diff --git a/ CONTRIBUTING.md b/ CONTRIBUTING.md
new file mode 100644
index 0000000..c61f930
--- /dev/null
+++ b/ CONTRIBUTING.md	
@@ -0,0 +1,191 @@
+# Contributing to Viyog
+
+Thank you for your interest in contributing to **Viyog**. Contributions of all kinds are welcome, including:
+
+* New scoring variants or robustness improvements
+* Additional evaluation metrics
+* Bug fixes
+* Performance optimizations
+* Documentation enhancements
+* Unit tests and CI improvements
+
+For significant architectural or API changes, please open an issue first to discuss your proposal before submitting a pull request.
+
+---
+
+## Development Setup
+
+Viyog uses **uv** for dependency management and environment reproducibility.
+
+### 1. Install `uv`
+
+If not already installed:
+
+```bash
+pip install uv
+```
+
+Recommended installation:
+
+```bash
+curl -Ls https://astral.sh/uv/install.sh | sh
+```
+
+---
+
+### 2. Clone the Repository
+
+```bash
+git clone <your-fork-url>
+cd viyog_repo
+```
+
+---
+
+### 3. Create and Sync the Environment
+
+```bash
+uv sync
+```
+
+This will:
+
+* Create a virtual environment
+* Install all project dependencies
+* Respect the `uv.lock` file for reproducible installs
+
+To activate the environment:
+
+```bash
+source .venv/bin/activate
+```
+
+---
+
+## Development Workflow
+
+1. Fork the repository
+
+2. Sync your fork with the latest `main` branch
+
+3. Create a feature branch:
+
+   ```bash
+   git checkout -b feature/my-change
+   ```
+
+4. Make your changes
+
+5. Commit with clear, descriptive messages
+
+6. Push to your fork
+
+7. Open a Pull Request (PR)
+
+**Do not open pull requests directly from `main`.**
+
+---
+
+## Code Style & Linting
+
+This project uses:
+
+* **Ruff** for linting
+* **pre-commit** for automated formatting and checks
+
+Configuration is stored in:
+
+```
+.pre-commit-config.yaml
+```
+
+### Install Pre-commit Hooks
+
+```bash
+uv run pre-commit install
+```
+
+Hooks will run automatically on every commit.
+
+To run manually:
+
+```bash
+uv run pre-commit run --all-files
+```
+
+All linting and formatting checks must pass before merging.
+
+---
+
+## Testing
+
+Run tests with:
+
+```bash
+uv run pytest
+```
+
+If you add or modify functionality:
+
+* Include unit tests
+* Ensure all tests pass
+* Verify tensor shapes and device handling
+* Avoid introducing gradients (Viyog is inference-only)
+* Ensure CPU and CUDA behavior remain consistent (if applicable)
+
+Pull requests without appropriate test coverage may be requested to add tests before review.
+
+---
+
+## Pull Request Guidelines
+
+Before submitting a PR, ensure:
+
+* Code passes linting and formatting checks
+* All tests pass
+* New functionality includes tests
+* Public API changes are documented
+* The README is updated if behavior changes
+
+PR descriptions should clearly explain:
+
+* What was changed
+* Why the change was made
+* Any trade-offs or limitations
+
+---
+
+## Reporting Issues
+
+When opening an issue, please include:
+
+* Python version
+* PyTorch version
+* Operating system
+* Minimal reproducible example
+* Expected vs. actual behavior
+* Full error traceback (if applicable)
+
+Incomplete reports may delay resolution.
+
+---
+
+## Design & API Stability
+
+* Keep changes focused and minimal
+* Write clear docstrings for new functionality
+* Avoid breaking existing public APIs without prior discussion
+* Maintain inference-only guarantees
+* Preserve backward compatibility whenever possible
+
+---
+
+## Security
+
+If you discover a security vulnerability, do **not** open a public issue. Instead, contact the maintainers privately with detailed information.
+
+---
+
+## License
+
+By contributing to Viyog, you agree that your contributions will be licensed under the *MIT** used by this project.
diff --git a/.copier-answers.yml b/.copier-answers.yml
new file mode 100644
index 0000000..09316c8
--- /dev/null
+++ b/.copier-answers.yml
@@ -0,0 +1,9 @@
+# Changes here will be overwritten by Copier; NEVER EDIT MANUALLY
+_commit: v1.1.0
+_src_path: gh:mjun0812/python-copier-template
+author_email: singh052001aman@gmail.com
+author_name: Aman Singh
+description: A simple tool to seperate Out of distribution and Adversarial Attack
+package_name: viyog
+project_name: viyog
+python_version: '3.10'
diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
new file mode 100644
index 0000000..1494755
--- /dev/null
+++ b/.devcontainer/devcontainer.json
@@ -0,0 +1,62 @@
+{
+	"name": "python-devcontainer",
+	"image": "mcr.microsoft.com/devcontainers/base:ubuntu",
+	"containerEnv": {
+		"DISPLAY": "${localEnv:DISPLAY}",
+		"PYTHONUNBUFFERED": "1",
+		"PYTHONDONTWRITEBYTECODE": "1",
+		"UV_CACHE_DIR": "/home/vscode/.cache/uv",
+		"UV_LINK_MODE": "copy",
+		"UV_PROJECT_ENVIRONMENT": "/home/vscode/.venv",
+		"UV_COMPILE_BYTECODE": "1",
+		"CLAUDE_CONFIG_DIR": "/home/vscode/.claude"
+	},
+	"features": {
+		"ghcr.io/devcontainers/features/github-cli:1": {},
+		"ghcr.io/devcontainers/features/common-utils:2": {
+			"configureZshAsDefaultShell": true
+		},
+		"ghcr.io/rocker-org/devcontainer-features/apt-packages:1": {
+			"packages": "curl,wget,git,jq,ca-certificates,build-essential,ripgrep,fd-find"
+		},
+		"ghcr.io/va-h/devcontainers-features/uv:1": {
+			"shellAutocompletion": true
+		},
+		"ghcr.io/devcontainers/features/node:1": {},
+		"ghcr.io/anthropics/devcontainer-features/claude-code:1.0": {}
+	},
+	"runArgs": [
+		"--init",
+		"--rm"
+	],
+	"hostRequirements": {
+		"gpu": "optional"
+	},
+	"customizations": {
+		"vscode": {
+			"settings": {
+				"python.defaultInterpreterPath": "/home/vscode/.venv/bin/python"
+			},
+			"extensions": [
+				"ms-python.python",
+				"charliermarsh.ruff",
+				"eamodio.gitlens",
+				"tamasfe.even-better-toml",
+				"ms-toolsai.jupyter",
+				"yzhang.markdown-all-in-one"
+			]
+		}
+	},
+	"remoteUser": "vscode",
+	"containerUser": "vscode",
+	"initializeCommand": "mkdir -p ${localEnv:HOME}/.claude ${localEnv:HOME}/.codex && touch ${localEnv:HOME}/.claude/CLAUDE.md ${localEnv:HOME}/.codex/config.toml",
+	"mounts": [
+		"source=shell_history-${devcontainerId},target=/shell_history,type=volume",
+		"source=${localEnv:HOME}/.claude,target=/home/vscode/.claude,type=bind",
+		"source=${localEnv:HOME}/.claude/CLAUDE.md,target=/home/vscode/.claude/CLAUDE.md,type=bind",
+		"source=${localEnv:HOME}/.codex,target=/home/vscode/.codex,type=bind",
+		"source=${localEnv:HOME}/.codex/config.toml,target=/home/vscode/.codex/config.toml,type=bind"
+	],
+	"postCreateCommand": "npm install -g @openai/codex@latest && uv sync",
+	"postStartCommand": "uv run pre-commit install"
+}
\ No newline at end of file
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 0000000..2788e13
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,23 @@
+name: CI
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+
+jobs:
+  ci:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+
+      - name: Format
+        run: uvx ruff format . --check --diff
+
+      - name: Lint
+        run: uvx ruff check --output-format=github .
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..3a6c197
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,290 @@
+# Created by https://www.toptal.com/developers/gitignore/api/linux,macos,visualstudiocode,vim,python,jupyternotebooks,venv,virtualenv
