Sharath33/WasteManagement

TSAP — Trash/Sustainability Assessment Platform

A deep learning computer vision system for automatic detection and classification of waste bins in images. The system localizes bins using YOLOv4-tiny and classifies their properties using a multi-task ResNet34 model.

Overview

TSAP runs two ML pipelines:

Pipeline	Architecture	Input Size	Output
Detection	YOLOv4-tiny	608×608	Bounding boxes
Classification	ResNet34 (multi-task)	200×200	Fullness + Shape

Classification labels:

• Fullness (5 classes): Closed, Empty, Half, Full, Overflow, Open
• Shape (8 classes): Star, Circle, Arrow, Centric, Triangle, Square, Chevron, Lightning Bolt

Directory Structure

tsap/
├── classification/
│   ├── model.py                        # TSAPMultiClassification (ResNet34 dual-head)
│   ├── dataset.py                      # Dataset loader with Albumentations augmentation
│   └── train.py                        # Training engine (mixed precision, wandb)
├── detection/
│   ├── config.py                       # YOLOv4-tiny hyperparameters and anchors
│   ├── dataset.py                      # YOLO dataset loader with mosaic augmentation
│   └── train_yolo.py                   # YOLO training engine with COCO evaluation
├── common/
│   ├── loss.py                         # FocalLoss, Yolo_loss, Yolo_loss_general
│   └── meters.py                       # AverageMeter, ProgressMeter
├── utils/
│   ├── utils.py                        # Train/test splits, confusion matrices, visualization
│   ├── detection_utils.py              # IoU / GIoU / DIoU / CIoU calculations
│   ├── annot_classification_map.py     # Image-to-label mapping
│   ├── annot_darknet2torch.py          # Darknet annotation format converter
│   └── cvat_annotation_converter.py   # CVAT XML → YOLO format converter
├── models/
│   ├── classification_model/           # Trained classification weights + ONNX
│   ├── detection_model/                # YOLOv4-tiny weights + ONNX
│   └── training/                       # Standalone training scripts for individual heads
├── main.py                             # Usage examples
├── test.py                             # Inference script
└── environment.yml                     # Conda environment

Setup

conda env create -f environment.yml
conda activate <env-name>

Key dependencies: PyTorch 1.7.1 (CUDA 10.2), TorchVision 0.8.2, OpenCV 3.4.2, Albumentations 0.5.2, ONNX 1.8.0 + ONNXRuntime 1.5.2, Weights & Biases.

Data Preparation

Annotations are created in CVAT and exported as XML. The converter handles bounding box extraction and label generation:

python utils/cvat_annotation_converter.py

This converts CVAT XML annotations to YOLO format and crops classification samples with their labels.

Training

Classification

from classification.train import Engine
from classification.model import TSAPMultiClassification

model = TSAPMultiClassification(pretrained=True)
engine = Engine(model, train_loader, val_loader, device_ids=[0,1])
engine.train(epochs=200)

Hyperparameters: batch size 128, SGD with momentum 0.9, OneCycleLR (lr=3e-3), image size 200×200.

Detection

from detection.train_yolo import Engine_YOLO

engine = Engine_YOLO(cfg_path, weights_path, train_loader, val_loader)
engine.train(max_batches=30000)

Hyperparameters: batch size 64 (16 subdivisions), learning rate 0.001 with burn-in, image size 608×608.

Inference

python test.py

The inference pipeline:

1. Resize to 200×200 (classification) or 608×608 (detection)
2. Normalize using dataset mean/std
3. Run forward pass
4. Apply sigmoid + argmax; filter predictions below threshold 0.3

Models

Pre-trained weights are in models/:

File	Description
classification_model/tsap_bin_classifer.pt	Multi-task fullness + shape classifier
classification_model/shape_classifier_resnet34.pt	Shape-only classifier
classification_model/TSAP_classifier_dynamic.onnx	ONNX (dynamic batch)
detection_model/tsap-detection.weights	Darknet format detection weights
detection_model/tsap-detection.cfg	Darknet config
detection_model/TSAP_detection_dynamic.onnx	ONNX (dynamic batch)

Architecture

Classification — TSAPMultiClassification

Input (200×200)
    └── ResNet34 backbone (shared)
         ├── Conv head → Linear(128) → Fullness logits (5)
         └── Conv head → Linear(128) → Shape logits (8)

Training uses BCEWithLogitsLoss for both heads simultaneously.

Detection — YOLOv4-tiny

• Input: 608×608, 6 object classes
• 2 detection scales (32× and 16× stride), 3 anchors each
• Losses: XY, WH, objectness, class (IoU-based anchor assignment)

Experiment Tracking

Training integrates with Weights & Biases for metric logging and visualization. Set your API key before training:

wandb login