Replace MDGT artifacts with FastVIT cross-sensor checkpoint

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+qai/fastvit_cross_sensor_qai_target_model.dlc filter=lfs diff=lfs merge=lfs -text

README.md

@@ -4,79 +4,99 @@ library_name: pytorch
 pipeline_tag: image-feature-extraction
 tags:
   - fingerprint
-  - matching
-  - mdgt
+  - biometric-matching
+  - fastvit
   - onnx
-  - qai-hub
+  - qualcomm-ai-hub
 ---
 
-# MDGT Matching
+# MDGT Matching - FastVIT Cross-Sensor Checkpoint
 
-This repository contains the MDGT fingerprint matching checkpoint and edge
-export artifacts prepared from:
+This repository currently points to the FastVIT cross-sensor branch, not the older MDGT/NIST300A checkpoint.
 
-`/home/linhdang/workspace2/fingerprint-graph/checkpoints_nist300a/last.pt`
+Primary checkpoint:
 
-## Files
-
-- `pytorch/mdgt_nist300a_last.pt`: PyTorch checkpoint, epoch 53.
-- `onnx/mdgt_pretrained_embedding.onnx`: ONNX embedding model, input
-  `image: float32[1,1,224,224]`, output `embedding: float32[1,256]`.
-- `qai/mdgt_pretrained_qai_target_model.bin`: Qualcomm AI Hub compiled target
-  model for Samsung Galaxy S24 Family, QNN context binary.
-- `qai/qai_summary.json`: QAI compile/profile/smoke inference job summary.
-- `qai/mdgt_pretrained_qai_profile.json`: QAI NPU profile JSON.
-- `results/nist302_spread64_qai_summary.json`: QAI NIST302A/B 1:N matching
-  result on a 64-identity spread subset.
-
-## Export Notes
-
-The ONNX export uses a deterministic TRAM setting for deployability:
-
-- original TRAM: attention 21, uniform 6, random 3
-- export TRAM: attention 24, uniform 6, random 0
-- DINOv2 grid: 16 x 16
-- Grid RPE uses an ONNX-safe cosine/sine formulation instead of `atan2`
+```text
+checkpoints_fastvit_cross_sensor_sa12_round5_consistency_light/best_rank1.pt
+```
 
-Validation against local ONNXRuntime:
+## Model
 
-- ONNXRuntime vs PyTorch max abs diff: `2.72e-7`
-- QAI embeddings vs local ONNX cosine mean: `0.99967`
-- QAI embeddings vs local ONNX cosine min: `0.99916`
+- 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
 
-## Qualcomm AI Hub Jobs
+## Metrics
 
-- Compile: `j5mdzql95`
-- Profile: `jp2vm0zq5`
-- Smoke inference: `jgk9y7kn5`
-- NIST302 spread64 inference: `jp49wk615`
+These metrics are from the checkpoint metadata for the full NIST302 cross-sensor 1:N protocol:
 
-Profile on Samsung Galaxy S24 Family NPU:
+- Probe: NIST302A challengers, 13,630 probes
+- Reference: NIST302B baseline, 8,000 gallery images
+- Identities: 2,000
 
-- mean inference time: `10.35 ms`
-- p50 inference time: `10.33 ms`
-- p95 inference time: `10.64 ms`
-- estimated peak memory: `103,829,504 bytes`
+| Metric | Value |
+|---|---:|
+| Rank-1 | 0.8133528829 |
+| Rank-5 | 0.9174614549 |
+| Rank-10 | 0.9479089975 |
+| mAP | 0.7901349983 |
+| AUC | 0.9983431867 |
+| EER | 0.0178200894 |
 
-## NIST302A/B Spread64 Result
+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.
 
-Protocol:
-
-- Probe: NIST302A challengers, series A-H
-- Gallery: NIST302B baseline, series U/V, 1000/500 DPI
-- 64 identities
-- 256 gallery samples
-- 64 probe samples
-- Preprocess: Gabor + squeeze resize + `[0,1]` tensor
-
-Metrics:
-
-- Rank-1: `0.390625`
-- Rank-5: `0.671875`
-- Rank-10: `0.71875`
-- Mean rank: `12.859375`
-- Median rank: `3.0`
-- MRR: `0.5001383744`
+## Files
 
-This checkpoint is not the strongest FastVIT cross-sensor model from the
-training experiments; it is the MDGT checkpoint prepared for ONNX/QAI testing.
+```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.

