Publish selected model-only bundle for Omatrack 1.8.3

NOTICE.md

@@ -0,0 +1,26 @@
+# Omatrack bundled telemetry models — release 1.8.3
+
+The model owner authorized distribution of these selected weights with the
+Omatrack patch release. The original model and contract bytes are unchanged.
+No private footage, screenshots, telemetry, labels, datasets, checkpoint files,
+or real-image parity fixtures are included.
+
+## License scope
+
+A separate license for the new task-specific weights has not been assigned.
+Do not infer that Omatrack's source-code MIT license licenses these weights.
+The following upstream notices are retained for their respective components;
+neither is presented as a blanket license grant for the new task model:
+
+- Reader backbone: timm MobileNetV3-small, upstream Apache-2.0. See
+  `licenses/reader/NOTICE` and its accompanying upstream license. The original
+  example-code MIT notice is preserved alongside it; examples are available from
+  the original reader repository rather than copied into this bundle.
+- Detector backbones: torchvision MobileNetV3-small and ConvNeXt-Tiny, upstream
+  BSD-3-Clause. See `licenses/detectors/NOTICE` and accompanying license.
+
+Original export metadata/NOTICE files are retained byte-for-byte. Any statement
+there that no public upload had occurred describes the training/export stage;
+this repository is the subsequent owner-authorized model distribution.
+No rights to source footage or datasets are granted. Names identify provenance
+and compatibility, not endorsement. Predictions are not authoritative telemetry.

README.md

@@ -0,0 +1,80 @@
+---
+library_name: onnx
+tags:
+- onnx
+- computer-vision
+- object-detection
+- racing
+- telemetry
+---
+# Omatrack telemetry model bundle
+
+Selected local-inference models for the Omatrack 1.8.3 patch release. Release
+builds download an immutable revision and verify every file hash; the resulting
+applications include the weights for offline operation. No video is uploaded.
+This is a custom ONNX contract, not a drop-in Transformers pipeline.
+
+| File | Purpose | Size |
+|---|---|---:|
+| `gauge-reader.onnx` | Proven four-crop value reader | 2,213,746 bytes |
+| `detectors/tiny-v2/gauge-detector.onnx` | Lightweight general proposal discovery | 966,419 bytes |
+| `detectors/aim-large-v1/gauge-detector.onnx` | Higher-accuracy known-AiM proposal discovery | 112,025,875 bytes |
+
+The reader is byte-identical to the existing
+[tobil/omatrack-telemetry-reader v1.0.0](https://huggingface.co/tobil/omatrack-telemetry-reader/tree/v1.0.0)
+artifact. The original repository and revision are unchanged.
+
+## Intended routing and safeguards
+
+Use the larger detector only after the current full source image independently
+matches the reviewed 1920x1080 orange AiM layout. Use the lightweight detector for
+other layouts and as a failure fallback. Do not infer layout from file extension
+or filenames. Source dimensions/display transforms must be verified before boxes
+are projected onto playback.
+
+All learned detections are proposals, not calibrated probabilities or reader
+approval. The reader outputs gear, the displayed stint counter, brake visible-fill
+percent and throttle visible-fill percent only for its reviewed crops. It does not
+measure physical brake pressure, GPS, lap classification, arbitrary fonts or every
+other detected widget. Unsupported crops stay unknown. Native telemetry takes
+priority. User confirmation precedes extraction.
+
+The detector tensor/preprocessing/postprocessing contract is documented in each
+`contract.json`: RGB float32 NCHW [1,3,384,640], full-source half-pixel bilinear
+resize without antialiasing, then five stride-four prediction heads. Read each
+contract in full; class ordering, sigmoid/offset math, thresholds and NMS matter.
+
+## Evidence and limitations
+
+These are known-style, session-held-out development checks, not an unseen-layout
+or broad no-HUD certification. Private evaluation images are not included.
+
+- Original eight full frames: lightweight precision77.5% / recall69.4%; larger
+  precision73.5% / recall82.6%. The larger model is NOT a global replacement.
+- On the original VBOX subset, larger precision fell to58.2% from75.3%.
+- Additional eight exhaustively reviewed AiM frames: larger precision97.8% /
+  recall99.3%, versus lightweight83.3% /66.2%.
+- Additional eight partial VBOX frames: larger annotated-target recall85.5%;
+  precision is unscored. Estimated pedal-track extents were withheld from exact
+  IoU scoring, not treated as absent.
+- Larger model produced two false proposals on16 additional reviewed no-gauge
+  crops. Such crops are not full no-HUD recordings.
+- ARM-server CPU measurements: the larger model costs about388ms inference /
+  410ms including file decode, preprocessing and box decode with two ORT threads.
+  One-thread native processing is around0.8s. Discovery runs only every few
+  seconds on a worker; this is not a claim of5Hz whole-frame detection.
+
+The backbone, annotation coverage and training recipe changed together; the
+comparison is not an isolated model-capacity experiment. The more heavily
+framing-randomized value-reader candidate failed clean/extreme-framing gates and
+is deliberately NOT included.
+
+## Privacy and rights
+
+Only selected ONNX models, their companion contracts/metadata, checksums and scoped
+upstream notices are published. No private frames, telemetry, labels, training
+checkpoints, outputs or real-image fixtures are redistributed. A separate license
+for the task-specific weights remains unspecified; see [NOTICE.md](NOTICE.md).
+Do not infer a blanket MIT/Apache/BSD grant for the task weights or source footage.
+
+Application code and runtime documentation: [tobi/omatrack](https://github.com/tobi/omatrack).

