unicornwhodev/lite-rt_models_cadrylpro

LiteRT Models for Cadryl Pro

Six specialized models in 13 LiteRT files, with input/output contracts, labels, artifact hashes and CPU inference examples. The repository is public; access to its files requires manual approval on Hugging Face.

Model catalogue

Model	Formats	Input	Test images	Recorded FP16 result
Isolated waste classification	FP32, FP16	224 px	238	Balanced accuracy: 0.8521
Synthetic material classification	FP32, FP16	320 px	1,480	Balanced accuracy: 0.9993
PCB1 anomaly classification, 448 px	FP32, FP16	448 px	442	Balanced accuracy: 0.9713
Helmets, heads and people	FP32, FP16	320 px	478	mAP50:95: 0.2775
Pothole segmentation	FP32, FP16, INT8	256 px	60	Foreground IoU: 0.6750
PCB1 anomaly classification, 320 px	FP32, FP16	320 px	442	Balanced accuracy: 0.9325

Scores are specific to each model's test set and metric; they are not a common cross-task ranking. The 320 px PCB1 model is a separate compact variant of the 448 px model. See catalogue.json for exact values and source revisions.

Get started

Request access on this page. After approval, authenticate and download the pinned package:

hf auth login
hf download unicornwhodev/lite-rt_models_cadrylpro --revision 88254abbe2b8ed6932a19c06308a59b252970953 --local-dir cadryl-models
cd cadryl-models
python -m pip install tensorflow-cpu==2.15.1 numpy==1.26.4 pillow==10.4.0
python verify_package.py
python infer.py models/trashnet your-photo.jpg --precision fp16

For a binary mask or bounding boxes:

python infer.py models/nids-de-poule road.jpg --precision int8 --mask-output mask.png
python infer.py models/casques worksite.jpg --precision fp16

Each model directory contains .tflite files, labels.txt, contract.json, format-specific metrics and CPU inference evidence. FP16 is the default weight precision; its input remains float32. The pothole INT8 variant accepts uint8 input and can retain floating-point operations.

Images are decoded as RGB and normalization is inside the graph. Classification and segmentation use direct bilinear resizing. The detector preserves aspect ratio, pads with gray 127.5, and places the resized image at the top left. Its named SSD outputs are mapped back to the original photograph's coordinates.

Verification and limitations

The retained 5 October 2026 verification record reports that all 13 files were loaded and executed on Windows CPU with TensorFlow Lite 2.15.1, with checks of hashes, signatures, shapes and dtypes. The original package review also reran the full waste and pothole test sets (238 and 60 images); the other scores come from retained complete Colab evaluations. This card refresh does not repeat those executions. verify_package.py checks and runs a downloaded copy.

Use the models within their documented domains: isolated waste objects, synthetic material renders, the PCB1 board family and road imagery for potholes. Transfer of the material classifier to real photographs is unmeasured. Helmet/head/person detections require human review and do not establish individual safety compliance.

Android phone qualification, on-device latency and validation on your own task images remain open. Cadryl mask and SSD adapters require their own integration checks. A test-set score is not a guarantee on another domain.

Attribution and terms

Read NOTICES.md, the notices in licenses/ and the sources recorded in catalogue.json. The existing Apache-2.0 code licence applies separately; no new blanket licence for model weights is granted. Upstream terms remain applicable. The pothole source's recorded CC-BY-4.0/Apache-2.0 declaration discrepancy remains documented in the notices. Access approval does not replace a licence.

Documentation and revision

Card updated in English on 5 October 2026, from the public repository at 88254abbe2b8ed6932a19c06308a59b252970953 and its retained reports. This update checks documentation, repository metadata and small evidence files; it does not rerun training, inference, dataset payload verification or device qualification. Historical receipts keep their original dates, revisions and scope. Earlier README hashes in artifact manifests refer to those earlier releases.