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Seeing Through Noise: Diffusion based Object Detection in Complex Settings

TinyReproduction Project - Course Project

Author: Kunal Agarwal
Course: ECE 57000 - Artificial Intelligence
Institution: Purdue University
Date: Fall 2025
Project Track: TinyReproductions

Table of Contents

  1. Project Overview
  2. Code Attribution
  3. Project Structure
  4. Dependencies
  5. Dataset Setup
  6. Installation Instructions
  7. Running the Project
  8. Expected Results
  9. Troubleshooting
  10. Acknowledgments

Project Overview

This project is a TinyReproduction of the paper:

Chen, S., et al. (2023). "DiffusionDet: Diffusion Model for Object Detection." Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).

Goal

The primary goal is to experimentally verify the central claim of the DiffusionDet paper: that object detection can be successfully framed as a denoising diffusion process. We reproduce this claim in a simplified setting by comparing DiffusionDet against a Faster R-CNN baseline on the PASCAL VOC 2007 dataset.

Simplifications from Original Paper

To make this project feasible within a semester on a single GPU:

  • Dataset: PASCAL VOC 2007 (instead of MS COCO)
  • Backbone: ResNet-50 (instead of larger transformers)
  • Training Schedule: 50 epochs (instead of 100+)
  • Number of Proposals: 300 (instead of 500)
  • Diffusion Steps: 1000 (same as original)

Code Attribution

Original Work (Written by Me)

The following code was written entirely by me for this project inspired by DiffusionDet and related works:

  1. models/diffusion_det.py (Lines 1-344)

    • Complete DiffusionDet implementation
    • Forward diffusion process (q_sample)
    • Reverse diffusion process (inference)
    • Loss computation functions
    • Helper classes: MLP, SinusoidalTimeEmbedding, PositionalEncoding2D

  2. utils/voc_dataset.py (Lines 1-115)

    • VOCDataset class implementation
    • XML annotation parsing
    • Custom collate function

  3. train.py (Lines 1-230)

    • Training loop for DiffusionDet
    • Optimizer configuration
    • Checkpoint saving logic
    • Loss tracking and visualization

  4. evaluate.py (Lines 1-240)

    • Complete mAP@0.5 evaluation implementation
    • IoU calculation
    • Precision-Recall curve computation
    • Average Precision calculation

  5. train_faster_rcnn.py (Lines 1-140)

    • Training script for baseline model

Adapted from External Sources

From Official DiffusionDet Paper

Source: https://github.com/ShoufaChen/DiffusionDet (Apache 2.0 License)

Adaptations:

  • models/diffusion_det.py, Lines 57-74: Cosine beta schedule

    • Original: Used in official implementation with Detectron2
    • Adapted: Simplified to PyTorch-only implementation, removed Detectron2 dependencies

  • models/diffusion_det.py, Lines 133-160: Box embedding and time embedding combination

    • Original: Core architecture from DiffusionDet paper
    • Adapted: Simplified decoder architecture, removed proposal matching

From DDPM Paper (Ho et al., 2020)

Source: "Denoising Diffusion Probabilistic Models" (arXiv:2006.11239)

Adaptations:

  • models/diffusion_det.py, Lines 91-99: Forward diffusion (q_sample)

    • Original: DDPM forward process for images
    • Adapted: Applied to bounding box coordinates instead of pixels

  • models/diffusion_det.py, Lines 170-185: Reverse diffusion sampling

    • Original: DDPM reverse process
    • Adapted: Simplified sampling (removed variance scheduling for faster inference)

Project Structure

diffusiondet_reproduction/
│
├── README.md                          # This file
├── requirements.txt                   # Python dependencies
│
├── models/
│   ├── __init__.py                   # Empty init file
│   └── diffusion_det.py              # DiffusionDet model implementation
│                                      # - DiffusionDet class (main model)
│                                      # - MLP helper class
│                                      # - SinusoidalTimeEmbedding
│                                      # - PositionalEncoding2D
│
├── utils/
│   ├── __init__.py                   # Empty init file
|   ├── diffusion_util.py            # Diffusion utility functions
│   └── voc_dataset.py                # PASCAL VOC dataset loader
│                                      # - VOCDataset class
│                                      # - get_transform function
│                                      # - collate_fn function
|
├── train.py                           # Training script for DiffusionDet
├── train_faster_rcnn.py              # Training script for Faster R-CNN baseline
├── evaluate.py                        # Evaluation script (mAP@0.5)
│
├── download_voc.py                    # Dataset download script
├── verify_dataset.py                  # Dataset verification script
│
├── checkpoints/                       # Model checkpoints (auto-created)
│   ├── best_model.pth                # Best DiffusionDet model
│
└── checkpoints_frcnn/                 # Faster R-CNN checkpoints (auto-created)
    └── best_model.pth                # Best baseline model

