**Overall Verdict:** Accept — The submission is complete, technically coherent, safely reachable, and supported by strong artifact-level verification, with only minor documentation drift.

**Completeness Check:**
- Instruction: Complete — Defines the exact trial grid, training scope, selection formula, export constraints, CPU parity procedure, and deliverables.
- Inputs: Complete — Dataset counts, split table, latent caches, model architecture, configuration, and frozen model weights are present and structurally consistent.
- Toolchain: Complete — Provides a pinned, open-source Linux/Python/CUDA environment, offline runtime requirements, hardware sizing, and installation recipe.
- Reference output: Complete — Includes eight distinct checkpoint artifacts, complete metric tables, an externally stored ONNX model, baseline data, provenance summaries, and replay evidence.
- Rubric: Complete — Covers schemas, joins, thresholds, checkpoint scope and hashes, ONNX structure, independent parity replay, validation rebinding, integrity failures, and partial diagnostic scoring.

**File and Task Reachability:**
The attachment set is `reachable`, and the end-to-end task is `reachable`. Both declared attachments were inspected and match their ZIP/ZIP64 formats and manifest sizes. Their directories contain no encryption, traversal paths, absolute paths, duplicate names, symbolic links, device entries, or extreme expansion.

The input archive contains 3,221 members, including 3,200 PNGs. Inspected content included `README.md`, `task_config.json`, `unet_config.json`, the 400-row evaluation CSV, the 1,200-row training JSONL, all three supplied Python modules as static text, a representative matching RGB image and binary mask, both NPZ directories and all ten NPY headers, and headers from the backbone and five auxiliary Safetensors files. The CSV has 200 validation and 200 test rows with unique IDs. The caches contain the expected float16 `(N,4,32,32)` latents and uint8 `(N,256,256)` masks. The backbone header reports 945,393,668 float32 parameters.

The reference archive contains 46 members. Inspected content included the TSV, all three Parquet tables, baseline and trial JSON files, checkpoint fingerprints, ONNX metadata, CPU/CUDA parity summaries, relevant logs, grading modules, and solution modules as static text. All eight checkpoint headers expose the same 60 in-scope tensors totaling 56,252,416 parameters. The graded Parquets contain 200 metric rows and 1,600 CPU parity rows with the declared schemas. The ONNX graph directly confirms opset 17, symbolic batch axes, required input/output shapes, 479 external initializers, and exact accounting against the 3.780 GB external-data member.

The remaining individual image bodies, complete numerical tensor payloads, complete external-weight body, and ancillary authoring scripts were not exhaustively decoded. Uploaded programs were not executed; therefore, self-grader pass claims were assessed through static code, schema, graph, header, and log inspection rather than rerunning the submitted grader. This is a review-safety limitation, not an attachment defect.

The completion path is viable on one suitable machine: train the eight pinned trials on one GPU, evaluate and select the measured argmin, rebuild from a pristine backbone, export external-data ONNX, replay PyTorch and ONNX Runtime on CPU, emit the tables and checkpoints, then let the evaluator reopen all artifacts, replay selected samples, and rebind every validation row to its checkpoint.

License keys, accounts, tokens, and other credentials are not required from the contributor. Confidential, proprietary, or internal material may be evaluated as submitted evidence without requiring additional disclosure.

**Special Screening:**
None

**Recommended Changes:**
No material changes are required. As optional documentation cleanup, update the input `README.md` to list all 26 TSV fields and all ten parity columns, and replace the retained older 45-check run log with output from the current 68/76-check grader.