+# Edit at https://www.toptal.com/developers/gitignore?templates=linux,macos,visualstudiocode,vim,python,jupyternotebooks,venv,virtualenv
+
+### JupyterNotebooks ###
+# gitignore template for Jupyter Notebooks
+# website: http://jupyter.org/
+
+.ipynb_checkpoints
+*/.ipynb_checkpoints/*
+
+# IPython
+profile_default/
+ipython_config.py
+
+# Remove previous ipynb_checkpoints
+#   git rm -r .ipynb_checkpoints/
+
+### Linux ###
+*~
+
+# temporary files which can be created if a process still has a handle open of a deleted file
+.fuse_hidden*
+
+# KDE directory preferences
+.directory
+
+# Linux trash folder which might appear on any partition or disk
+.Trash-*
+
+# .nfs files are created when an open file is removed but is still being accessed
+.nfs*
+
+### macOS ###
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+### macOS Patch ###
+# iCloud generated files
+*.icloud
+
+### Python ###
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+
+# IPython
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+### Python Patch ###
+# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
+poetry.toml
+
+# ruff
+.ruff_cache/
+
+# LSP config files
+pyrightconfig.json
+
+### venv ###
+# Virtualenv
+# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/
+[Bb]in
+[Ii]nclude
+[Ll]ib
+[Ll]ib64
+[Ll]ocal
+[Ss]cripts
+pyvenv.cfg
+pip-selfcheck.json
+
+### Vim ###
+# Swap
+[._]*.s[a-v][a-z]
+!*.svg  # comment out if you don't need vector files
+[._]*.sw[a-p]
+[._]s[a-rt-v][a-z]
+[._]ss[a-gi-z]
+[._]sw[a-p]
+
+# Session
+Session.vim
+Sessionx.vim
+
+# Temporary
+.netrwhist
+# Auto-generated tag files
+tags
+# Persistent undo
+[._]*.un~
+
+### VirtualEnv ###
+# Virtualenv
+# http://iamzed.com/2009/05/07/a-primer-on-virtualenv/
+
+### VisualStudioCode ###
+.vscode/*
+!.vscode/settings.json
+!.vscode/tasks.json
+!.vscode/launch.json
+!.vscode/extensions.json
+!.vscode/*.code-snippets
+
+# Local History for Visual Studio Code
+.history/
+
+# Built Visual Studio Code Extensions
+*.vsix
+
+### VisualStudioCode Patch ###
+# Ignore all local history of files
+.history
+.ionide
+
+# End of https://www.toptal.com/developers/gitignore/api/linux,macos,visualstudiocode,vim,python,jupyternotebooks,venv,virtualenv
\ No newline at end of file
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..e67372f
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,15 @@
+repos:
+  - repo: https://github.com/astral-sh/uv-pre-commit
+    # uv version.
+    rev: 0.8.22
+    hooks:
+      - id: uv-lock
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    # Ruff version.
+    rev: v0.13.2
+    hooks:
+      # Run the linter.
+      - id: ruff-check
+        args: [--fix]
+      # Run the formatter.
+      - id: ruff-format
diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..7c7a975
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.10
\ No newline at end of file
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..3b66410
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,3 @@
+{
+    "git.ignoreLimitWarning": true
+}
\ No newline at end of file
diff --git a/AGENTS.md b/AGENTS.md
new file mode 100644
index 0000000..7e059fa
--- /dev/null
+++ b/AGENTS.md
@@ -0,0 +1,37 @@
+# Development Guidelines
+
+This document contains critical information about working with this codebase.
+Follow these guidelines precisely.
+
+## Rules
+
+1. Package Management
+   - ONLY use uv, NEVER pip
+   - Installation: `uv add package`
+   - Upgrading: `uv add --dev package --upgrade-package package`
+   - FORBIDDEN: `uv pip install`, `@latest` syntax
+
+2. Code Quality
+   - Type hints required for all code
+   - Follow existing patterns exactly
+   - Use Google style for docstring
+
+3. Testing Requirements
+   - Framework: `uv run --frozen pytest`
+   - Coverage: test edge cases and errors
+   - New features require tests
+   - Bug fixes require regression tests
+
+4. Git
+   - Follow the Conventional Commits style on commit messages.
+
+## Code Formatting and Linting
+
+1. Ruff
+   - Format: `uv run --frozen ruff format .`
+   - Check: `uv run --frozen ruff check .`
+   - Fix: `uv run --frozen ruff check . --fix`
+2. Pre-commit
+   - Config: `.pre-commit-config.yaml`
+   - Runs: on git commit
+   - Tools: Ruff (Python)
diff --git a/CLAUDE.md b/CLAUDE.md
new file mode 100644
index 0000000..7e059fa
--- /dev/null
+++ b/CLAUDE.md
@@ -0,0 +1,37 @@
+# Development Guidelines
+
+This document contains critical information about working with this codebase.
+Follow these guidelines precisely.
+
+## Rules
+
+1. Package Management
+   - ONLY use uv, NEVER pip
+   - Installation: `uv add package`
+   - Upgrading: `uv add --dev package --upgrade-package package`
+   - FORBIDDEN: `uv pip install`, `@latest` syntax
+
+2. Code Quality
+   - Type hints required for all code
+   - Follow existing patterns exactly
+   - Use Google style for docstring
+
+3. Testing Requirements
+   - Framework: `uv run --frozen pytest`
+   - Coverage: test edge cases and errors
+   - New features require tests
+   - Bug fixes require regression tests
+
+4. Git
+   - Follow the Conventional Commits style on commit messages.
+
+## Code Formatting and Linting
+
+1. Ruff
+   - Format: `uv run --frozen ruff format .`
+   - Check: `uv run --frozen ruff check .`
+   - Fix: `uv run --frozen ruff check . --fix`
+2. Pre-commit
+   - Config: `.pre-commit-config.yaml`
+   - Runs: on git commit
+   - Tools: Ruff (Python)
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..e4732c9
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,6 @@
+MIT License
+
+Copyright (c) 2026 Aman
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+...
diff --git a/README.md b/README.md
index 2bb87ea..4385449 100644
--- a/README.md
+++ b/README.md
@@ -1,305 +1,351 @@
-## Viyog
-
-**Viyog** is a lightweight **post-hoc technique** that improves the **reliability of machine learning systems** by enhancing **adversarial robustness**, *without requiring adversarial training*.
-
-### How Viyog Fits In
-
-- **Does not perform OOD or adversarial detection on its own**
-- Operates **after** samples have been flagged by a joint detector  
-  (e.g., **Mahalanobis-based methods**)
-- Helps **separate non–in-distribution (non-ID)** samples  
-  such as **OOD** and **adversarial inputs**
-- Designed to work seamlessly with **pre-trained models**
-
-Once OOD and adversarial samples are identified by an external detector,  
-**Viyog provides an additional signal that helps distinguish and separate them**,  
-improving downstream decision-making and overall system robustness.
-
-
-## Table of contents
-
-- [Key ideas](#key-ideas)
-- [Features](#features)
-- [Repository structure](#repository-structure)
-- [Requirements](#requirements)
-- [Installation](#installation)
-- [Quick start](#quick-start)
-- [Context manager usage](#context-manager-usage)
-- [Scoring function](#scoring-function)
-- [OOD metrics](#ood-metrics)
-- [Design notes](#design-notes)
-- [Common pitfalls](#common-pitfalls)
-- [Testing suggestions](#testing-suggestions)
-- [Contributing](#contributing)
-- [License](#license)
-- [Citation](#citation)
+Below is a revised and corrected version reflecting the key clarification:
+
+* **Primary separation: OOD vs ADV**
+* Not framed as ID vs OOD
+* Testing section removed
+* Language tightened accordingly
+
+You can replace your README with this version.
 
