pascal-triheadnet

Pascal-TriheadNet: Joint Detection & Segmentation

Single-stage unified perception model for Pascal VOC: Detection, Semantic, and Instance Segmentation in one forward pass.

Pascal-TriheadNet is a multi-task learning model that jointly solves three computer vision tasks using a unified Vision Transformer backbone with three specialized task heads. Validated on Pascal VOC 2012, it achieves strong performance across all tasks while maintaining efficient inference.

🔗 View Full Code & Documentation on GitHub

🚀 Key Highlights

• Detection mAP@50: 75.6%
• Semantic mIoU: 87.3%
• Instance Mask mAP@50: 65.7%
• Architecture: One Backbone, One Neck, Three Heads (ViT + FPN)

📥 Model Checkpoints

Two versions of the model are provided:

Training Context: Model was fine-tuned on an L4 GPU in Google Colab.

📊 Performance Metrics

Evaluated on the Pascal VOC 2012 Validation set:

For detailed per-class analysis and ablation studies, please refer to the GitHub Repository.

🏗 Model Overview

The architecture utilizes a Vision Transformer (ViT-Base) backbone pretrained on ImageNet.

1. Backbone: vit_base_patch16_224 with the last 6 blocks fine-tuned.
2. Neck: A Simple Feature Pyramid (ViTDet-style) that creates multi-scale feature maps (P2-P5) from the single-scale ViT output.
3. Heads:
   • Detection: FCOS-style anchor-free detector.
   • Semantic: Panoptic FPN-style segmentation head.
   • Instance: Mask R-CNN-style head using RoI Align.

⚙️ Training Configuration

• Epochs: 50
• Batch Size: 32
• Optimizer: AdamW (Base LR: 2e-4)
• Loss: Weighted sum of Focal Loss (Det), Cross-Entropy/Dice (Sem/Inst), and GIoU (Box).

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Model Details

• Developed by: Sivasubiramaniam Subbiah
• Model type: Multi-task Vision Model
• Language(s): Python, PyTorch
• License: MIT
• Finetuned from: Vision Transformer (ViT)