artifact_metadata.json

@@ -1,101 +1,76 @@
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 }

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+}

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pytorch/{mdgt_nist300a_last.pt → fastvit_cross_sensor_sa12_round5_consistency_light_best_rank1.pt}

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qai/mdgt_pretrained_qai_profile.json

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qai/onnx_ort_summary.json

@@ -1,101 +0,0 @@
-{
-  "model": {
-    "checkpoint": "/home/linhdang/workspace2/fingerprint-graph/checkpoints_nist300a/last.pt",
-    "epoch": 53,
-    "image_size": 224,
-    "model_kind": "vit_small_patch14_dinov2.lvd142m",
-    "tram_export": {
-      "deterministic": true,
-      "updates": [
-        {
-          "after": {
-            "grid_size": [
-              16,
-              16
-            ],
-            "k_attention": 24,
-            "k_random": 0,
-            "k_uniform": 6,
-            "name": "tram",
-            "num_tokens": 30
-          },
-          "before": {
-            "grid_size": [
-              14,
-              14
-            ],
-            "k_attention": 21,
-            "k_random": 3,
-            "k_uniform": 6,
-            "name": "tram",
-            "num_tokens": 30
-          }
-        }
-      ]
-    },
-    "val_eer": 0.09928751739616241
-  },
-  "onnx": {
-    "onnx_check": "ok",
-    "onnx_nodes": 972,
-    "onnx_path": "runs/qai_mdgt_pretrained_nist300a_20260707/mdgt_pretrained_embedding.onnx",
-    "op_counts": {
-      "Add": 127,
-      "And": 1,
-      "BatchNormalization": 2,
-      "Cast": 6,
-      "Clip": 2,
-      "Concat": 6,
-      "Constant": 302,
-      "ConstantOfShape": 8,
-      "Conv": 1,
-      "Div": 23,
-      "Equal": 9,
-      "Erf": 15,
-      "Expand": 11,
-      "Gather": 1,
-      "GatherElements": 1,
-      "Gemm": 3,
-      "LayerNormalization": 29,
-      "MatMul": 95,
-      "Mod": 1,
-      "Mul": 77,
-      "Not": 2,
-      "Pow": 2,
-      "ReduceL2": 1,
-      "ReduceMax": 13,
-      "ReduceSum": 13,
-      "Relu": 2,
-      "Reshape": 35,
-      "ScatterND": 1,
-      "Shape": 10,
-      "Slice": 26,
-      "Softmax": 15,
-      "Split": 12,
-      "Sqrt": 1,
-      "Squeeze": 36,
-      "Sub": 3,
-      "Tile": 1,
-      "TopK": 3,
-      "Transpose": 50,
-      "Unsqueeze": 15,
-      "Where": 11
-    },
-    "opset": 17
-  },
-  "ort": {
-    "iterations": 3,
-    "latency_ms_mean": 54.28004334680736,
-    "latency_ms_p50": 50.898298039101064,
-    "latency_ms_p95": 75.85197165608406,
-    "max_abs_diff": 2.7194619178771973e-07,
-    "mean_abs_diff": 7.6402443482948e-08,
-    "output_finite": true,
-    "output_shape": [
-      1,
-      256
-    ],
-    "provider": "CPUExecutionProvider",
-    "warmup": 1
-  }
-}

qai/qai_qnn_context_binary_summary.json

@@ -0,0 +1,20 @@
+{
+  "compile_job_id": "jgjwl2v75",
+  "compile_status": "JobStatus\n---------\ncode    : FAILED\nmessage : Conversion to context binary failed with exit code 14\n",
+  "compile_url": "https://workbench.aihub.qualcomm.com/jobs/jgjwl2v75/",
+  "compute_unit": "npu",
+  "device_name": "Samsung Galaxy S24 (Family)",
+  "error": "Model passed in was 'None' (make sure this is not the target of a failed compile job)",
+  "error_type": "UserError",
+  "input_specs": {
+    "image": [
+      1,
+      1,
+      224,
+      224
+    ]
+  },
+  "source_onnx": "runs/fastvit_cross_sensor_sa12_round5_consistency_light_export_20260707/fastvit_round4_embedding_fp32.onnx",
+  "status": "failed",
+  "target_runtime": "qnn_context_binary"
+}