 ---
 
-## Key ideas
+# Viyog
 
-- Early convolutional layers encode low-level structure.
-- ADV samples often produce low activation magnitudes.
-- Measuring and centering per-sample activation norms provides a simple, model-agnostic ADV vs OOD signal.
+<p align="center">
+  <img src="Viyog.png" width="360" alt="Viyog logo"/>
+</p>
 
----
+**Viyog** is a lightweight, post-hoc reliability signal for convolutional neural networks (CNNs).
+
+It extracts a simple activation-norm statistic from the **first convolutional layer** and produces a bounded score designed to help separate:
+
+* **Out-of-Distribution (OOD)** samples
+* **Adversarial (ADV)** samples
+
+Viyog is:
 
-## Features
-
-- Automatically finds and hooks the **first convolutional layer**
-  - Prefers `conv1` if present
-  - Falls back to first `Conv1d / Conv2d / Conv3d`
-- Captures activations via forward hooks (no gradients)
-- Computes per-sample **infinity norm** of flattened activations of Training data
-- Training-time baseline via `fit()`
-- Scores batches or full DataLoaders
-- Temperature-scaled, bounded scoring function
-- Safe hook cleanup (manual or context manager)
-- Standard OOD metrics:
-  - AUROC
-  - AUPR (IN / OUT)
-  - FPR\@95% TPR
-  - Detection Error
-  - AUTC
+* Model-agnostic
+* Training-free
+* Gradient-free
+* Designed for post-hoc reliability analysis
+
+It requires only forward hooks and does not modify model parameters.
 