detectors/aim-large-v1/contract.json

@@ -0,0 +1,19 @@
+{
+  "schema": "gauge-detector-center-v1",
+  "training_scope_version": "convnext-tiny-large-v1-strong28-partial-mil",
+  "architecture": "full torchvision ConvNeXt-Tiny backbone plus 64-channel four-scale FPN",
+  "status": "experimental_discovery_not_reader_approval",
+  "input": {"name":"image","dtype":"float32","layout":"NCHW","shape":[1,3,384,640],"channels":"RGB","range":[0,1],"spatial_multiple":32,"dynamic_spatial":true,"resize":"stretch_full_source_half_pixel_bilinear_no_antialias","pixel_formula":"sx=(dx+0.5)*source_width/dest_width-0.5; sy analogous; floor coordinates, clamp each neighbor index; interpolate raw uint8 RGB in float32 then divide by 255; no intermediate uint8 rounding","normalization":"divide_255_only; ImageNet normalization is inside the graph"},
+  "stride":4,
+  "outputs":[
+    {"name":"center_logits","channels":1},
+    {"name":"size_logits","channels":2,"order":["width","height"]},
+    {"name":"offset_logits","channels":2,"order":["x","y"]},
+    {"name":"representation_logits","channels":4,"labels":["digits","bar","wheel","needle"]},
+    {"name":"semantic_logits","channels":5,"labels":["unknown","gear","stint_lap","brake","throttle"]}
+  ],
+  "output_layout":"NCHW; all outputs [1,C,input_height/4,input_width/4]; finite float32 logits",
+  "decode":{"score_threshold":0.35,"local_max_kernel":3,"local_max_rule":"sigmoid(center) >= every in-bounds 3x3 neighbor; equal maxima all retained before top-k","candidate_limit":100,"sort":"descending score then ascending flat y*grid_width+x index","center":"((x+sigmoid(offset_x))/grid_width,(y+sigmoid(offset_y))/grid_height)","size":"(sigmoid(width_logit),sigmoid(height_logit)) normalized full-source width/height","box":"center +/- size/2; clip each coordinate to [0,1]; discard zero-area","nms":"class-agnostic greedy suppress IoU strictly > 0.4","nms_iou":0.4,"max_detections":32,"representation":"argmax; lowest index wins ties","semantic":"softmax argmax; lowest index wins ties; emit unknown if max probability < 0.8","semantic_threshold":0.8},
+  "runtime_guards":["require RGB source dimensions > 0","require batch=1,3 input channels, H/W divisible by32","reject missing, extra, wrong-shaped, nonfloat32 or nonfinite outputs","model and metadata SHA256 must match pinned setup identity"],
+  "limitations":["scores are not calibrated probabilities","unknown semantic positives only for explicitly observed non-Tier1 fields; ambiguous fields and generic lap-counter ownership masked; no open-set certification","all synthetic semantic loss fully masked; no renderer-intent labels","28 reviewed TRAIN parents include 24 exhaustive overlays and 4 partial parents; ALL partial geometry is MIL-window-only, with zero exact-center/size/offset and zero negative losses because dimbar endpoints were uncertain","weak real windows use positive MIL only; layered ignore regions do not erase documented foreground positives","bar includes segmented indicators, no fill direction or scale predicted","visibility/readability/support remain unknown","detector boxes never imply arbitrary crop reader support","confirmed user/reviewed configuration required before extraction","two known real layouts and four reused background assets; repeated views are not new source diversity; larger model CPU/laptop latency must be measured"]
+}

