| --- |
| license: apache-2.0 |
| pipeline_tag: image-segmentation |
| tags: |
| - model_hub_mixin |
| - pytorch_model_hub_mixin |
| --- |
| |
| # EdgeCrafter: ECSeg |
|
|
| EdgeCrafter is a unified compact Vision Transformer (ViT) framework designed for efficient dense prediction tasks on edge devices. This specific checkpoint is part of the **ECSeg** series, which focuses on high-performance instance segmentation using a distilled compact backbone and an edge-friendly encoder-decoder design. |
|
|
| - **Project Page:** [EdgeCrafter](https://intellindust-ai-lab.github.io/projects/EdgeCrafter/) |
| - **Paper:** [EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation](https://arxiv.org/abs/2603.18739) |
| - **Repository:** [GitHub - Intellindust-AI-Lab/EdgeCrafter](https://github.com/Intellindust-AI-Lab/EdgeCrafter) |
|
|
| ## Performance (Instance Segmentation on COCO2017) |
|
|
| | Model | Size | AP<sub>50:95</sub> | #Params | GFLOPs | Latency (ms) | |
| |:-----:|:----:|:--:|:-------:|:------:|:------------:| |
| | **ECSeg-S** | 640 | 43.0 | 10M | 33 | 6.96 | |
| | **ECSeg-M** | 640 | 45.2 | 20M | 64 | 9.85 | |
| | **ECSeg-L** | 640 | 47.1 | 34M | 111 | 12.56 | |
| | **ECSeg-X** | 640 | 48.4 | 50M | 168 | 14.96 | |
|
|
| *Note: Latency is measured on an NVIDIA T4 GPU with batch size 1 under FP16 precision using TensorRT (v10.6).* |
|
|
| ## Usage |
|
|
| To run inference with this model, follow the instructions in the official repository. You can use the provided inference script: |
|
|
| ```bash |
| # 1. Clone the repository and install dependencies |
| git clone https://github.com/Intellindust-AI-Lab/EdgeCrafter |
| cd EdgeCrafter/ecdetseg |
| pip install -r requirements.txt |
| |
| # 2. Run PyTorch inference |
| # Replace `path/to/your/image.jpg` with an actual image path |
| python tools/inference/torch_inf.py -c configs/ecseg/ecseg_s.yml -r ecdet_s.pth -i path/to/your/image.jpg |
| ``` |
|
|
| ## Citation |
|
|
| ```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={arXiv}, |
| year={2026} |
| } |
| ``` |
|
|
| This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration. |