qai/qai_qnn_dlc_summary.json

@@ -0,0 +1,26 @@
+{
+  "compile_job_id": "jgdzvqxk5",
+  "compile_status": "JobStatus\n---------\ncode    : SUCCESS\nmessage : \n",
+  "compile_url": "https://workbench.aihub.qualcomm.com/jobs/jgdzvqxk5/",
+  "compute_unit": "npu",
+  "device_name": "Samsung Galaxy S24 (Family)",
+  "inference_job_id": "jgz47j2zp",
+  "inference_status": "JobStatus\n---------\ncode    : FAILED\nmessage : Failed to profile the model: Failed to call QnnModel_composeGraphsFromDlc: MODEL_GRAPH_ERROR.\n",
+  "inference_url": "https://workbench.aihub.qualcomm.com/jobs/jgz47j2zp/",
+  "input_specs": {
+    "image": [
+      1,
+      1,
+      224,
+      224
+    ]
+  },
+  "profile_job_id": "jp847m2z5",
+  "profile_path": "{}",
+  "profile_status": "JobStatus\n---------\ncode    : FAILED\nmessage : Failed to profile the model: Failed to call QnnModel_composeGraphsFromDlc: MODEL_GRAPH_ERROR.\n",
+  "profile_url": "https://workbench.aihub.qualcomm.com/jobs/jp847m2z5/",
+  "source_onnx": "runs/fastvit_cross_sensor_sa12_round5_consistency_light_export_20260707/fastvit_round4_embedding_fp32.onnx",
+  "status": "completed",
+  "target_model_path": "runs/qai_fastvit_cross_sensor_sa12_round5_qnndlc_20260707/qai_artifacts/fastvit_cross_sensor_qai_target_model.dlc",
+  "target_runtime": "qnn_dlc"
+}

qai/qai_qnn_lib_attempt_summary.json

@@ -0,0 +1,17 @@
+{
+  "compute_unit": "npu",
+  "device_name": "Samsung Galaxy S24 (Family)",
+  "error": "argument --target_runtime: Unsupported target runtime: qnn_lib_aarch64_android. Expecting one of 'onnx', 'precompiled_qnn_onnx', 'qnn_context_binary', 'qnn_dlc', 'tflite'",
+  "error_type": "UserError",
+  "input_specs": {
+    "image": [
+      1,
+      1,
+      224,
+      224
+    ]
+  },
+  "source_onnx": "runs/fastvit_cross_sensor_sa12_round5_consistency_light_export_20260707/fastvit_round4_embedding_fp32.onnx",
+  "status": "failed",
+  "target_runtime": "qnn_lib_aarch64_android"
+}

qai/qai_summary.json

@@ -1,101 +0,0 @@
-{
-  "model": {
-    "checkpoint": "/home/linhdang/workspace2/fingerprint-graph/checkpoints_nist300a/last.pt",
-    "epoch": 53,
-    "image_size": 224,
-    "model_kind": "vit_small_patch14_dinov2.lvd142m",
-    "tram_export": {
-      "deterministic": true,
-      "updates": [
-        {
-          "after": {
-            "grid_size": [
-              16,
-              16
-            ],
-            "k_attention": 24,
-            "k_random": 0,
-            "k_uniform": 6,
-            "name": "tram",
-            "num_tokens": 30
-          },
-          "before": {
-            "grid_size": [
-              14,
-              14
-            ],
-            "k_attention": 21,
-            "k_random": 3,
-            "k_uniform": 6,
-            "name": "tram",
-            "num_tokens": 30
-          }
-        }
-      ]
-    },
-    "val_eer": 0.09928751739616241
-  },
-  "onnx": {
-    "onnx_check": "ok",
-    "onnx_nodes": 972,
-    "onnx_path": "runs/qai_mdgt_pretrained_nist300a_20260707/mdgt_pretrained_embedding.onnx",
-    "op_counts": {
-      "Add": 127,
-      "And": 1,
-      "BatchNormalization": 2,
-      "Cast": 6,
-      "Clip": 2,
-      "Concat": 6,
-      "Constant": 302,
-      "ConstantOfShape": 8,
-      "Conv": 1,
-      "Div": 23,
-      "Equal": 9,
-      "Erf": 15,
-      "Expand": 11,
-      "Gather": 1,
-      "GatherElements": 1,
-      "Gemm": 3,
-      "LayerNormalization": 29,
-      "MatMul": 95,
-      "Mod": 1,
-      "Mul": 77,
-      "Not": 2,
-      "Pow": 2,
-      "ReduceL2": 1,
-      "ReduceMax": 13,
-      "ReduceSum": 13,
-      "Relu": 2,
-      "Reshape": 35,
-      "ScatterND": 1,
-      "Shape": 10,
-      "Slice": 26,
-      "Softmax": 15,
-      "Split": 12,
-      "Sqrt": 1,
-      "Squeeze": 36,
-      "Sub": 3,
-      "Tile": 1,
-      "TopK": 3,
-      "Transpose": 50,
-      "Unsqueeze": 15,
-      "Where": 11
-    },
-    "opset": 17
-  },
-  "qai": {
-    "compile_job_id": "j5mdzql95",
-    "compile_status": "JobStatus\n---------\ncode    : SUCCESS\nmessage : \n",
-    "compile_url": "https://workbench.aihub.qualcomm.com/jobs/j5mdzql95/",
-    "inference_job_id": "jgk9y7kn5",
-    "inference_output_path": "runs/qai_mdgt_pretrained_nist300a_20260707/qai_artifacts/mdgt_pretrained_qai_inference_output.h5",
-    "inference_status": "JobStatus\n---------\ncode    : SUCCESS\nmessage : \n",
-    "inference_url": "https://workbench.aihub.qualcomm.com/jobs/jgk9y7kn5/",
-    "profile_job_id": "jp2vm0zq5",
-    "profile_path": "runs/qai_mdgt_pretrained_nist300a_20260707/qai_artifacts/mdgt_pretrained_qai_profile.json",
-    "profile_status": "JobStatus\n---------\ncode    : SUCCESS\nmessage : \n",
-    "profile_url": "https://workbench.aihub.qualcomm.com/jobs/jp2vm0zq5/",
-    "status": "completed",
-    "target_model_path": "runs/qai_mdgt_pretrained_nist300a_20260707/qai_artifacts/mdgt_pretrained_qai_target_model.bin"
-  }
-}