detectors/aim-large-v1/gauge-detector.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:92bbc4a61395c3fea309986986cbd41ef78d1ab735ecaa755a7e57c0c1fcb8c1
+size 112025875

detectors/aim-large-v1/metadata.json

@@ -0,0 +1,141 @@
+{
+  "schema": "gauge-detector-center-v1",
+  "model_file": "gauge-detector.onnx",
+  "model_sha256": "92bbc4a61395c3fea309986986cbd41ef78d1ab735ecaa755a7e57c0c1fcb8c1",
+  "model_bytes": 112025875,
+  "contract_file": "contract.json",
+  "contract_sha256": "bfd0df6a83f714e00a5b92e441bf7c45ae777899a6976eee2353f728c7080645",
+  "checkpoint_sha256": "f957a4d61b34f9af43e3b6c4db0ce3b493181a692beffd30e233d4893c3d27a9",
+  "selected_epoch": 9,
+  "frozen_config_sha256": "a25c8906cf674498d1ce43788f0b2ffdf086052e33c988896f1ea16ec4bd1490",
+  "parameters": 27985774,
+  "onnx_opset": 17,
+  "operators": [
+    "Add",
+    "Cast",
+    "Concat",
+    "Constant",
+    "Conv",
+    "Div",
+    "Erf",
+    "Gather",
+    "LayerNormalization",
+    "MatMul",
+    "Mul",
+    "Relu",
+    "Resize",
+    "Shape",
+    "Slice",
+    "Sub",
+    "Transpose",
+    "Unsqueeze"
+  ],
+  "onnx_version": "1.19.1",
+  "ort_version": "1.24.2",
+  "status": "experimental_candidate_not_independently_accepted",
+  "input": {
+    "name": "image",
+    "dtype": "float32",
+    "layout": "NCHW",
+    "shape": [
+      1,
+      3,
+      384,
+      640
+    ],
+    "channels": "RGB",
+    "range": [
+      0,
+      1
+    ],
+    "spatial_multiple": 32,
+    "dynamic_spatial": true,
+    "resize": "stretch_full_source_half_pixel_bilinear_no_antialias",
+    "pixel_formula": "sx=(dx+0.5)*source_width/dest_width-0.5; sy analogous; floor coordinates, clamp each neighbor index; interpolate raw uint8 RGB in float32 then divide by 255; no intermediate uint8 rounding",
+    "normalization": "divide_255_only; ImageNet normalization is inside the graph"
+  },
+  "outputs": [
+    {
+      "name": "center_logits",
+      "channels": 1
+    },
+    {
+      "name": "size_logits",
+      "channels": 2,
+      "order": [
+        "width",
+        "height"
+      ]
+    },
+    {
+      "name": "offset_logits",
+      "channels": 2,
+      "order": [
+        "x",
+        "y"
+      ]
+    },
+    {
+      "name": "representation_logits",
+      "channels": 4,
+      "labels": [
+        "digits",
+        "bar",
+        "wheel",
+        "needle"
+      ]
+    },
+    {
+      "name": "semantic_logits",
+      "channels": 5,
+      "labels": [
+        "unknown",
+        "gear",
+        "stint_lap",
+        "brake",
+        "throttle"
+      ]
+    }
+  ],
+  "decode": {
+    "score_threshold": 0.35,
+    "local_max_kernel": 3,
+    "local_max_rule": "sigmoid(center) >= every in-bounds 3x3 neighbor; equal maxima all retained before top-k",
+    "candidate_limit": 100,
+    "sort": "descending score then ascending flat y*grid_width+x index",
+    "center": "((x+sigmoid(offset_x))/grid_width,(y+sigmoid(offset_y))/grid_height)",
+    "size": "(sigmoid(width_logit),sigmoid(height_logit)) normalized full-source width/height",
+    "box": "center +/- size/2; clip each coordinate to [0,1]; discard zero-area",
+    "nms": "class-agnostic greedy suppress IoU strictly > 0.4",
+    "nms_iou": 0.4,
+    "max_detections": 32,
+    "representation": "argmax; lowest index wins ties",
+    "semantic": "softmax argmax; lowest index wins ties; emit unknown if max probability < 0.8",
+    "semantic_threshold": 0.8
+  },
+  "limitations": [
+    "scores are not calibrated probabilities",
+    "unknown semantic positives only for explicitly observed non-Tier1 fields; ambiguous fields and generic lap-counter ownership masked; no open-set certification",
+    "all synthetic semantic loss fully masked; no renderer-intent labels",
+    "28 reviewed TRAIN parents include 24 exhaustive overlays and 4 partial parents; ALL partial geometry is MIL-window-only, with zero exact-center/size/offset and zero negative losses because dimbar endpoints were uncertain",
+    "weak real windows use positive MIL only; layered ignore regions do not erase documented foreground positives",
+    "bar includes segmented indicators, no fill direction or scale predicted",
+    "visibility/readability/support remain unknown",
+    "detector boxes never imply arbitrary crop reader support",
+    "confirmed user/reviewed configuration required before extraction",
+    "two known real layouts and four reused background assets; repeated views are not new source diversity; larger model CPU/laptop latency must be measured"
+  ],
+  "licensing": "torchvision BSD-3-Clause backbone; task weights redistribution license unspecified; no public upload",
+  "required_metadata_keys": [
+    "schema",
+    "model_file",
+    "model_sha256",
+    "contract_file",
+    "contract_sha256",
+    "status",
+    "input",
+    "outputs",
+    "decode",
+    "limitations"
+  ]
+}