 ---
 
-## Repository structure
+# Purpose
 
-```
+OOD and adversarial samples often trigger similar alarms in primary detectors, yet arise from fundamentally different mechanisms:
 
-V2/viyog_repo/
-├── README.md
-└── src/
-└── main.py
+* **OOD**: distributional shift
+* **ADV**: structured perturbation crafted to manipulate decision boundaries
 
-```
+Viyog provides a lightweight secondary signal that helps distinguish between these two regimes using early-layer activation behavior.
 
-All implementation lives in `src/main.py`.
+It is intended to be used after a primary detection stage has identified suspicious inputs.
 
 ---
 
-## Requirements
+# Core Idea
 
-- Python 3.8+
-- PyTorch
-- NumPy
-- scikit-learn
+Viyog measures the **infinity norm (max absolute activation)** of the first convolutional layer.
 
----
+### Intuition
 
-## Installation
+* The first convolutional layer captures low-level structural information.
+* Adversarial perturbations and distributional shifts influence activation magnitudes differently.
+* The maximum absolute activation provides a stable and architecture-independent scalar signal.
 
-Install dependencies:
+### Scoring Pipeline
 
-```bash
-pip install torch numpy scikit-learn
-```
+1. Attach a forward hook to the first convolutional layer.
 
-Make sure the `src/` directory is importable:
+2. Capture per-sample activations.
 
-```bash
-export PYTHONPATH="${PWD}/src:${PYTHONPATH}"
-```
+3. Flatten activations per sample.
+
+4. Compute the **infinity norm**:
+
+   [
+   |x|_\infty = \max |x_i|
+   ]
+
+5. Compute a training baseline mean via `fit()`.
+
+6. Center new norms by this baseline.
+
+7. Apply temperature-scaled bounded nonlinearity.
+
+Final score range: approximately **(-1, 1)**.
+
+Interpretation (recommended usage):
 
-(Alternatively, install the repo in editable mode if packaging files are added.)
+* Higher score → more likely **OOD**
+* Lower score → more likely **ADV**
 
 ---
 
-## Quick start
+# Repository Structure
 
-### 1. Create the wrapper
+```
+<repo-root>/
+├── .devcontainer/
+├── .github/
+├── .venv/
+├── .vscode/
+├── docker/
+├── docs/
+├── src/
+│   └── viyog/
+│       ├── __init__.py
+│       └── main.py
+├── tests/
+├── pyproject.toml
+├── uv.lock
+├── README.md
+└── Viyog.png
+```
+
+### Public API
+
+`src/viyog/__init__.py`
 
 ```python
-from main import Viyog
+from .main import Viyog, viyog_metrics
 
-v = Viyog(model)
+__all__ = ["Viyog", "viyog_metrics"]
 ```
 
-Optionally specify a device:
+All implementation resides in:
 
-```python
-v = Viyog(model, device="cuda:0")
+```
+src/viyog/main.py
 ```
 
 ---
 
-### 2. Fit the training baseline
+# Installation
 
-```python
-v.fit(trainloader)
+The project uses `pyproject.toml` and `uv`.
+
+## 1. Install uv
+
+```bash
+pip install uv
 ```
 
-This computes and stores the **mean per-sample infinity norm** of the first-layer activations.
+## 2. Clone repository
 
-> `fit()` **must** be called before `score()`.
+```bash
+git clone <repo-url>
+cd <repo-dir>
+```
 
----
+## 3. Create virtual environment
+
+```bash
+uv venv
+```
 
-### 3. Score data
+## 4. Activate environment
 
-Score a batch:
+macOS / Linux:
 
-```python
-scores = v.score(batch)
+```bash
+source .venv/bin/activate
 ```
 
-Score a full DataLoader:
+Windows (PowerShell):
 
-```python
-scores = v.score(testloader)
+```powershell
+.venv\Scripts\Activate.ps1
+```
+
+## 5. Install package (editable mode)
+
+```bash
+uv pip install -e .
 ```
 
-Each call returns a **1D tensor of per-sample scores**.
+## 6. Run tests
 
-Higher scores indicate stronger OOD likelihood.
+```bash
+pytest
+```
 
 ---
 
-### 4. Cleanup
+# Quick Usage
 
 ```python
-v.close()
+from viyog import Viyog, viyog_metrics
+
+v = Viyog(model, device="cuda:0")  # or "cpu"
+
+# Step 1: Fit baseline (required)
+v.fit(train_loader)
+
+# Step 2: Score new samples
+scores = v.score(test_loader)  # torch.Tensor [N]
 ```
 
----
+Higher score → more likely OOD
+Lower score → more likely ADV
 
-## Context manager usage
+---
 
-The wrapper supports safe automatic cleanup:
+# Context Manager (Recommended)
 
 ```python
-from main import Viyog, viyog_metrics
-
 with Viyog(model) as v:
-    v.fit(trainloader)
-
-    id_scores = v.score(id_loader)
+    v.fit(train_loader)
     ood_scores = v.score(ood_loader)
+    adv_scores = v.score(adv_loader)
 
 metrics = viyog_metrics(
-    id_scores.cpu().numpy(),
-    ood_scores.cpu().numpy()
+    ood_scores.cpu().numpy(),
+    adv_scores.cpu().numpy()
 )
-
 print(metrics)
 ```
 
+Hooks are automatically removed on exit.
+
 ---
 
-## Scoring function
+# API Reference
 
-The scoring pipeline is:
+## `Viyog(model, device=None)`
 
-1. Capture first-layer activations
-2. Flatten per sample
-3. Compute infinity norm
-4. Center by training mean
-5. Apply temperature-scaled nonlinearity
+Initializes hook and prepares scoring mechanism.
 
-```python
-Viyog.Viyog_Score(centered_norms, Temperature=1000.0)
-```
+**Raises:**
 
-Scores are approximately bounded in `(-1, 1)`.
+* `RuntimeError` if no convolutional layer is found.
 
