Neural
Object Explorer.
An interactive deployment of Faster R-CNN for localized object recognition. Bridging complex PyTorch vision architectures with an accessible, user-controllable web interface.
Architecture
ResNet-50 FPN
Dataset
COCO Instance
Framework
PyTorch 2.1
Classes
80 Categories
Project Insight
Object detection goes beyond classification by providing spatial awareness. The model must predict bounding box coordinates and class probabilities simultaneously.
Faster R-CNN introduced the Region Proposal Network (RPN), allowing the model to share convolutional features with the detection network, drastically improving speed over its predecessors while maintaining state-of-the-art precision.
Inference Benchmarks
Input: Raw Image Tensor
Output: Processed Detections (0.5 Thresh)
Faster R-CNN Backbone
Utilizes ResNet-50 with Feature Pyramid Network (FPN) for robust object detection across varying scales.
Dynamic Thresholding
Implemented a slider-based interface to filter detections by confidence scores (0.0 to 1.0).
COCO Dataset Support
Capable of identifying 80+ diverse classes including people, vehicles, and everyday household items.
The Inference Pipeline
Images are preprocessed into standardized tensors using Torchvision's default ResNet weights, ensuring consistency with the training distribution.
Bounding boxes are rendered dynamically on the CPU to ensure the interface remains responsive across various hosting environments.