detectors/tiny-v2/contract.json

@@ -0,0 +1,19 @@
+{
+  "schema": "gauge-detector-center-v1",
+  "training_scope_version": "pilot-v2-mil-strong4-backgrounds",
+  "tensor_decode_abi": "unchanged from gauge-detector-center-v1",
+  "status": "experimental_discovery_not_reader_approval",
+  "input": {"name":"image","dtype":"float32","layout":"NCHW","shape":[1,3,384,640],"channels":"RGB","range":[0,1],"spatial_multiple":32,"dynamic_spatial":true,"resize":"stretch_full_source_half_pixel_bilinear_no_antialias","pixel_formula":"sx=(dx+0.5)*source_width/dest_width-0.5; sy analogous; floor coordinates, clamp each neighbor index; interpolate raw uint8 RGB in float32 then divide by 255; no intermediate uint8 rounding","normalization":"divide_255_only; ImageNet normalization is inside the graph"},
+  "stride":4,
+  "outputs":[
+    {"name":"center_logits","channels":1},
+    {"name":"size_logits","channels":2,"order":["width","height"]},
+    {"name":"offset_logits","channels":2,"order":["x","y"]},
+    {"name":"representation_logits","channels":4,"labels":["digits","bar","wheel","needle"]},
+    {"name":"semantic_logits","channels":5,"labels":["unknown","gear","stint_lap","brake","throttle"]}
+  ],
+  "output_layout":"NCHW; all outputs [1,C,input_height/4,input_width/4]; finite float32 logits",
+  "decode":{"score_threshold":0.35,"local_max_kernel":3,"local_max_rule":"sigmoid(center) >= every in-bounds 3x3 neighbor; equal maxima all retained before top-k","candidate_limit":100,"sort":"descending score then ascending flat y*grid_width+x index","center":"((x+sigmoid(offset_x))/grid_width,(y+sigmoid(offset_y))/grid_height)","size":"(sigmoid(width_logit),sigmoid(height_logit)) normalized full-source width/height","box":"center +/- size/2; clip each coordinate to [0,1]; discard zero-area","nms":"class-agnostic greedy suppress IoU strictly > 0.4","nms_iou":0.4,"max_detections":32,"representation":"argmax; lowest index wins ties","semantic":"softmax argmax; lowest index wins ties; emit unknown if max probability < 0.8","semantic_threshold":0.8},
+  "runtime_guards":["require RGB source dimensions > 0","require batch=1,3 input channels, H/W divisible by32","reject missing, extra, wrong-shaped, nonfloat32 or nonfinite outputs","model and metadata SHA256 must match pinned setup identity"],
+  "limitations":["scores are not calibrated probabilities","unknown semantic has positive supervision only for explicitly observed non-Tier1 fields in four TRAIN images; ambiguous fields and generic lap-counter ownership remain masked; no open-set certification","all synthetic semantic loss fully masked; no renderer-intent labels","real weak windows use positive MIL only, no extent or offset loss or background negatives; four pixel-reviewed TRAIN images provide stronger geometry with explicit ignored regions","bar includes segmented indicators, no fill direction or scale predicted","visibility/readability/support remain unknown","detector boxes never imply arbitrary crop reader support","confirmed user/reviewed configuration required before extraction","only two known real layouts and four reviewed TRAIN background assets; repeated views are not new source diversity"]
+}