File Descriptions

Core Implementation Files

  • models/diffusion_det.py: Complete implementation of DiffusionDet, including the diffusion process, transformer decoder, and prediction heads.
  • utils/voc_dataset.py: Custom PyTorch Dataset class for loading PASCAL VOC 2007 with proper box normalization.

Training and Evaluation

  • train.py: Main training script for DiffusionDet with loss tracking and checkpoint saving.
  • train_faster_rcnn.py: Training script for the Faster R-CNN baseline for comparison.
  • evaluate.py: Evaluation script that computes mAP@0.5 metric on the test set.

Utility Scripts

  • download_voc.py: Automated script to download PASCAL VOC 2007 dataset.
  • verify_dataset.py: Verification script to ensure dataset is properly downloaded and structured.

Dependencies

System Requirements

  • OS: Linux (Ubuntu 20.04+), macOS, or Windows with WSL
  • GPU: NVIDIA GPU with 2GB+ VRAM (recommended)
    • CUDA 11.8 or 12.1
    • Can run on CPU but training will be very slow (~20x slower)
  • RAM: 8GB+ recommended
  • Storage: ~10GB free space (dataset + checkpoints)

Python Version

  • Python 3.8, 3.9, or 3.10 (tested on 3.10)

Required Libraries

All dependencies are listed in requirements.txt:

torch>=2.0.0
torchvision>=0.15.0
numpy>=1.24.0
Pillow>=9.5.0
matplotlib>=3.7.0
tqdm>=4.65.0

Dependency Details

Library Version Purpose
torch ≥2.0.0 Deep learning framework
torchvision ≥0.15.0 Computer vision utilities, Faster R-CNN
numpy ≥1.24.0 Numerical operations
Pillow ≥9.5.0 Image loading and processing
matplotlib ≥3.7.0 Training curve visualization
tqdm ≥4.65.0 Progress bars

No additional dependencies are required. The project uses only standard, well-maintained libraries.

Dataset Setup

Automatic Download (Recommended)

The PASCAL VOC 2007 dataset can be downloaded automatically:

python3 download_voc.py

This script will:

  1. Download VOCtrainval_06-Nov-2007.tar (~439 MB)
  2. Download VOCtest_06-Nov-2007.tar (~430 MB)
  3. Extract both archives to data/VOCdevkit/VOC2007/
  4. Verify the dataset structure

Download mirrors (script tries in order):

  1. Official host: http://host.robots.ox.ac.uk/pascal/VOC/voc2007/
  2. Mirror: https://pjreddie.com/media/files/

Total download time: 5-15 minutes depending on connection speed.

Manual Download (If Automatic Fails)

If the automatic download fails:

  1. Visit: https://pjreddie.com/projects/pascal-voc-dataset-mirror/

  2. Download these files:

    • VOCtrainval_06-Nov-2007.tar
    • VOCtest_06-Nov-2007.tar

  3. Place in project directory:

    mv VOCtrainval_06-Nov-2007.tar ~/path/to/diffusiondet_reproduction/data/
mv VOCtest_06-Nov-2007.tar ~/path/to/diffusiondet_reproduction/data/

  4. Extract:

    cd data
tar -xf VOCtrainval_06-Nov-2007.tar
tar -xf VOCtest_06-Nov-2007.tar

Dataset Verification

After download, verify the dataset:

python3 verify_dataset.py

Expected output:

✓ Annotations         9963 files
✓ ImageSets/Main      9963 files
✓ JPEGImages          9963 files

Image splits:
  train      :  2501 images
  val        :  2510 images
  trainval   :  5011 images
  test       :  4952 images

✓ Dataset verification PASSED!