results/checkpoint_metrics.json

@@ -0,0 +1,80 @@
+{
+  "best_rank1": 0.8133528828620911,
+  "config_summary": {
+    "augment_profile": "light",
+    "clahe": true,
+    "consistency_weight": 0.05,
+    "fastvit_model": "fastvit_sa12.apple_in1k",
+    "feature_index": 1,
+    "gabor": false,
+    "gnn_dim": 256,
+    "gnn_heads": 4,
+    "gnn_k": 8,
+    "gnn_layers": 2,
+    "hard_negative_ratio": 0.5,
+    "image_size": 224,
+    "model_type": "fastvit_graph",
+    "output_dim": 256,
+    "resize_mode": "squeeze",
+    "supcon_weight": 0.15,
+    "token_k": 64,
+    "token_selection": "tram",
+    "token_tram_temperature": 0.2,
+    "token_tram_weight": 0.5
+  },
+  "epoch": 1,
+  "metrics": {
+    "auc": 0.9983431867002304,
+    "eer": 0.017820089428426635,
+    "gallery_count": 8000,
+    "identity_count": 2000,
+    "mAP": 0.790134998310548,
+    "probe_count": 13630,
+    "protocol_summary": {
+      "common_identity_count": 2000,
+      "gallery": {
+        "device_counts": {
+          "nist302b_U_1000_roll": 2000,
+          "nist302b_U_500_roll": 2000,
+          "nist302b_V_1000_roll": 2000,
+          "nist302b_V_500_roll": 2000
+        },
+        "domain_counts": {
+          "nist302b": 8000
+        },
+        "identity_count": 2000,
+        "role_counts": {
+          "reference": 8000
+        },
+        "sample_count": 8000
+      },
+      "missing_gallery_identity_count": 0,
+      "missing_probe_identity_count": 0,
+      "probe": {
+        "device_counts": {
+          "nist302a_A_roll": 1960,
+          "nist302a_B_roll": 1956,
+          "nist302a_C_roll": 1998,
+          "nist302a_D_roll": 1987,
+          "nist302a_E_roll": 1887,
+          "nist302a_F_roll": 2000,
+          "nist302a_G_roll": 700,
+          "nist302a_H_roll": 1142
+        },
+        "domain_counts": {
+          "nist302a": 13630
+        },
+        "identity_count": 2000,
+        "role_counts": {
+          "probe": 13630
+        },
+        "sample_count": 13630
+      }
+    },
+    "rank1": 0.8133528828620911,
+    "rank10": 0.9479089975357056,
+    "rank5": 0.9174614548683167,
+    "status": "ok"
+  },
+  "source_checkpoint": "checkpoints_fastvit_cross_sensor_sa12_round5_consistency_light/best_rank1.pt"
+}

results/nist302_spread64_matches.csv

@@ -1,65 +0,0 @@
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training/history.jsonl

@@ -0,0 +1,4 @@
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