 ---
 
-## OOD metrics
+## `fit(train_loader)`
+
+Computes training baseline mean of infinity norms.
+
+Requirements:
+
+* Must be called before `score()`
+* Uses `torch.no_grad()`
+* Sets `model.eval()`
+* Accumulates in CPU float64 for stability
+
+---
 
-The helper function:
+## `score(data_loader_or_batch)`
+
+Returns:
 
 ```python
-from main import viyog_metrics
-metrics = viyog_metrics(id_scores, ood_scores)
+torch.Tensor  # shape [num_samples]
 ```
 
-Returns:
+Raises:
 
-- `AUROC`
-- `AUPR_IN`
-- `AUPR_OUT`
-- `FPR95`
-- `DetectionError`
-- `AUTC`
-- `AUTC_components`
-  - `AUFPR`
-  - `AUFNR`
+* `RuntimeError` if `fit()` was not called.
 
-**Labeling convention**
+---
+
+## `close()`
 
-- In-distribution → label `0`
-- Out-of-distribution → label `1`
-- Higher score → more likely OOD
+Manually removes forward hook.
 
 ---
 
-## Design notes
+## `Viyog.Viyog_Score(centered_norms, Temperature=1000.0)`
 
-- Hook is attached during `Viyog.__init__`
-- Uses `torch.no_grad()` and `model.eval()`
-- Device inferred from model parameters unless specified
-- Accumulation during `fit()` uses CPU float64 for stability
-- Only first element of `(inputs, labels)` batches is used
+Internal scoring transformation that converts centered norms into bounded values.
 
 ---
 
-## Common pitfalls
+# Metrics
 
-- **Calling **``** before **`` Raises a runtime error.
+Use:
 
-- **No convolutional layer found** The model must contain `Conv1d/2d/3d` or a `conv1` attribute.
+```python
+viyog_metrics(ood_scores, adv_scores)
+```
+
+### Label Convention Used
 
-- **Hook not capturing features** Ensure the model’s forward pass actually uses the hooked layer.
+| Type | Label |
+| ---- | ----- |
+| OOD  | 1     |
+| ADV  | -1     |
 
-- **Device mismatch** Explicitly pass `device="cuda:0"` if needed.
+Lower score → more likely ADV
+Higher score → more likely OOD
 
 ---
 
-## Testing suggestions
+## Returned Metrics
 
-Add tests that verify:
+* **AUROC**
+* **AUPR_OOD**
+* **AUPR_ADV**
+* **FPR95**
+* **DetectionError**
+* **AUTC**
 
-- Hook attachment to `conv1`
-- `fit()` returns a finite mean
-- `score()` returns correct shapes
-- `viyog_metrics()` produces numeric outputs
+  * AUFPR
+  * AUFNR
 
-Example (pytest):
+These follow standard binary detection evaluation procedures.
 
-```python
-def test_viyog_basic():
-    import torch
-    from main import Viyog
-    model = torch.nn.Sequential(
-        torch.nn.Conv2d(3, 4, 3, padding=1),
-        torch.nn.Flatten(),
-        torch.nn.Linear(4 * 32 * 32, 10),
-    )
-```
+---
+
+# Design Constraints
+
+* Hook attaches during initialization.
+* Prefers `conv1` attribute if present.
+* Otherwise selects first `Conv1d/2d/3d` layer.
+* The layer must be exercised during forward pass.
+* Only first element of `(inputs, labels)` batches is used.
+* No gradients.
+* No parameter updates.
+* Stateless beyond baseline mean.
 
 ---
 
-## Contributing
+# Minimal Example
 
-Contributions are welcome, especially:
+```python
+import torch
+from viyog import Viyog
 
-- Unit tests and CI
-- Example scripts
-- Documentation improvements
-- Additional scoring variants
+model = torch.nn.Sequential(
+    torch.nn.Conv2d(3, 4, 3, padding=1),
+    torch.nn.Flatten(),
+    torch.nn.Linear(4 * 32 * 32, 10),
+)
 
-Please include a minimal reproducible example when reporting issues.
+v = Viyog(model, device="cpu")
 
----
+x = torch.randn(8, 3, 32, 32)
+loader = [(x, None)]
 
-## License
+v.fit(loader)
+scores = v.score(loader)
 
-No license is currently specified. Add a `LICENSE` file (e.g. MIT or Apache-2.0) to clarify usage and redistribution.
+print(scores.shape)  # torch.Size([8])
+
+v.close()
+```
 
 ---
 
-## Citation
+# License
+
+This project is licensed under the MIT License.
+
+See the [LICENSE](LICENSE) file for full details.
 
-If you use this code in academic work, please cite the associated paper or reference this repository in your methods section.
+# Citation
 