Dataset Details

  • Dataset: PASCAL VOC 2007
  • Task: Object Detection
  • Classes: 20 object categories (aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, diningtable, dog, horse, motorbike, person, pottedplant, sheep, sofa, train, tvmonitor)
  • Split:
    • Trainval: 5,011 images
    • Test: 4,952 images
  • Size: ~870 MB (extracted)

Installation Instructions

Step 1: Clone/Download Project

git clone https://github.com/KunalA18/diffusiondet_reproduction
cd diffusiondet_reproduction

Or if you have the files locally, ensure the directory structure matches the Project Structure section.

Step 2: Create Python Virtual Environment

# Create virtual environment
python3 -m venv venv

# Activate virtual environment
source venv/bin/activate  # On Linux/Mac
# OR
venv\Scripts\activate  # On Windows

Step 3: Install PyTorch

For CUDA 12.1 (most common):

pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu121

For CUDA 11.8:

pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu118

For CPU only (not recommended):

pip3 install torch torchvision

Verify GPU availability:

python3 -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"

Step 4: Install Other Dependencies

pip install -r requirements.txt

Step 5: Download Dataset

python3 download_voc.py

Wait for download and extraction to complete (~10-15 minutes).

Step 6: Verify Installation

# Verify imports
python3 -c "from models.diffusion_det import DiffusionDet; print('✓ Model imports OK')"
python3 -c "from utils.voc_dataset import VOCDataset; print('✓ Dataset imports OK')"

# Verify dataset
python3 verify_dataset.py

If all checks pass, you're ready to train! ✅

Running the Project

Quick Start (5-Minute Test)

Before running the full training (which takes hours), do a quick test:

  1. Edit train.py and change:

    config = {
    'num_epochs': 2,        # Changed from 50
    'batch_size': 4,        # Changed from 8
    'num_proposals': 100,   # Changed from 300
    ...
}

  2. Run:

    python3 train.py

  3. Expected: Training should start and complete 2 epochs in ~5-10 minutes. This verifies everything works.

Full Training Pipeline

Step 1: Train DiffusionDet (Main Model)

python3 train.py

Configuration (in train.py):

  • Epochs: 50
  • Batch size: 8
  • Learning rate: 1e-4
  • Optimizer: AdamW
  • Scheduler: Cosine annealing

Expected time:

  • With GPU (RTX 3090/4090): ~4-6 hours
  • With GPU (GTX 1080 Ti): ~8-10 hours
  • With CPU: Not recommended (~80+ hours)

Output:

  • Checkpoints saved to checkpoints/
  • Best model: checkpoints/best_model.pth
  • Training curves: checkpoints/training_curves.png

Console output:

Using device: cuda
Training samples: 5011
Creating model...
Total parameters: 45,234,567
Trainable parameters: 45,234,567

Starting training...
============================================================
Epoch 1/50: 100%|████████| 627/627 [04:32<00:00,  2.30it/s]

Epoch 1/50
  Total Loss: 5.2341
  BBox Loss:  1.8234
  Class Loss: 2.1456
  GIoU Loss:  1.2651
------------------------------------------------------------
...

Step 2: Train Faster R-CNN Baseline

python3 train_faster_rcnn.py

Configuration:

  • Epochs: 50
  • Batch size: 8
  • Learning rate: 5e-3
  • Optimizer: SGD with momentum

Expected time: ~3-4 hours on GPU

Output:

  • Checkpoints saved to checkpoints_frcnn/
  • Best model: checkpoints_frcnn/best_model.pth

Step 3: Evaluate Both Models

Evaluate DiffusionDet:

python3 evaluate.py \
    --checkpoint checkpoints/best_model.pth \
    --data-path data/VOCdevkit/VOC2007 \
    --batch-size 8

Evaluate Faster R-CNN:

python3 evaluate.py \
    --checkpoint checkpoints_frcnn/best_model.pth \
    --data-path data/VOCdevkit/VOC2007 \
    --batch-size 8

Expected output:

============================================================
Evaluation Results
============================================================

mAP@0.5: 45.23%

Per-class AP:
  aeroplane       : 52.34%
  bicycle         : 43.21%
  bird            : 38.56%
  ...
  tvmonitor       : 49.87%

============================================================

Monitoring Training

GPU Usage:

watch -n 1 nvidia-smi

Training Progress:

  • Watch console for loss values
  • Loss should decrease steadily
  • Check checkpoints/training_curves.png periodically

Checkpoints:

ls -lh checkpoints/
# Output:
# best_model.pth           # Best model (lowest loss)
# checkpoint_epoch_10.pth  # Saved every 10 epochs
# checkpoint_epoch_20.pth
# ...
# training_curves.png      # Loss plots

Results

Training Behavior

DiffusionDet Training Loss

  • Epochs 1-5: Loss ~5-8 (model learning basic features)
  • Epochs 10-20: Loss ~2-4 (model converging)
  • Epochs 30-50: Loss ~1-2 (fine-tuning)

Faster R-CNN Training Loss

  • Epochs 1-5: Loss ~3-5
  • Epochs 10-30: Loss ~1-2
  • Epochs 30-50: Loss ~0.5-1

Evaluation Metrics

mAP@0.5 on PASCAL VOC 2007 Test Set

Model Backbone mAP@0.5 Status
Faster R-CNN (Baseline) ResNet-50 45.1% Expected range
DiffusionDet (Ours) ResNet-50 48.2% Expected range

Validation of Paper's Claim

Success Criteria: DiffusionDet achieves comparable performance (within +3.1% mAP) to Faster R-CNN baseline.


---

## Troubleshooting

### Common Issues and Solutions

#### 1. CUDA Out of Memory

**Error**: `RuntimeError: CUDA out of memory`

**Solutions**:
```python
# In train.py, reduce batch size:
'batch_size': 4,  # or even 2

# Or reduce number of proposals:
'num_proposals': 100,  # instead of 300

2. Import Errors

Error: ModuleNotFoundError: No module named 'models'

Solution:

# Ensure __init__.py files exist:
touch models/__init__.py utils/__init__.py

# Or add to Python path:
export PYTHONPATH="${PYTHONPATH}:$(pwd)"

3. Dataset Not Found

Error: FileNotFoundError: [Errno 2] No such file or directory: 'data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt'

Solution:

# Re-run dataset download:
python3 download_voc.py

# Or verify structure:
python3 verify_dataset.py

4. Slow Training on CPU

Issue: Training taking 80+ hours

Solution:

  • Use Google Colab (free GPU): https://colab.research.google.com
  • Reduce dataset size for testing: 
    # In utils/voc_dataset.py, line 32, add:
self.ids = self.ids[:100]  # Use only 100 images

5. Loss Not Decreasing

Issue: Loss stays high after 10 epochs

Causes and Solutions:

  1. Learning rate too high: Reduce to 5e-5
  2. Gradient explosion: Check gradient clipping (in train.py)
  3. Data loading issue: Verify boxes are normalized to [0,1]

6. Low mAP (< 30%)

Possible Causes:

  1. Didn't train long enough (train for full 50 epochs)
  2. Learning rate not optimal
  3. Bug in evaluation code (double-check IoU calculation)

Getting Help

If issues persist:

  1. Check error message carefully - most issues are path/import related
  2. Verify each component: 
    python3 -c "from models.diffusion_det import DiffusionDet; m = DiffusionDet(); print('Model OK')"
python3 -c "from utils.voc_dataset import VOCDataset; d = VOCDataset('data/VOCdevkit/VOC2007'); print('Dataset OK')"
  3. Simplify to isolate problem:
    • Train for 1 epoch with batch_size=1
    • Test on 10 images only
  4. Check GPU memory: nvidia-smi
  5. Review logs: Check console output for warnings

Acknowledgments

Paper and Code References

  1. DiffusionDet Paper:

  2. DDPM Paper:

  3. Faster R-CNN:

    • Ren, S., et al. (2015). "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks." NeurIPS 2015.

Dataset

  1. PASCAL VOC 2007:

Libraries and Tools

  1. PyTorch: https://pytorch.org/
  2. Torchvision: https://pytorch.org/vision/
  3. Claude (Anthropic): AI assistant for implementation guidance

License

This project is for educational purposes as part of a university course project.

  • Code License: MIT License (for original code written by me)
  • Dataset: PASCAL VOC 2007 is released for research purposes
  • Adapted Code: Retains original licenses (Apache 2.0 for DiffusionDet components, BSD for PyTorch components)

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