detectors/tiny-v2/gauge-detector.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:99bdd483402444e8c799452d9885c1184980a41f2cd80d4af4d7a6b3ae00abc6
+size 966419

detectors/tiny-v2/metadata.json

@@ -0,0 +1,139 @@
+{
+  "schema": "gauge-detector-center-v1",
+  "model_file": "gauge-detector.onnx",
+  "model_sha256": "99bdd483402444e8c799452d9885c1184980a41f2cd80d4af4d7a6b3ae00abc6",
+  "model_bytes": 966419,
+  "contract_file": "contract.json",
+  "contract_sha256": "87a4cec9e0466250b635e561c74be81f2ce5f15fbd79804a1be639c3719a69d3",
+  "checkpoint_sha256": "42fca6a5cad028bd21c7a05195c93c02597f8ffe2c5280875a5f1254029c0f83",
+  "selected_epoch": 7,
+  "frozen_config_sha256": "a58dcdd47804b38eb2dfeccce12f43f570628bf82117ccdf62975b16a0244280",
+  "parameters": 237142,
+  "onnx_opset": 17,
+  "operators": [
+    "Add",
+    "Cast",
+    "Concat",
+    "Constant",
+    "Conv",
+    "Div",
+    "Gather",
+    "GlobalAveragePool",
+    "HardSigmoid",
+    "HardSwish",
+    "Mul",
+    "Relu",
+    "Resize",
+    "Shape",
+    "Slice",
+    "Sub",
+    "Unsqueeze"
+  ],
+  "onnx_version": "1.19.1",
+  "ort_version": "1.24.2",
+  "status": "experimental_candidate_not_independently_accepted",
+  "input": {
+    "name": "image",
+    "dtype": "float32",
+    "layout": "NCHW",
+    "shape": [
+      1,
+      3,
+      384,
+      640
+    ],
+    "channels": "RGB",
+    "range": [
+      0,
+      1
+    ],
+    "spatial_multiple": 32,
+    "dynamic_spatial": true,
+    "resize": "stretch_full_source_half_pixel_bilinear_no_antialias",
+    "pixel_formula": "sx=(dx+0.5)*source_width/dest_width-0.5; sy analogous; floor coordinates, clamp each neighbor index; interpolate raw uint8 RGB in float32 then divide by 255; no intermediate uint8 rounding",
+    "normalization": "divide_255_only; ImageNet normalization is inside the graph"
+  },
+  "outputs": [
+    {
+      "name": "center_logits",
+      "channels": 1
+    },
+    {
+      "name": "size_logits",
+      "channels": 2,
+      "order": [
+        "width",
+        "height"
+      ]
+    },
+    {
+      "name": "offset_logits",
+      "channels": 2,
+      "order": [
+        "x",
+        "y"
+      ]
+    },
+    {
+      "name": "representation_logits",
+      "channels": 4,
+      "labels": [
+        "digits",
+        "bar",
+        "wheel",
+        "needle"
+      ]
+    },
+    {
+      "name": "semantic_logits",
+      "channels": 5,
+      "labels": [
+        "unknown",
+        "gear",
+        "stint_lap",
+        "brake",
+        "throttle"
+      ]
+    }
+  ],
+  "decode": {
+    "score_threshold": 0.35,
+    "local_max_kernel": 3,
+    "local_max_rule": "sigmoid(center) >= every in-bounds 3x3 neighbor; equal maxima all retained before top-k",
+    "candidate_limit": 100,
+    "sort": "descending score then ascending flat y*grid_width+x index",
+    "center": "((x+sigmoid(offset_x))/grid_width,(y+sigmoid(offset_y))/grid_height)",
+    "size": "(sigmoid(width_logit),sigmoid(height_logit)) normalized full-source width/height",
+    "box": "center +/- size/2; clip each coordinate to [0,1]; discard zero-area",
+    "nms": "class-agnostic greedy suppress IoU strictly > 0.4",
+    "nms_iou": 0.4,
+    "max_detections": 32,
+    "representation": "argmax; lowest index wins ties",
+    "semantic": "softmax argmax; lowest index wins ties; emit unknown if max probability < 0.8",
+    "semantic_threshold": 0.8
+  },
+  "limitations": [
+    "scores are not calibrated probabilities",
+    "unknown semantic has positive supervision only for explicitly observed non-Tier1 fields in four TRAIN images; ambiguous fields and generic lap-counter ownership remain masked; no open-set certification",
+    "all synthetic semantic loss fully masked; no renderer-intent labels",
+    "real weak windows use positive MIL only, no extent or offset loss or background negatives; four pixel-reviewed TRAIN images provide stronger geometry with explicit ignored regions",
+    "bar includes segmented indicators, no fill direction or scale predicted",
+    "visibility/readability/support remain unknown",
+    "detector boxes never imply arbitrary crop reader support",
+    "confirmed user/reviewed configuration required before extraction",
+    "only two known real layouts and four reviewed TRAIN background assets; repeated views are not new source diversity"
+  ],
+  "licensing": "torchvision BSD-3-Clause backbone; task weights redistribution license unspecified; no public upload",
+  "required_metadata_keys": [
+    "schema",
+    "model_file",
+    "model_sha256",
+    "contract_file",
+    "contract_sha256",
+    "status",
+    "input",
+    "outputs",
+    "decode",
+    "limitations"
+  ]
+}