+If Viyog is used in academic work, cite the associated paper or reference this repository in the methods section.
diff --git a/Viyog.png b/Viyog.png
new file mode 100644
index 0000000..53077d8
Binary files /dev/null and b/Viyog.png differ
diff --git a/compose.yml b/compose.yml
new file mode 100644
index 0000000..b723d42
--- /dev/null
+++ b/compose.yml
@@ -0,0 +1,15 @@
+services:
+  app:
+    build:
+      context: .
+      dockerfile: docker/Dockerfile
+    restart: unless-stopped
+    working_dir: /app
+    volumes:
+      - .:/app
+      # Use anonymous volumes to avoid sharing host's .venv
+      # Renew volumes when you change packages with
+      # `docker compose up --renew-anon-volumes`
+      - /app/.venv
+    command: >
+      python main.py
diff --git a/docker/Dockerfile b/docker/Dockerfile
new file mode 100644
index 0000000..15a2a11
--- /dev/null
+++ b/docker/Dockerfile
@@ -0,0 +1,75 @@
+# syntax=docker/dockerfile:1
+
+ARG BUILDER_IMAGE="ubuntu:24.04"
+ARG RUNNER_IMAGE="ubuntu:24.04"
+
+FROM $BUILDER_IMAGE AS builder
+
+ENV DEBIAN_FRONTEND=noninteractive \
+    UV_PYTHON_INSTALL_DIR="/opt/python" \
+    UV_PROJECT_ENVIRONMENT="/opt/venv" \
+    # Compile packages to bytecode after installation
+    UV_COMPILE_BYTECODE=1 \
+    # Copy packages from wheel
+    UV_LINK_MODE=copy
+
+RUN rm -f /etc/apt/apt.conf.d/docker-clean \
+    && echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
+RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
+--mount=type=cache,target=/var/lib/apt,sharing=locked \
+    apt-get update && apt-get install -y --no-install-recommends \
+    git \
+    curl \
+    ca-certificates
+
+COPY --from=ghcr.io/astral-sh/uv:latest /uv /bin/uv
+WORKDIR /app
+
+# Sync dependencies
+RUN --mount=type=cache,target=/root/.cache/uv \
+    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
+    --mount=type=bind,source=.python-version,target=.python-version \
+    --mount=type=bind,source=uv.lock,target=uv.lock \
+    ulimit -n 8192 \
+    && uv sync --locked --no-install-project --no-editable
+
+# Sync this project
+COPY . .
+RUN --mount=type=cache,target=/root/.cache/uv \
+    ulimit -n 8192 \
+    && uv sync --locked --no-editable
+
+FROM $RUNNER_IMAGE AS production
+
+ENV DEBIAN_FRONTEND=noninteractive \
+    PATH="/opt/venv/bin:$PATH" \
+    UV_PYTHON_INSTALL_DIR="/opt/python" \
+    UV_PROJECT_ENVIRONMENT="/opt/venv" \
+    # Disable buffering of standard output and error streams
+    PYTHONUNBUFFERED=1 \
+    # Disable generation of .pyc files
+    PYTHONDONTWRITEBYTECODE=1
+
+# Copy Python
+COPY --from=builder /opt/python /opt/python
+COPY --from=builder /opt/venv /opt/venv
+COPY --from=ghcr.io/astral-sh/uv:latest /uv /bin/uv
+
+RUN rm -f /etc/apt/apt.conf.d/docker-clean \
+    && echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
+RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
+    --mount=type=cache,target=/var/lib/apt,sharing=locked \
+    apt-get update && apt-get install -y --no-install-recommends \
+    git \
+    curl \
+    ca-certificates \
+    build-essential \
+    sudo \
+    gosu
+
+# Allow sudo without password
+RUN echo '%sudo ALL=(ALL) NOPASSWD:ALL' >> /etc/sudoers
+
+COPY --chmod=755 ./docker/entrypoint.sh /usr/local/bin/entrypoint.sh
+
+ENTRYPOINT ["/usr/local/bin/entrypoint.sh"]
diff --git a/docker/build.sh b/docker/build.sh
new file mode 100755
index 0000000..ef05da9
--- /dev/null
+++ b/docker/build.sh
@@ -0,0 +1,14 @@
+#!/bin/bash
+
+cd $(dirname $0)
+cd ../
+
+BUILDER_IMAGE="ubuntu:24.04"
+RUNNER_IMAGE="ubuntu:24.04"
+IMAGE_NAME=$(basename $(pwd) | tr '[:upper:]' '[:lower:]')
+
+docker build \
+    --build-arg BUILDER_IMAGE=${BUILDER_IMAGE} \
+    --build-arg RUNNER_IMAGE=${RUNNER_IMAGE} \
+    -t "${IMAGE_NAME}:latest" \
+    -f docker/Dockerfile .
diff --git a/docker/entrypoint.sh b/docker/entrypoint.sh
new file mode 100644
index 0000000..51db1fe
--- /dev/null
+++ b/docker/entrypoint.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+
+USER_ID=${USER_ID:-1000}
+GROUP_ID=${GROUP_ID:-1000}
+USER_NAME=${USER_NAME:-user}
+GROUP_NAME=${GROUP_NAME:-group}
+
+userdel -r ubuntu > /dev/null 2>&1 # For Ubuntu 24.04 image
+groupadd -g ${GROUP_ID} ${GROUP_NAME} > /dev/null 2>&1
+useradd -u ${USER_ID} -g ${GROUP_NAME} -G sudo -o -m ${USER_NAME} > /dev/null 2>&1
+
+if [[ $# -eq 0 ]]; then
+    exec /usr/sbin/gosu ${USER_NAME} bash
+else
+    /usr/sbin/gosu ${USER_NAME} "$@"
+fi
diff --git a/docker/run.sh b/docker/run.sh
new file mode 100755
index 0000000..32507b1
--- /dev/null
+++ b/docker/run.sh
@@ -0,0 +1,41 @@
+#!/bin/bash
+
+cd $(dirname $0)
+cd ../
+
+IMAGE_NAME=$(basename $(pwd) | tr '[:upper:]' '[:lower:]')
+USER_ID=`id -u`
+GROUP_ID=`id -g`
+GROUP_NAME=`id -gn`
+USER_NAME=$USER
+
+GPU_OPTION=""
+if docker system info | grep -qE '^\s*Runtimes: .*nvidia.*'; then
+    # Use GPU
+    GPU_OPTION="--gpus all"
+fi
+
+# Check if TTY is available (not in CI environment)
+TTY_OPTION=""
+if [ -t 0 ] && [ -t 1 ]; then
+    TTY_OPTION="-it"
+fi
+
+docker run \
+    $TTY_OPTION \
+    $GPU_OPTION \
+    --rm \
+    --shm-size=32g \
+    --net host \
+    --env DISPLAY=$DISPLAY \
+    --env USER_NAME=$USER_NAME \
+    --env USER_ID=$USER_ID \
+    --env GROUP_NAME=$GROUP_NAME \
+    --env GROUP_ID=$GROUP_ID \
+    --workdir /app \
+    -v $HOME/.Xauthority:/home/$USER_NAME/.Xauthority:rw \
+    -v /tmp/.X11-unix:/tmp/.X11-unix:rw \
+    -v ${PWD}:/app \
+    --name "${IMAGE_NAME}-$(date '+%s')" \
+    "${IMAGE_NAME}:latest" \
+    ${@:-bash}
diff --git a/docs/source/api.rst b/docs/source/api.rst
index 2c17e7f..37153bb 100644
--- a/docs/source/api.rst
+++ b/docs/source/api.rst
@@ -7,3 +7,4 @@ API Reference
 
    src.main.Viyog
    src.main.viyog_metrics
+``
\ No newline at end of file
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 6ea22d5..cd80ade 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -1,17 +1,25 @@
 # docs/source/conf.py  (replace or merge into your existing conf.py)
 
