -0,0 +1,4 @@
+{
+  "grid": 16,
+  "width": 64
+}

evaluation.json

@@ -0,0 +1,118 @@
+{
+  "selection": {
+    "variant": "objects1024",
+    "checkpoint": "local-run/runs/forma/experiments/objects1024-seed44/best.pt",
+    "threshold": 0.6,
+    "repeat_checkpoint": "local-run/runs/forma/experiments/objects1024-seed45/best.pt",
+    "repeat_threshold": 0.6
+  },
+  "runs": {
+    "selected": {
+      "score": {
+        "loss": 0.5288282543048263,
+        "occupancy_iou": 0.434657429975929,
+        "silhouette_iou": 0.5405500827612161,
+        "chamfer": 0.05237952823786939,
+        "latency_seconds": 0.007597888194723055,
+        "threshold": 0.6,
+        "examples": 400,
+        "empty_outputs": 0,
+        "manifest_sha256": "3e8cbaf375a295064f46df792e428e409aef384d5f4983c534b17ccddef9db91",
+        "rss_bytes": 414646272,
+        "peak_rss_bytes": 435437568
+      },
+      "checkpoint_sha256": "ed7820ccd07b1a0fdbb17a95476f45892e2bd4ea7eba6c593e995845d9a9560d",
+      "ci": {
+        "silhouette_iou": {
+          "delta_candidate_minus_baseline": 0.07686164669613241,
+          "ci95": [
+            0.06620625277555024,
+            0.08925644709590333
+          ],
+          "clusters": 100,
+          "bootstrap_repeats": 2000
+        },
+        "occupancy_iou": {
+          "delta_candidate_minus_baseline": 0.09725900993297572,
+          "ci95": [
+            0.07289312401256197,
+            0.12272462646698633
+          ],
+          "clusters": 100,
+          "bootstrap_repeats": 2000
+        },
+        "chamfer": {
+          "delta_candidate_minus_baseline": -0.017205378974122443,
+          "ci95": [
+            -0.03761618323597912,
+            -0.0062196922360514394
+          ],
+          "clusters": 100,
+          "bootstrap_repeats": 2000
+        }
+      }
+    },
+    "repeat": {
+      "score": {
+        "loss": 0.5437050361186266,
+        "occupancy_iou": 0.4125259027257265,
+        "silhouette_iou": 0.5465734927516692,
+        "chamfer": 0.05410549798496925,
+        "latency_seconds": 0.007690862412564456,
+        "threshold": 0.6,
+        "examples": 400,
+        "empty_outputs": 0,
+        "manifest_sha256": "3e8cbaf375a295064f46df792e428e409aef384d5f4983c534b17ccddef9db91",
+        "rss_bytes": 420741120,
+        "peak_rss_bytes": 435437568
+      },
+      "checkpoint_sha256": "a576d652928faa265c221bedac7fdbce1d8e8be8a8c7b9ec2d5cff240d28021c",
+      "ci": {
+        "silhouette_iou": {
+          "delta_candidate_minus_baseline": 0.08288505668658566,
+          "ci95": [
+            0.07360989200676811,
+            0.09423433733451456
+          ],
+          "clusters": 100,
+          "bootstrap_repeats": 2000
+        },
+        "occupancy_iou": {
+          "delta_candidate_minus_baseline": 0.07512748268277328,
+          "ci95": [
+            0.0540098029584061,
+            0.0983109243792212
+          ],
+          "clusters": 100,
+          "bootstrap_repeats": 2000
+        },
+        "chamfer": {
+          "delta_candidate_minus_baseline": -0.0154794092270226,
+          "ci95": [
+            -0.035662256027738926,
+            -0.004783220070014258
+          ],
+          "clusters": 100,
+          "bootstrap_repeats": 2000
+        }
+      }
+    }
+  },
+  "baseline": {
+    "loss": 0.5606700345873833,
+    "occupancy_iou": 0.33739842004295334,
+    "silhouette_iou": 0.46368843606508364,
+    "chamfer": 0.06958490721199183,
+    "latency_seconds": 0.00743420758168213,
+    "threshold": 0.5,
+    "examples": 400,
+    "empty_outputs": 2,
+    "manifest_sha256": "3e8cbaf375a295064f46df792e428e409aef384d5f4983c534b17ccddef9db91",
+    "rss_bytes": 433438720,
+    "peak_rss_bytes": 433422336
+  },
+  "batch": {
+    "batch": 8
+  },
+  "limitations": "ABO Gaussian-proxy input only. Chamfer is center-to-mesh-surface-sample distance; silhouettes use orthographic alpha>=0.5 vs triangle rasterization at 96px. Not photorealistic or natural-photo evaluation."
+}

