[TMLR 26] EdgeCrafter: Compact ViTs for Edge Dense Prediction via
Task-Specialized Distillation
English | 简体中文
Longfei Liu *‡
Yongjie Hou *
Yang Li *
Qirui Wang *
Youyang Sha
Yongjun Yu
Yinzhi Wang
Peizhe Ru
Xuanlong Yu†
Xi Shen †
* Equal Contribution ‡ Project Lead † Corresponding Author
Intellindust AI Lab
---
## 🚀 Updates
- **[2026-08-20]** Thanks to [@Apolinario](https://github.com/apolinario) from the Hugging Face open-source team for building an interactive EdgeCrafter demo on Hugging Face Spaces. Try the [Demo](https://huggingface.co/spaces/Intellindust/edgecrafter-detection).
- **[2026-08-14]** We released the Objects365 pretrained checkpoints. Enjoy it!
- **[2026-08-13]** EdgeCrafter has been integrated into [Intel® Geti™](https://github.com/open-edge-platform/geti): fine-tune ECDet-S/M/L/X on your own data with no code, train on Intel dGPU / iGPU / CPU, export to OpenVINO™ IR and optimized for INT8 for edge deployment.
- **[2026-08-11]** EdgeCrafter has been accepted for publication in **TMLR 2026** 🎉
- **[2026-07-23]** EdgeCrafter has been integrated into [LightlyTrain](https://docs.lightly.ai/train/stable/pretrain_distill/models/edgecrafter.html)
- **[2026-07-22]** EdgeCrafter has been integrated into [LibreYOLO](https://www.libreyolo.com/zh).
- **[2026-04-20]** Our previous version [DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) was used by two winning teams at the [CVPR 2026 Maritime Computer Vision Workshop](https://arxiv.org/abs/2604.13244), taking home 2nd place in Thermal Object Detection Challenge and 3rd place in Vision-to-Chart Data Association Challenge.
- **[2026-03-21]** We have uploaded our models on 🤗 Hugging Face.
- **[2026-03-19]** Initial release of EdgeCrafter.
---
## 🤗 Hugging Face
We have uploaded our models on 🤗 Hugging Face! You can also access these models via [hf_models.ipynb](./hf_models.ipynb). Have a try!
---
## 📍 Reproducing the Results
- **Detection & Instance Segmentation:** [Instructions](./ecdetseg)
- **Pose Estimation:** [Instructions](./ecpose)
---
## 🏆 Model Zoo
### COCO2017 Validation Results
> **Note**: Latency is measured on an NVIDIA T4 GPU with batch size 1 under FP16 precision using TensorRT (v10.6).
> `--` denotes COCO-only training, and `O365` denotes additional Objects365 pretraining. For ECSeg and ECPose, the `O365` results are obtained by directly transferring the detection model pretrained on Objects365.
### Object Detection
| Model | Extra Sup. | Size | AP50:95 | #Params | GFLOPs | Latency (ms) | Config | Log | Checkpoint |
|:-----:|:----------:|:----:|:------------------:|:-------:|:------:|:------------:|:------:|:---:|:----------:|
| **ECDet-S** | -- | 640 | 51.7 | 10 | 26 | 5.41 | [config](ecdetseg/configs/ecdet/ecdet_s.yml) | [log](https://github.com/capsule2077/edgecrafter/raw/refs/heads/main/logs/ecdet_s.log) | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecdet_s.pth) |
| **ECDet-S** | O365 | 640 | 53.6 | 10 | 26 | 5.41 | - | - | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1_o365/ecdet_s_o3652coco.pth) |
| **ECDet-M** | -- | 640 | 54.3 | 19 | 53 | 7.98 | [config](ecdetseg/configs/ecdet/ecdet_m.yml) | [log](https://github.com/capsule2077/edgecrafter/raw/refs/heads/main/logs/ecdet_m.log) | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecdet_m.pth) |
| **ECDet-M** | O365 | 640 | 56.7 | 19 | 53 | 7.98 | - | - | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1_o365/ecdet_m_o3652coco.pth) |
| **ECDet-L** | -- | 640 | 57.0 | 33 | 101 | 10.49 | [config](ecdetseg/configs/ecdet/ecdet_l.yml) | [log](https://github.com/capsule2077/edgecrafter/raw/refs/heads/main/logs/ecdet_l.log) | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecdet_l.pth) |
