Pedestrian-Segmentation

This project applies transfer learning on the Mask-RCNN model to train detection and segmentation predictor heads on the Penn-Fudan Dataset for Pedestrain Detection and Segmentaion. Click here to view the dataset.

Getting Started

To install all dependencies

pip install -r requirements.txt

To train the model and view inference

python ./src/PedestrianDetection.py

Model Performance

Loss

loss: 0.1434 (0.1577)  loss_classifier: 0.0191 (0.0247)

IoU metric: bbox

Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.833

Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.991

Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.959

Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000

Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.588

Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.844

Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.382

Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.871

Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.871

Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000

Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.787

Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.877

IoU metric: segm

Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.767

Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.991

Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.912

Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000

Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.458

Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.776

Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.352

Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.808

Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.808

Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000

Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.750

Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.813

Aditya239233 / Pedestrian-Segmentation