Welcome to DeepLib

DeepLib is a unified PyTorch library for computer vision tasks, focusing on object detection, semantic segmentation, and anomaly detection.

Installation

Prerequisites

DeepLib requires PyTorch and torchvision to be installed first. For optimal performance, CUDA 11.8 or above is recommended:

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

For other installation options (CPU-only, different CUDA versions), see the PyTorch installation guide.

Installing DeepLib

Full Installation (Recommended):

pip install deeplib

Core Installation (without logging backends):

pip install deeplib[core]

Features

Semantic Segmentation Models (✅ Implemented)
- UNet
- DeepLabV3
- DeepLabV3+
Experiment Tracking (✅ Implemented)
- TensorBoard
- MLflow
- Weights & Biases (W&B)

Contents

Documentation:

Quick Start

Here’s a simple example of training a segmentation model:

from deeplib.models.segmentation import UNet
from deeplib.trainers import SegmentationTrainer
from deeplib.datasets import SegmentationDataset
from deeplib.loggers import WandbLogger
from torch.utils.data import DataLoader

# Initialize model
model = UNet(num_classes=4)

# Prepare dataset
train_dataset = SegmentationDataset(
    root="path/to/data",
    images_dir="images",
    masks_dir="masks",
    num_classes=4,
    split="train"
)

# Create dataloader
train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)

# Configure loss function
model.configure_loss('dice', {'ignore_index': 255})

# Initialize logger
logger = WandbLogger(
    experiment_name="segmentation_experiment",
    project="deeplib-segmentation"
)

# Train model
trainer = SegmentationTrainer(
    model=model,
    train_loader=train_loader,
    monitor_metric='iou',
    logger=logger
)

trainer.train(num_epochs=100)

For more examples and detailed usage, check the Examples section.