-import os
 import sys
-sys.path.insert(0, os.path.abspath("../../"))  # repo root so "src" is importable
+from pathlib import Path
+
+# repo root so "src" is importable
+sys.path.insert(0, str(Path(__file__).parents[2].resolve()))  # repo root so "src" is importable
 # If your package root is different, adjust path accordingly (e.g., "../..", "../").
 
-project = "Viyog "
+project = "Viyog"
 extensions = [
     "sphinx.ext.autodoc",
-    "sphinx.ext.autosummary",
-    "sphinx.ext.napoleon",
+    "sphinx.ext.napoleon",  # Google / NumPy style docstrings
+    "sphinx.ext.autosummary",  # generate summaries
     "sphinx.ext.viewcode",
-    "sphinx_autodoc_typehints",  # optional but highly recommended
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.doctest",
+    "sphinx.ext.mathjax",
+    "recommonmark",  # if you want to use Markdown (or use myst-parser)
+    "sphinx_rtd_theme",
+    "nbsphinx",  # optional: for Jupyter notebooks in docs
+    "sphinx_gallery.gen_gallery",  # optional: for script -> gallery (sphinx-gallery)
 ]
 
 # autosummary will create .rst files for each listed object under _autosummary/
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..275bcc0
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,81 @@
+[project]
+name = "viyog"
+version = "0.0.1"
+description = "A simple tool to seperate Out of distribution and Adversarial Attack"
+readme = "README.md"
+authors = [{ name = "Aman Singh", email = "singh052001aman@gmail.com" }]
+requires-python = ">=3.10"
+dependencies = [
+    "numpy>=2.2.6",
+    "pytest>=9.0.2",
+    "torch>=2.10.0",
+]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/viyog"]
+
+[dependency-groups]
+dev = ["pre-commit>=4.2.0", "pytest>=8.4.0", "ruff>=0.11.12"]
+
+[tool.ruff]
+line-length = 100
+target-version = "py310"
+exclude = [".git", ".ruff_cache", ".venv", ".vscode"]
+
+[tool.ruff.lint]
+preview = true
+select = [
+    "ANN", # type annotation
+    "B",   # flake8-bugbear
+    "D",   # pydocstyle
+    "E",   # pycodestyle errors
+    "F",   # pyflakes
+    "I",   # isort
+    "PTH", # use `pathlib.Path` instead of `os.path`
+    "RUF", # ruff specific rules
+    "SIM", # flake8-simplify
+    "UP",  # pyupgrade
+    "W",   # pycodestyle warnings
+]
+ignore = [
+    "ANN401", # Checks that function arguments are annotated with a more specific type than Any.
+    "B007",   # Unused loop variable
+    "B008",   # Function call in default argument
+    "B905",   # `zip()` without `strict=True`
+    "COM812", # Missing trailing comma
+    "COM819", # Trailing comma prohibited
+    "D1",     # Missing docstring in public module, class, function, or method
+    "D203",   # One blank line required before class docstring (ignored because D211 is prioritized in pydocstyle convention 'google')
+    "D205",   # 1 blank line required between summary line and description
+    "D212",   # Multi-line docstring summary should start at the first line
+    "D213",   # Multi-line docstring summary should start at the second line
+    "D400",   # First line of docstring should end with a period
+    "D415",   # First line of docstring should end with punctuation (period, question mark, or exclamation point)
+    "E114",   # Indentation is not a multiple of four (comment)
+    "G004",   # Logging statement uses f-string
+    "ISC001", # Implicit string concatenation on a single line
+    "ISC002", # Implicit string concatenation on multiple lines
+    "PTH123", # `open()` should be replaced by `Path.open()`
+    "Q000",   # Single quotes found when double quotes are preferred
+    "Q001",   # Single quotes found when double quotes are preferred for multi-line strings
+    "Q002",   # Single quotes found when double quotes are preferred for docstrings
+    "RUF002", # Checks for ambiguous Unicode characters in docstrings.
+    "RUF003", # Checks for ambiguous Unicode characters in comments.
+    "SIM105", # try-except-pass blocks that can be replaced with the contextlib.suppress context manager.
+    "SIM108", # Use ternary operator instead of `if`-`else`-block
+    "SIM116", # Use a dictionary instead of consecutive `if` statements
+]
+unfixable = [
+    "F401", # unused import
+    "F841", # unused variable
+]
+
+[tool.ruff.lint.per-file-ignores]
+"__init__.py" = ["F401"]
+
+[tool.ruff.lint.pydocstyle]
+convention = "google"
diff --git a/src/__init__.py b/src/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/viyog/__init__.py b/src/viyog/__init__.py
new file mode 100644
index 0000000..e63dd5f
--- /dev/null
+++ b/src/viyog/__init__.py
@@ -0,0 +1,8 @@
+"""Viyog package — thin imports and re-exports.
+
+Keep implementation in a separate module to satisfy packaging/tooling rules.
+"""
+
+from .main import Viyog, viyog_metrics
+
+__all__ = ["Viyog", "viyog_metrics"]
diff --git a/src/main.py b/src/viyog/main.py
similarity index 86%
rename from src/main.py
rename to src/viyog/main.py
index 591d091..9e7463f 100644
--- a/src/main.py
+++ b/src/viyog/main.py
@@ -20,12 +20,12 @@
 
 from __future__ import annotations
 
-import math
-from typing import Tuple, Dict, Optional, List, Union
+from collections.abc import Sequence
+from typing import Any
 
-import torch
 import numpy as np
-from sklearn.metrics import roc_auc_score, average_precision_score, roc_curve
+import torch
+from sklearn.metrics import average_precision_score, roc_auc_score, roc_curve
 
 
 class Viyog:
@@ -48,24 +48,24 @@ class Viyog:
         Device to use for computation. If ``None`` (default) the device is inferred
         from model parameters as needed.
 
