README.md

metadata

title: EdgeCrafter
emoji: 🪶
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 6.24.0
app_file: app.py
short_description: Detection, segmentation & pose with compact edge ViTs
python_version: '3.12'
startup_duration_timeout: 30m
pinned: false

EdgeCrafter

Demo of EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation — one distilled ViT backbone family serving three dense-prediction tasks:

task	models	classes
Object detection	Intellindust/ECDet_{S,M,L,X}	COCO 80
Instance segmentation	Intellindust/ECSeg_{S,M,L,X}	COCO 80
Human pose estimation	Intellindust/ECPose_{S,M,L,X}	COCO 17 keypoints

All twelve checkpoints (~10M → ~50M params) are loaded and selectable in the UI. Preprocessing (640×640 resize + ImageNet normalization), the deploy-mode re-parameterization, postprocessing and the drawing style are ported 1:1 from the authors' tools/inference/torch_inf.py scripts and hf_models.ipynb.

• Paper: https://huggingface.co/papers/2603.18739
• Code: https://github.com/Intellindust-AI-Lab/EdgeCrafter (Apache-2.0)

The ecdetseg/ and ecpose/ folders vendor the inference-only subset of the authors' Apache-2.0 model code.

Example photos are COCO images redistributed from the Apache-2.0 repositories DEIMv2 (example.jpg) and DETRPose (examples/example1.jpg, examples/example2.jpg).