| --- |
| license: apache-2.0 |
| library_name: pytorch |
| pipeline_tag: object-detection |
| tags: |
| - object-center-detection |
| - keypoint-detection |
| - edge |
| - npu |
| - mobilenetv4 |
| - coco |
| --- |
| |
| # OCDet: Object Center Detection via Bounding Box-Aware Heatmap Prediction on Edge Devices with NPUs |
|
|
| OCDet is a lightweight Object Center Detection framework optimized for edge devices |
| with NPUs. It predicts heatmaps of object center probabilities and extracts center |
| points via peak identification. Built on an NPU-friendly Semantic FPN with |
| MobileNetV4 backbones, OCDet is trained with Balanced Continuous Focal Loss (BCFL) |
| and evaluated with the Center Alignment Score (CAS). |
|
|
| - **Code:** https://github.com/chen-xin-94/ocdet |
| - **Backbones:** MobileNetV4 (conv small / medium / large) |
| - **Tasks:** COCO 80-class object center detection (OCDet) and person-only center detection (PCDet) |
|
|
| ## Files |
|
|
| This repo hosts 10 checkpoints. **Each `<model>.pt` has a matching `<model>.yaml`** |
| that records the exact architecture and hyperparameters it was trained with. |
| Always load a weight together with its own `.yaml` — the training templates in the |
| GitHub `configs/` folder can disagree with the released weights (e.g. `pcdet-n` |
| was trained with `out_channel=32`, not `64`). |
|
|
| | Group | Weights | |
| |-------|---------| |
| | OCDet (COCO 80-class) | `ocdet-{n,s,m,l,x}.pt` (+ `.yaml`) | |
| | PCDet (person-only) | `pcdet-{n,s,m,l,x}.pt` (+ `.yaml`) | |
|
|
| ## Model Zoo |
|
|
| ### COCO 80-class Object Center Detection (OCDet) |
|
|
| | Model | #Params (M) | FLOPs (G) | NPU Latency i.MX 8M Plus (ms) | CAS ↑ | CP ↓ | MD ↓ | P ↑ | R ↑ | F1 ↑ | |
| |-------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
| | OCDet-N | 1.51 | 0.54 | 10.94 | 0.297 | 0.645 | 0.059 | 0.634 | 0.466 | 0.510 | |
| | OCDet-S | 1.59 | 0.94 | 24.25 | 0.313 | 0.644 | 0.055 | 0.621 | 0.507 | 0.531 | |
| | OCDet-M | 8.25 | 3.54 | 63.26 | 0.362 | 0.573 | 0.065 | 0.643 | 0.585 | 0.599 | |
| | OCDet-L | 31.88 | 7.67 | 94.14 | 0.389 | 0.563 | 0.064 | 0.694 | 0.596 | 0.630 | |
| | OCDet-X | 22.20 | 7.00 | 181.81 | 0.410 | 0.525 | 0.066 | 0.713 | 0.643 | 0.669 | |
|
|
| ### Person Center Detection (PCDet) |
|
|
| | Model | #Params (M) | FLOPs (G) | NPU Latency i.MX 8M Plus (ms) | CAS ↑ | CP ↓ | MD ↓ | P ↑ | R ↑ | F1 ↑ | |
| |-------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
| | PCDet-N | 1.51 | 0.52 | 9.99 | 0.472 | 0.440 | 0.088 | 0.815 | 0.745 | 0.779 | |
| | PCDet-S | 1.59 | 0.92 | 18.41 | 0.506 | 0.411 | 0.083 | 0.830 | 0.762 | 0.794 | |
| | PCDet-M | 7.79 | 2.75 | 39.68 | 0.546 | 0.379 | 0.075 | 0.910 | 0.715 | 0.801 | |
| | PCDet-L | 30.96 | 6.06 | 60.47 | 0.557 | 0.366 | 0.077 | 0.894 | 0.750 | 0.816 | |
| | PCDet-X | 21.91 | 6.49 | 160.37 | 0.582 | 0.337 | 0.081 | 0.831 | 0.848 | 0.840 | |
|
|
| ## Usage |
|
|
| Clone the [code](https://github.com/chen-xin-94/ocdet) and install its |
| dependencies, then download a weight together with its config: |
|
|
| ```bash |
| mkdir -p weights |
| # either grab a single model + its config ... |
| wget -P weights https://huggingface.co/Moonxc/OCDet/resolve/main/pcdet-n.pt |
| wget -P weights https://huggingface.co/Moonxc/OCDet/resolve/main/pcdet-n.yaml |
| # ... or pull the whole repo at once |
| # pip install -U huggingface_hub && hf download Moonxc/OCDet --local-dir weights |
| ``` |
|
|
| Run inference (`predict.py` auto-loads `weights/pcdet-n.yaml` next to the weight): |
|
|
| ```bash |
| python predict.py \ |
| --input_path images/000000032081.jpg \ |
| --trained weights/pcdet-n.pt \ |
| --n_classes 1 \ |
| --gpu -1 \ |
| --min_distance 3 --threshold_abs 0.5 --input_size 320 \ |
| --vis --save |
| ``` |
|
|
| ## Citation |
|
|
| If you use OCDet, please cite the work and link back to the |
| [GitHub repository](https://github.com/chen-xin-94/ocdet). |
|
|
