---
license: other
library_name: pytorch
pipeline_tag: image-feature-extraction
tags:
  - fingerprint
  - biometric-matching
  - fastvit
  - onnx
  - qualcomm-ai-hub
---

# MDGT Matching - FastVIT Cross-Sensor Checkpoint

This repository currently points to the FastVIT cross-sensor branch, not the older MDGT/NIST300A checkpoint.

Primary checkpoint:

```text
checkpoints_fastvit_cross_sensor_sa12_round5_consistency_light/best_rank1.pt
```

## Model

- Architecture: `FastVITGraph`
- Backbone: `fastvit_sa12.apple_in1k`
- Feature index: `1`
- Token selection: TRAM
- Token count: `64`
- GNN: 2 layers, dim 256, 4 heads, k=8
- Embedding dim: `256`
- Input: preprocessed grayscale tensor `(B, 1, 224, 224)`
- Preprocess used for export/eval: squeeze resize, CLAHE on, Gabor off

## Metrics

These metrics are from the checkpoint metadata for the full NIST302 cross-sensor 1:N protocol:

- Probe: NIST302A challengers, 13,630 probes
- Reference: NIST302B baseline, 8,000 gallery images
- Identities: 2,000

| Metric | Value |
|---|---:|
| Rank-1 | 0.8133528829 |
| Rank-5 | 0.9174614549 |
| Rank-10 | 0.9479089975 |
| mAP | 0.7901349983 |
| AUC | 0.9983431867 |
| EER | 0.0178200894 |

The earlier low `Rank-1: 0.390625` result belonged to an MDGT/NIST300A QAI smoke benchmark on a 64-identity spread subset. It is not the metric for this FastVIT checkpoint.

## Files

```text
pytorch/fastvit_cross_sensor_sa12_round5_consistency_light_best_rank1.pt
onnx/fastvit_cross_sensor_embedding_fp32.onnx
onnx/fastvit_cross_sensor_embedding_fp16.onnx
onnx/fastvit_cross_sensor_embedding_int8_linear_qdq.onnx
onnx/export_summary.json
qai/fastvit_cross_sensor_qai_target_model.dlc
qai/qai_qnn_context_binary_summary.json
qai/qai_qnn_dlc_summary.json
qai/qai_qnn_lib_attempt_summary.json
results/checkpoint_metrics.json
training/history.jsonl
artifact_metadata.json
```

## ONNX Export

The ONNX export uses an export-safe FastVITGraph wrapper with vectorized deterministic token deduplication.

Observed local drift on 16 samples:

| Comparison | Cos mean | Cos min |
|---|---:|---:|
| Original PyTorch vs exportable PyTorch | 1.0000000000 | 0.9999999404 |
| Exportable PyTorch vs ONNX FP32 | 0.9999986887 | 0.9999958277 |
| ONNX FP16 vs ONNX FP32 | 0.9990564585 | 0.9972373843 |
| ONNX INT8 linear QDQ vs ONNX FP32 | 0.9941318631 | 0.9882222414 |

## Qualcomm AI Hub

QAI test was rerun on the FastVIT ONNX, not on MDGT.

- `qnn_context_binary`: compile failed with exit code 14.
- `qnn_lib_aarch64_android`: unsupported by the installed QAI Hub client.
- `qnn_dlc`: compile succeeded, target DLC was produced.
- NPU profile/inference for the DLC failed with `MODEL_GRAPH_ERROR` from `QnnModel_composeGraphsFromDlc`.

Jobs:

```text
qnn_context_binary compile: https://workbench.aihub.qualcomm.com/jobs/jgjwl2v75/
qnn_dlc compile:           https://workbench.aihub.qualcomm.com/jobs/jgdzvqxk5/
qnn_dlc profile:           https://workbench.aihub.qualcomm.com/jobs/jp847m2z5/
qnn_dlc inference:         https://workbench.aihub.qualcomm.com/jobs/jgz47j2zp/
```

This means the current full FastVITGraph ONNX can be exported and compiled to DLC, but the graph is not yet deployable as a working NPU profile/inference artifact on QAI Hub. The likely next step is to simplify or split the dynamic token/GNN section for NPU compatibility.