| **ECDet-L** | O365 | 640 | 59.0 | 33 | 101 | 10.49 | - | - | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1_o365/ecdet_l_o3652coco.pth) |
| **ECDet-X** | -- | 640 | 57.9 | 49 | 151 | 12.70 | [config](ecdetseg/configs/ecdet/ecdet_x.yml) | [log](https://github.com/capsule2077/edgecrafter/raw/refs/heads/main/logs/ecdet_x.log) | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecdet_x.pth) |
| **ECDet-X** | O365 | 640 | 59.9 | 49 | 151 | 12.70 | - | - | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1_o365/ecdet_x_o3652coco.pth) |
### Objects365 Pretrained Checkpoints
| Model | Checkpoint |
|:-----:|:----------:|
| **ECDet-S** | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1_o365/ecdet_s_o365.pth) |
| **ECDet-M** | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1_o365/ecdet_m_o365.pth) |
| **ECDet-L** | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1_o365/ecdet_l_o365.pth) |
| **ECDet-X** | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1_o365/ecdet_x_o365.pth) |
### Instance Segmentation
| Model | Extra Sup. | Size | AP50:95 | #Params | GFLOPs | Latency (ms) | Config | Log | Checkpoint |
|:-----:|:----------:|:----:|:------------------:|:-------:|:------:|:------------:|:------:|:---:|:----------:|
| **ECSeg-S** | -- | 640 | 43.0 | 10 | 33 | 6.96 | [config](ecdetseg/configs/ecseg/ecseg_s.yml) | [log](https://github.com/capsule2077/edgecrafter/raw/refs/heads/main/logs/ecseg_s.log) | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecseg_s.pth) |
| **ECSeg-S** | O365 | 640 | 43.9 | 10 | 33 | 6.96 | - | - | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1_o365/ecseg_s_o3652coco.pth) |
| **ECSeg-M** | -- | 640 | 45.2 | 20 | 64 | 9.85 | [config](ecdetseg/configs/ecseg/ecseg_m.yml) | [log](https://github.com/capsule2077/edgecrafter/raw/refs/heads/main/logs/ecseg_m.log) | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecseg_m.pth) |
| **ECSeg-M** | O365 | 640 | 46.9 | 20 | 64 | 9.85 | - | - | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1_o365/ecseg_m_o3652coco.pth) |
| **ECSeg-L** | -- | 640 | 47.1 | 34 | 111 | 12.56 | [config](ecdetseg/configs/ecseg/ecseg_l.yml) | [log](https://github.com/capsule2077/edgecrafter/raw/refs/heads/main/logs/ecseg_l.log) | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecseg_l.pth) |
| **ECSeg-L** | O365 | 640 | 48.8 | 34 | 111 | 12.56 | - | - | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1_o365/ecseg_l_o3652coco.pth) |
| **ECSeg-X** | -- | 640 | 48.4 | 50 | 168 | 14.96 | [config](ecdetseg/configs/ecseg/ecseg_x.yml) | [log](https://github.com/capsule2077/edgecrafter/raw/refs/heads/main/logs/ecseg_x.log) | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecseg_x.pth) |
| **ECSeg-X** | O365 | 640 | 49.8 | 50 | 168 | 14.96 | - | - | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1_o365/ecseg_x_o3652coco.pth) |
### Pose Estimation
| Model | Extra Sup. | Size | AP50:95 | #Params | GFLOPs | Latency (ms) | Config | Log | Checkpoint |
|:-----:|:----------:|:----:|:------------------:|:-------:|:------:|:------------:|:------:|:---:|:----------:|
| **ECPose-S** | -- | 640 | 68.9 | 10 | 30 | 5.54 | [config](ecpose/configs/ecpose/ecpose_s_coco.yml) | [log](https://github.com/capsule2077/edgecrafter/raw/refs/heads/main/logs/ecpose_s.log) | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecpose_s.pth) |
| **ECPose-S** | O365 | 640 | 69.7 | 10 | 30 | 5.54 | - | - | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1_o365/ecpose_s_o3652coco.pth) |
