You can deploy this model and start earning money today!

giswqs/
nwpu-vhr10-maskrcnn

Name: nwpu-vhr10-maskrcnn
Availability: InStock

Views

No views yet

Mask R-CNN for Multi-Class Object Detection on NWPU-VHR-10

This model is a Mask R-CNN with a ResNet-50 FPN backbone, trained for multi-class object detection on very high resolution (VHR) remote sensing imagery using the NWPU-VHR-10 benchmark dataset.

Model Description

Architecture: Mask R-CNN with ResNet-50 FPN backbone (torchvision maskrcnn_resnet50_fpn)
Task: Multi-class object detection and instance segmentation
Domain: Very high resolution (VHR) remote sensing imagery
Training Framework: PyTorch + torchvision
Package: Trained using geoai-py

Training Data

The NWPU-VHR-10 dataset was constructed by researchers at Northwestern Polytechnical University (NWPU). It contains:

800 VHR remote sensing images (650 positive with annotations, 150 negative)
3,775 annotated object instances across 10 classes
Images cropped from Google Earth and the Vaihingen dataset
Manually annotated by domain experts

Classes

ID	Class	ID	Class
1	airplane	6	basketball_court
2	ship	7	ground_track_field
3	storage_tank	8	harbor
4	baseball_diamond	9	bridge
5	tennis_court	10	vehicle

Data Split

Split	Images	Purpose
Train	509 (85%)	Model training
Val	128 (15%)	Evaluation

Training Details

Parameter	Value
Backbone	ResNet-50 FPN (ImageNet pretrained)
Optimizer	SGD (momentum=0.9, weight_decay=0.0005)
Learning Rate	0.005 with StepLR (step=3, gamma=0.1)
Batch Size	4
Epochs	20
Input Channels	3 (RGB)
Seed	42

Evaluation Results

Evaluated on the validation set (128 images) using standard COCO detection metrics:

Metric	Value
mAP@0.5	0.709
mAP@0.75	0.518
mAP@[0.5:0.95]	0.459

Per-Class AP@0.5

Class	AP@0.5	Class	AP@0.5
tennis_court	0.902	harbor	0.695
basketball_court	0.878	bridge	0.678
baseball_diamond	0.821	storage_tank	0.591
ground_track_field	0.807	vehicle	0.532
airplane	0.713	ship	0.470

Usage

With geoai

python

import geoai

# Run inference on a remote sensing image
result_path, inference_time, detections = geoai.multiclass_detection(
    input_path="your_image.tif",
    output_path="detections.tif",
    model_path="best_model.pth",
    num_classes=11,
    class_names=[
        "background", "airplane", "ship", "storage_tank",
        "baseball_diamond", "tennis_court", "basketball_court",
        "ground_track_field", "harbor", "bridge", "vehicle",
    ],
    window_size=512,
    overlap=256,
    confidence_threshold=0.5,
)

# Visualize results
geoai.visualize_multiclass_detections(
    image_path="your_image.tif",
    detections=detections,
    class_names=[
        "background", "airplane", "ship", "storage_tank",
        "baseball_diamond", "tennis_court", "basketball_court",
        "ground_track_field", "harbor", "bridge", "vehicle",
    ],
)

With PyTorch directly

python

import torch
import torchvision
from torchvision.models.detection import maskrcnn_resnet50_fpn

# Load model
model = maskrcnn_resnet50_fpn(weights=None, num_classes=11)
checkpoint = torch.load("best_model.pth", map_location="cpu")
model.load_state_dict(checkpoint)
model.eval()

# Run inference
from PIL import Image
from torchvision import transforms

img = Image.open("your_image.jpg").convert("RGB")
img_tensor = transforms.ToTensor()(img).unsqueeze(0)

with torch.no_grad():
    predictions = model(img_tensor)

# predictions[0] contains 'boxes', 'labels', 'scores', 'masks'

Files

File	Description
`best_model.pth`	Best model weights (by validation IoU)
`class_info.json`	Class names and number of classes
`training_summary.txt`	Training configuration summary

Notebook Example

For a complete end-to-end example including dataset download, training, evaluation, and inference, see the NWPU-VHR-10 Object Detection notebook.

Citation

If you use this model, please cite the NWPU-VHR-10 dataset:

bibtex

@article{cheng2014multi,
  title={Multi-class geospatial object detection and geographic image classification based on collection of part detectors},
  author={Cheng, Gong and Han, Junwei and Zhou, Peicheng and Guo, Lei},
  journal={ISPRS Journal of Photogrammetry and Remote Sensing},
  volume={98},
  pages={119--132},
  year={2014}
}

@article{cheng2016survey,
  title={A survey on object detection in optical remote sensing images},
  author={Cheng, Gong and Han, Junwei},
  journal={ISPRS Journal of Photogrammetry and Remote Sensing},
  volume={117},
  pages={11--28},
  year={2016}
}

License

This model is released under the MIT License. The NWPU-VHR-10 dataset is for research purposes only.

giswqs/nwpu-vhr10-maskrcnn