Lien-Feng/Lightweight-2-5D-LUNA16

object-detection model by Lien-Feng

Open original source ↗

Older snapshot: check the original source for current information.

From the publisher

Model card & documentation

Source preview

Read the publisher’s intended use, setup instructions, evaluations and limitations. The original model card is the source of truth.

Reference implementation for "Inter-Slice Representation Outweighs Bounding-Box Supervision Extent in Lightweight 2.5D Pulmonary Nodule Detection: A Whole-Volume Benchmark on LUNA16" (MDPI Diagnostics, under revision). The study compares two training-time design choices for a capacity-constrained detector - the inter-slice input representation and the spatial extent of bounding-box supervision - under the official LUNA16 evaluation protocol with whole-volume inference. Headline findings. The inter-slice representation dominates: adjacent-slice 2.5D stacking exceeds a 2D central-slice baseline by +0.130 CPM, with an advantage in all ten official folds, while thin-slab maximum-intensity projection is worse than a plain 2D slice. Reducing the bounding-box supervision extent confers no benefit, and across the (r, wmin) design grid performance tracks the fraction of training boxes clamped at the minimum box size more closely than it tracks either factor alone. Replacing the adjacent-slice channels with copies of the centre slice costs 71 % of the detection confidence for nodules only the…

Lien-Feng/Lightweight-2-5D-LUNA16 on Hugging Face A short preview, not the full document.

Read the full model card ↗ · Preview checked 2026-10-28T03:39:49.245Z

Inside the original model card — Document outline
  1. Lightweight 2.5D pulmonary nodule detection on LUNA16
  2. What is in this repository
  3. Evaluation protocol
  4. Results
  5. Usage
  6. Input construction
  7. Configurations
  8. Intended use and limitations
  9. Citation

Headings are captured from the source. Links open the publisher’s document, not a locally hosted copy.

Documentation belongs to its respective authors. Reported project/model license: cc-by-4.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.

Model overview

Lien-Feng/Lightweight-2-5D-LUNA16 is a object-detection model repository published by Lien-Feng on Hugging Face. The source reports the ultralytics library.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task
object-detection
Library
ultralytics
Recent downloads (30 days)
2,358
Cumulative likes
0
Hugging Face trending score
0
Reported safetensors parameters
Not reported
Architecture
Not reported
License
cc-by-4.0
Access
Not gated by Hugging Face
Created
2026-08-19T03:01:31.000Z
Last modified
2026-09-04T10:37:47.000Z

Compatibility and lineage

Language coverage was not reported.

Base-model lineage was not included.

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

Use and evaluate

Check license terms and access requirements first. Review the model card’s documented loading instructions, evaluations and safety limitations. Benchmark scores are not imported by this directory, and popularity is not an accuracy ranking.

No model weights are downloaded or executed by AltAPIs. Never enable remote model code without reviewing it.

Model card and usage instructions

Model files and configuration

Community discussion

Source and freshness

Source: Hugging Face Hub. Metadata observed 2026-09-19T06:35:21.686Z. Daily imports are snapshots, not real-time monitoring.

Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.

Open original source

Related models

mudler/locate-anything.cpp-gguf

microsoft/table-transformer-structure-recognition

PekingU/rtdetr_r101vd_coco_o365

PaddlePaddle/PP-DocLayoutV3_safetensors

hustvl/yolos-small

microsoft/table-transformer-detection