AnnaZhang/lwdetr_small_60e_coco

object-detection model by AnnaZhang

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LW-DETR, a Light-Weight DEtection TRansformer model, is designed to be a real-time object detection alternative that outperforms conventional convolutional (YOLO-style) and earlier transformer-based (DETR) methods in terms of speed and accuracy trade-off. It was introduced in the paper LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection by Chen et al. and first released in this repository. Disclaimer: This model was originally contributed by stevenbucaille in 🤗 transformers. LW-DETR is an end-to-end object detection model that uses a Vision Transformer (ViT) backbone as its encoder, a simple convolutional projector, and a shallow DETR decoder. The core philosophy is to leverage…

AnnaZhang/lwdetr_small_60e_coco on Hugging Face A short preview, not the full document.

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  1. LW-DETR (Light-Weight Detection Transformer)
  2. Model description
  3. How to use
  4. Training data
  5. BibTeX entry and citation info

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Model overview

AnnaZhang/lwdetr_small_60e_coco is a object-detection model repository published by AnnaZhang on Hugging Face. The source reports the transformers 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
transformers
Recent downloads (30 days)
10,051
Cumulative likes
0
Hugging Face trending score
0
Reported safetensors parameters
14,559,938
Architecture
LwDetrForObjectDetection
License
apache-2.0
Access
Not gated by Hugging Face
Created
2026-01-19T09:21:36.000Z
Last modified
2026-01-19T09:21:52.000Z

Compatibility and lineage

Language coverage was not reported.

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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.

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Source: Hugging Face Hub. Metadata observed 2026-11-01T06:54:34.007Z. Daily imports are snapshots, not real-time monitoring.

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