gauge-reader.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:97029f70068f4ec276b3d6bc28810763275806f579d91ddd4701b544af392147
+size 2213746

inventory.json

@@ -0,0 +1,79 @@
+{
+  "schema": "omatrack-model-bundle-inventory-v1",
+  "intended_release": "1.8.3",
+  "repository": "tobil/omatrack-telemetry-models",
+  "files": [
+    {
+      "path": "NOTICE.md",
+      "bytes": 1495,
+      "sha256": "4485d59783551e3cd30c25a7b80a5acc0ae5490364a17dbf69ae8954e6529d54"
+    },
+    {
+      "path": "README.md",
+      "bytes": 4117,
+      "sha256": "480168ada8b3e17514702db0d906aae1e114c214a6303e03fad4a9d38651b366"
+    },
+    {
+      "path": "detectors/aim-large-v1/contract.json",
+      "bytes": 3308,
+      "sha256": "bfd0df6a83f714e00a5b92e441bf7c45ae777899a6976eee2353f728c7080645"
+    },
+    {
+      "path": "detectors/aim-large-v1/gauge-detector.onnx",
+      "bytes": 112025875,
+      "sha256": "92bbc4a61395c3fea309986986cbd41ef78d1ab735ecaa755a7e57c0c1fcb8c1"
+    },
+    {
+      "path": "detectors/aim-large-v1/metadata.json",
+      "bytes": 4519,
+      "sha256": "92b755bbb91b7991f564369c57b9545207bfca8f3f083d1b68d30c6abd2d99d4"
+    },
+    {
+      "path": "detectors/tiny-v2/contract.json",
+      "bytes": 3125,
+      "sha256": "87a4cec9e0466250b635e561c74be81f2ce5f15fbd79804a1be639c3719a69d3"
+    },
+    {
+      "path": "detectors/tiny-v2/gauge-detector.onnx",
+      "bytes": 966419,
+      "sha256": "99bdd483402444e8c799452d9885c1184980a41f2cd80d4af4d7a6b3ae00abc6"
+    },
+    {
+      "path": "detectors/tiny-v2/metadata.json",
+      "bytes": 4356,
+      "sha256": "44af06b2619f3a8e084eec836245cc70038bc8cce3cbe80fddd6b3d397d6e970"
+    },
+    {
+      "path": "gauge-reader.onnx",
+      "bytes": 2213746,
+      "sha256": "97029f70068f4ec276b3d6bc28810763275806f579d91ddd4701b544af392147"
+    },
+    {
+      "path": "licenses/detectors/LICENSE-TORCHVISION-BSD-3-CLAUSE.txt",
+      "bytes": 1517,
+      "sha256": "6502f676851cfe25f8af75531dfb32375b7325b73c37e7b43741fa422893e71d"
+    },
+    {
+      "path": "licenses/detectors/NOTICE",
+      "bytes": 1259,
+      "sha256": "4721e1cd64cca08cb27bdbce1ade017ad7d5cf3541b4142cf5dd44cd29e03da3"
+    },
+    {
+      "path": "licenses/reader/LICENSE-APACHE-2.0-UPSTREAM.txt",
+      "bytes": 11343,
+      "sha256": "71b111620fa32c17a80ccd6db2da759d31a591e497d8b5f382174f59d1b59d49"
+    },
+    {
+      "path": "licenses/reader/LICENSE-MIT-OMATRACK-CODE.txt",
+      "bytes": 1078,
+      "sha256": "f14ce1144836b7d366f77f79e0a8d15a8add9689fddc2c9c05d4af0e81882d77"
+    },
+    {
+      "path": "licenses/reader/NOTICE",
+      "bytes": 1449,
+      "sha256": "56dd06fceed7b5c52ab2696e67aa3dc61954379c5a42b9fac5b386726214782f"
+    }
+  ],
+  "privacy": "models/contracts/notices only; no footage/datasets/checkpoints/fixtures",
+  "task_weights_license": "unspecified; scoped upstream notices retained"
+}

