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Moonxc/
OCDet

pytorch

object-center-detection

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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 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.