---
license: apache-2.0
library_name: onnxruntime
pipeline_tag: image-segmentation
base_model:
  - AnnotateIt/edgecrafter-ecseg-s-onnx
  - AnnotateIt/edgecrafter-ecseg-m-onnx
tags:
  - onnx
  - onnxruntime
  - onnxruntime-web
  - instance-segmentation
  - fp16
  - int8
  - quantization
  - webassembly
  - experimental
  - negative-results
---

# ECSeg ONNX optimization experiments

> [!WARNING]
> The ONNX files in this repository are experimental reproducibility artifacts. They are **not recommended
> for production inference or annotation workflows**. AnnotateIt continues to use the original FP32
> ECSeg-S and ECSeg-M checkpoints.

## Result

None of the tested graph-optimized, FP16, dynamic-INT8, or static-INT8 variants improved size, browser
latency, segmentation quality, and runtime stability simultaneously on ONNX Runtime Web 1.24.3 CPU/WASM.

| Model | Decision | Production artifact | FP32 warm p50 |
|---|---|---|---:|
| ECSeg-S | **REJECT optimized variants** | [Original FP32](https://huggingface.co/AnnotateIt/edgecrafter-ecseg-s-onnx) | 1299 ms |
| ECSeg-M | **REJECT optimized variants** | [Original FP32](https://huggingface.co/AnnotateIt/edgecrafter-ecseg-m-onnx) | 2093 ms |

Measurements were made on an Apple M4 Max using single-thread WASM, the configuration used by the
AnnotateIt Desktop and iOS targets. Chrome was the primary engine and WebKit was used as a cross-browser
check. See `results/` for the machine-readable data.

## Tested variants

| Variant | Storage result | Browser/runtime result | Quality result |
|---|---|---|---|
| Graph optimization | 1–2% larger | Warm latency unchanged | Bit-identical |
| FP16 | 49–51% smaller | 4–5% slower in Chrome WASM | Mean mask IoU 0.979–0.988, but individual masks reached IoU 0 |
| Dynamic INT8 | 58–59% smaller | Session creation fails with `ShapeInferenceError` | Not runnable |
| Static INT8 | 68–71% smaller | 12–17% slower | Zero instances at the production threshold |
| Selective static INT8 | 65–68% smaller | 12–15% slower | Zero instances at the production threshold |

The principal incompatibility is the DETR-style deformable-attention segmentation head, including
`GridSample`, `Einsum`, dynamic `TopK`, and `GatherElements`. The tested graph also failed to initialize on
threaded WASM and WebGPU in this runtime version.

## Included experimental artifacts

Only the two tested FP16 variants are retained as reproducibility artifacts. Broken INT8 variants and the
larger graph-optimized variants are deliberately omitted.

| File | Size | SHA-256 | Status |
|---|---:|---|---|
| `artifacts/ecseg-s.fp16.onnx` | 21,565,662 bytes | `508c79e144fa3d9a4691970798529ae03243992aff94b0c28794b4c877c4766c` | Experimental; do not deploy |
| `artifacts/ecseg-m.fp16.onnx` | 41,031,365 bytes | `e0d8865bc52e0c38f14638ece649a175ccd239dfa200274f6de38efa2e10eb90` | Experimental; do not deploy |

Both retain the production I/O contract:

- Input: `images`, float32 `[1,3,640,640]`
- Outputs: `labels` int64 `[1,300]`, `boxes` float32 `[1,300,4]`, `scores` float32 `[1,300]`,
  `masks` float32 `[1,300,160,160]`
- Opset 17, IR version 8

The FP16 conversion keeps public I/O in float32 with boundary casts. The smaller file size does not
translate to faster CPU/WASM execution because this target has no native FP16 compute path for the graph.

## Quality warning

Average agreement hides the failure tail. On the held-out comparison set:

- ECSeg-S FP16: mean mask IoU 0.988, worst-image mean 0.667, minimum individual-instance IoU 0.000.
- ECSeg-M FP16: mean mask IoU 0.979, worst-image mean 0.613, minimum individual-instance IoU 0.000.

For an annotation product, occasional fully incorrect masks are more important than the favorable average.
The artifacts must not be presented as drop-in optimized replacements.

## Repository contents

- `artifacts/`: the exact FP16 binaries used in the reported measurements and `SHA256SUMS`.
- `results/`: CSV and JSON size, latency, and correctness results.
- `images/`: qualitative benchmark comparisons, including the FP16 tail and collapsed static-INT8 output.
- `report/ecseg-quantization-report.md`: full methodology, limitations, tables, and verdicts.
- `scripts/`: reproducible conversion, validation, and browser-benchmark tooling.

The source tooling is also maintained in the
[AnnotateIt repository](https://github.com/yvolokitin/AnnotateIt_npm/tree/main/scripts/model-optimization).

## Reproduction

The exact commands are documented in `scripts/README.md`. In outline:

1. Download the immutable FP32 revisions and verify their pinned SHA-256 values.
2. Create an isolated Python 3.12 environment from `scripts/requirements.txt`.
3. Generate variants with `scripts/optimize.py`.
4. Compare raw outputs and final masks with `scripts/correctness.py`.
5. Benchmark in a real browser with `scripts/bench/bench_browser.mjs` and ONNX Runtime Web 1.24.3.

To verify the published binaries:

```bash
sha256sum -c artifacts/SHA256SUMS
```

On macOS, use `shasum -a 256` against the values in `artifacts/SHA256SUMS`.

## Limitations

- Replacement quality was evaluated as agreement against FP32 on 47 held-out images, not as full COCO AP.
- Static INT8 calibration used a separate 50-image COCO val2017 subset.
- Performance was measured on one Apple M4 Max; Chrome and WebKit were tested.
- Multi-thread WASM session creation hung for every ECSeg variant, including FP32, so latency comparisons use
  the production single-thread Desktop/iOS configuration.
- These results apply to ONNX Runtime Web 1.24.3. A future runtime or a redesigned/exported ECSeg head may
  justify rerunning the study.

## License and provenance

The ECSeg checkpoints are published under Apache-2.0. These files are numerical conversions of the
immutable AnnotateIt ECSeg-S and ECSeg-M ONNX releases; they are not newly trained models. The original
model repositories remain the authoritative production artifacts.