| **ECPose-M** | -- | 640 | 72.4 | 20 | 63 | 9.25 | [config](ecpose/configs/ecpose/ecpose_m_coco.yml) | [log](https://github.com/capsule2077/edgecrafter/raw/refs/heads/main/logs/ecpose_m.log) | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecpose_m.pth) |
| **ECPose-M** | O365 | 640 | 73.1 | 20 | 63 | 9.25 | - | - | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1_o365/ecpose_m_o3652coco.pth) |
| **ECPose-L** | -- | 640 | 73.5 | 34 | 112 | 11.83 | [config](ecpose/configs/ecpose/ecpose_l_coco.yml) | [log](https://github.com/capsule2077/edgecrafter/raw/refs/heads/main/logs/ecpose_l.log) | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecpose_l.pth) |
| **ECPose-L** | O365 | 640 | 74.5 | 34 | 112 | 11.83 | - | - | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1_o365/ecpose_l_o3652coco.pth) |
| **ECPose-X** | -- | 640 | 74.8 | 51 | 172 | 14.31 | [config](ecpose/configs/ecpose/ecpose_x_coco.yml) | [log](https://github.com/capsule2077/edgecrafter/raw/refs/heads/main/logs/ecpose_x.log) | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecpose_x.pth) |
| **ECPose-X** | O365 | 640 | 75.9 | 51 | 172 | 14.31 | - | - | [model](https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1_o365/ecpose_x_o3652coco.pth) |
---
## 📦 Installation
```bash
# Create conda environment
conda create -n ec python=3.11 -y
conda activate ec
# Install dependencies
pip install -r requirements.txt
```
### Intel XPU (Intel iGPU / dGPU)
EdgeCrafter supports Intel iGPUs and dGPUs through PyTorch's `torch.xpu` backend. First install an XPU-enabled PyTorch build by following the [official PyTorch XPU instructions](https://docs.pytorch.org/docs/stable/notes/get_start_xpu.html), then install the remaining dependencies:
```bash
conda create -n ec-xpu python=3.11 -y
conda activate ec-xpu
# Install PyTorch with Intel XPU support
pip install torch==2.12.1+xpu torchvision --index-url https://download.pytorch.org/whl/xpu
pip install -r requirements.txt
```
Run inference or training with `-d xpu` option.
## ⚡ Quick Start (Inference)
The easiest way to test EdgeCrafter is to run inference on a sample image using a pre-trained model.
```bash
# 1. Download a pre-trained model (e.g., ECDet-L)
cd ecdetseg
wget https://github.com/capsule2077/edgecrafter/releases/download/edgecrafterv1/ecdet_l.pth
# 2. Run PyTorch inference
# Make sure to replace `path/to/your/image.jpg` with an actual image path
# Add "-d cuda / xpu / cpu" option for selecting a device
python tools/inference/torch_inf.py -c configs/ecdet/ecdet_l.yml -r ecdet_l.pth -i path/to/your/image.jpg
```
## 📄 License
This project is released under the EdgeCrafter License. See the [LICENSE](./LICENSE.md) file for details.
For commercial licensing inquiries, please [Contact Us](https://www.intellindust.cn/#contact) or email shenxi@intellindust.com.
---
## 🙏 Acknowledgements
We thank the authors of the following open-source projects that made this work possible: [RT-DETR](https://github.com/lyuwenyu/RT-DETR), [D-FINE](https://github.com/Peterande/D-FINE), [DEIM](https://github.com/Intellindust-AI-Lab/DEIM), [lightly-train](https://github.com/lightly-ai/lightly-train), [DETRPose](https://github.com/SebastianJanampa/DETRPose), [RF-DETR](https://github.com/roboflow/rf-detr), [DINOv3](https://github.com/facebookresearch/dinov3)
---
## 📚 Citation
If you find this project useful in your research, please consider citing:
```bibtex
@article{liu2026edgecrafter,
title={EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation},
author={Liu, Longfei and Hou, Yongjie and Li, Yang and Wang, Qirui and Sha, Youyang and Yu, Yongjun and Wang, Yinzhi and Ru, Peizhe and Yu, Xuanlong and Shen, Xi},
journal={TMLR},
year={2026}
}
```