evaluation/benchmark-16.json

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+{
+  "batch": 16,
+  "steps": 35401,
+  "seconds": 900.0236777910031,
+  "samples_per_second": 629.334554164395,
+  "steps_per_second": 39.333409635274684,
+  "mps_driver_bytes": 1187954688,
+  "rss_bytes": 526614528,
+  "peak_rss_bytes": 526614528
+}

evaluation/benchmark-4.json

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+{
+  "batch": 4,
+  "steps": 104888,
+  "seconds": 900.003167249728,
+  "samples_per_second": 466.16724836878825,
+  "steps_per_second": 116.54181209219706,
+  "mps_driver_bytes": 105824256,
+  "rss_bytes": 517701632,
+  "peak_rss_bytes": 517750784
+}

evaluation/benchmark-8.json

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+{
+  "batch": 8,
+  "steps": 73128,
+  "seconds": 900.0087133753113,
+  "samples_per_second": 650.0203734761399,
+  "steps_per_second": 81.25254668451748,
+  "mps_driver_bytes": 147767296,
+  "rss_bytes": 519733248,
+  "peak_rss_bytes": 519766016
+}

evaluation/confirmation-baseline/confirmation.json

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+{
+  "loss": 0.5606700345873833,
+  "occupancy_iou": 0.33739842004295334,
+  "silhouette_iou": 0.46368843606508364,
+  "chamfer": 0.06958490721199183,
+  "latency_seconds": 0.00743420758168213,
+  "threshold": 0.5,
+  "examples": 400,
+  "empty_outputs": 2,
+  "manifest_sha256": "3e8cbaf375a295064f46df792e428e409aef384d5f4983c534b17ccddef9db91",
+  "rss_bytes": 433438720,
+  "peak_rss_bytes": 433422336
+}

evaluation/confirmation-repeat/confirmation.json

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+{
+  "loss": 0.5437050361186266,
+  "occupancy_iou": 0.4125259027257265,
+  "silhouette_iou": 0.5465734927516692,
+  "chamfer": 0.05410549798496925,
+  "latency_seconds": 0.007690862412564456,
+  "threshold": 0.6,
+  "examples": 400,
+  "empty_outputs": 0,
+  "manifest_sha256": "3e8cbaf375a295064f46df792e428e409aef384d5f4983c534b17ccddef9db91",
+  "rss_bytes": 420741120,
+  "peak_rss_bytes": 435437568
+}

evaluation/confirmation-repeat/historical-threshold.json

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+{
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evaluation/confirmation-selected/confirmation.json

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evaluation/confirmation-selected/historical-threshold.json

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evaluation/experiments/control-seed44/settings.json

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evaluation/experiments/control-seed44/threshold-0.3.json

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evaluation/experiments/control-seed44/threshold-0.4.json

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evaluation/experiments/control-seed44/threshold-0.5.json

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evaluation/experiments/control-seed44/threshold-0.6.json

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evaluation/experiments/control-seed44/threshold-selection.json

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evaluation/experiments/control-seed44/validation-1000.json

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evaluation/experiments/control-seed44/validation-2000.json

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evaluation/experiments/control-seed44/validation-3000.json

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evaluation/experiments/control-seed44/validation-4000.json

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evaluation/experiments/control-seed44/validation-5000.json

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evaluation/experiments/control-seed44/validation-6000.json

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evaluation/experiments/objects1024-seed44/complete.json

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evaluation/experiments/objects1024-seed44/settings.json

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evaluation/experiments/objects1024-seed44/threshold-0.3.json

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evaluation/experiments/objects1024-seed44/threshold-0.4.json

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evaluation/experiments/objects1024-seed44/threshold-0.5.json

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evaluation/experiments/objects1024-seed44/threshold-0.6.json

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evaluation/experiments/objects1024-seed44/threshold-selection.json

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evaluation/experiments/objects1024-seed44/validation-1000.json

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evaluation/experiments/objects1024-seed44/validation-2000.json

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evaluation/experiments/objects1024-seed44/validation-3000.json

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evaluation/experiments/objects1024-seed44/validation-4000.json

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evaluation/experiments/objects1024-seed44/validation-5000.json

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evaluation/experiments/objects1024-seed44/validation-6000.json

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