licenses/detectors/LICENSE-TORCHVISION-BSD-3-CLAUSE.txt

@@ -0,0 +1,29 @@
+BSD 3-Clause License
+
+Copyright (c) Soumith Chintala 2016, 
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

licenses/detectors/NOTICE

@@ -0,0 +1,22 @@
+Omatrack gauge detector backbones and pretrained-weight provenance
+
+Upstream: torchvision / PyTorch vision
+Copyright (c) Soumith Chintala 2016. BSD 3-Clause License.
+Source: https://github.com/pytorch/vision
+Full notice: LICENSE-TORCHVISION-BSD-3-CLAUSE.txt
+
+Lightweight detector: torchvision MobileNetV3-small prefix plus task-specific
+feature-pyramid, center, box, representation and semantic heads.
+Pretrained model: https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth
+Pretrained SHA256: 047dcff4addef86ea5bc2eff13c9614dc11f47ab1160d0a71a25e7db994f4e1f
+
+AiM-specific detector: full torchvision ConvNeXt-Tiny features plus task-specific
+feature-pyramid and center-based heads; classifier removed.
+Pretrained model: https://download.pytorch.org/models/convnext_tiny-983f1562.pth
+Pretrained SHA256: 983f1562536e84ff750a1576fb08e54de751dbf2e17c0d8a4a13704341fdcd3d
+
+Modification notice: these are fine-tuned gauge detectors, not the original
+ImageNet classifiers. They predict boxes/types and optional semantic labels,
+not gauge readings. Their immutable ONNX/contract hashes are in inventory.json.
+Task-specific weight licensing remains unspecified. This notice preserves
+upstream terms without inventing a new weights or source-data license.

licenses/reader/LICENSE-APACHE-2.0-UPSTREAM.txt

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "{}"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright 2019 Ross Wightman
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

licenses/reader/LICENSE-MIT-OMATRACK-CODE.txt

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Omatrack contributors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

licenses/reader/NOTICE

@@ -0,0 +1,28 @@
+Omatrack telemetry reader 1.0.0
+
+Upstream component: PyTorch Image Models (timm), MobileNetV3-small
+Copyright 2019 Ross Wightman
+Licensed under the Apache License, Version 2.0.
+Full upstream license: LICENSE-APACHE-2.0-UPSTREAM.txt
+Source: https://github.com/huggingface/pytorch-image-models
+Pretrained model: https://huggingface.co/timm/mobilenetv3_small_100.lamb_in1k
+
+Modification notice: this is not the original ImageNet classifier. The model
+retains the pretrained stem and early backbone blocks, adds a crop encoder,
+digit/CTC, visible-fill and digit-count heads, fine-tunes for gauge reading,
+and exports the resulting task model to ONNX. No new training was performed
+for this publication. Private training data and original checkpoints are not
+included.
+
+Omatrack-derived example source code
+Copyright (c) 2026 Omatrack contributors
+MIT License: LICENSE-MIT-OMATRACK-CODE.txt
+The read_frame.py and test_read_frame.py examples adapt the public Omatrack
+reader conventions and contain no private image or recording fixtures.
+Source: https://github.com/tobi/omatrack
+
+The license for the new task-specific model weights has not yet been assigned.
+The upstream Apache-2.0 and example-code MIT notices are scoped to their
+respective components; neither is a blanket license grant for the new model.
+No rights to private footage, screenshots, labels or datasets are granted.
+Third-party names identify compatibility/provenance, not endorsement.