-    Attributes
+    Attributes:
     ----------
     id_norm_scores_mean : float or None
         Mean per-sample infinity norm computed by :meth:`fit`. ``None`` until fit
         completes successfully.
     """
 
-    def __init__(self, model: torch.nn.Module, device: Optional[torch.device | str] = None):
+    def __init__(self, model: torch.nn.Module, device: torch.device | str | None = None) -> None:
         self.model = model
         # device may be provided or inferred later
         self.device = torch.device(device) if device is not None else None
 
         # state
-        self.id_norm_scores_mean: Optional[float] = None
-        self._hook_layer_name: Optional[str] = None
-        self._hook_handle: Optional[torch.utils.hooks.RemovableHandle] = None
+        self.id_norm_scores_mean: float | None = None
+        self._hook_layer_name: str | None = None
+        self._hook_handle: torch.utils.hooks.RemovableHandle | None = None
         # will hold last-forward features (detached) while a forward is happening
-        self._features: Dict[str, torch.Tensor] = {}
+        self._features: dict[str, torch.Tensor] = {}
 
         # find conv & attach hook immediately
         name, layer = self._find_first_conv(self.model)
@@ -74,17 +74,17 @@ def __init__(self, model: torch.nn.Module, device: Optional[torch.device | str]
         self._hook_layer_name = name
 
         # attach hook that stores a detached tensor on the device of the layer
-        def hook_fn(module, input, output):
+        def hook_fn(module: Any, input: Any, output: Any) -> None:
             # detach to avoid retaining graph; keep on same device for speed
             self._features["first"] = output.detach()
 
         self._hook_handle = layer.register_forward_hook(hook_fn)
 
     # Context-manager helpers so user can rely on deterministic cleanup
-    def __enter__(self) -> "Viyog":
+    def __enter__(self) -> Viyog:
         return self
 
-    def __exit__(self, exc_type, exc_value, traceback) -> None:
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
         self.close()
 
     def close(self) -> None:
@@ -106,7 +106,7 @@ def Viyog_Score(num: torch.Tensor, Temperature: float = 1000.0) -> torch.Tensor:
         Temperature : float, optional
             Temperature scaling factor. Default is 1000.0.
 
-        Returns
+        Returns:
         -------
         torch.Tensor
             Tensor of same shape as ``num`` with values approx in (-1, 1).
@@ -118,19 +118,19 @@ def Viyog_Score(num: torch.Tensor, Temperature: float = 1000.0) -> torch.Tensor:
         return sign / denom
 
     @staticmethod
-    def _find_first_conv(module: torch.nn.Module) -> Tuple[Optional[str], Optional[torch.nn.Module]]:
+    def _find_first_conv(module: torch.nn.Module) -> tuple[str | None, torch.nn.Module | None]:
         """
         Find the first convolutional submodule.
 
         Prefers attribute ``conv1`` if present; otherwise iterates submodules in
         ``named_modules()`` order and returns the first instance of Conv1d/2d/3d.
 
-        Returns
+        Returns:
         -------
         (name, module) or (None, None) if not found
         """
         if hasattr(module, "conv1"):
-            return "conv1", getattr(module, "conv1")
+            return "conv1", module.conv1
         for name, sub in module.named_modules():
             if name == "":
                 continue
@@ -153,7 +153,7 @@ def _get_first_layer_features(self, x: torch.Tensor) -> torch.Tensor:
         """
         Run a forward and return the detached features captured by the hook.
 
-        Notes
+        Notes:
         -----
         - This uses the persistent hook attached in :meth:`__init__`, which writes
           to ``self._features`` under the key ``"first"``.
@@ -181,7 +181,7 @@ def fit(self, trainloader: torch.utils.data.DataLoader) -> float:
             DataLoader providing samples to estimate the mean activation norm. Each
             yielded batch should be (inputs, labels) or where inputs are the first item.
 
-        Returns
+        Returns:
         -------
         float
             The computed mean per-sample infinity norm (stored in
@@ -219,7 +219,11 @@ def fit(self, trainloader: torch.utils.data.DataLoader) -> float:
         return self.id_norm_scores_mean
 
     @torch.no_grad()
-    def score(self, x: Union[torch.Tensor, torch.utils.data.DataLoader], Temperature: float = 1000.0) -> torch.Tensor:
+    def score(
+        self,
+        x: torch.Tensor | torch.utils.data.DataLoader,
+        Temperature: float = 1000.0,
+    ) -> torch.Tensor:
         """
         If `x` is a Tensor (batch): returns a 1D tensor of scores for that batch.
         If `x` is a DataLoader: processes entire loader and returns concatenated scores.
@@ -231,7 +235,7 @@ def score(self, x: Union[torch.Tensor, torch.utils.data.DataLoader], Temperature
         Temperature : float, optional
             Temperature scaling factor passed through to :meth:`Viyog_Score`.
 
-        Returns
+        Returns:
         -------
         torch.Tensor
             1D tensor of scores (device equals inferred device).
@@ -245,7 +249,7 @@ def score(self, x: Union[torch.Tensor, torch.utils.data.DataLoader], Temperature
 
         if isinstance(x, torch.utils.data.DataLoader):
             # convenience: score an entire loader
-            out = []
+            out: list[torch.Tensor] = []
             for batch in x:
                 if isinstance(batch, (list, tuple)) and len(batch) >= 1:
                     xb = batch[0]
@@ -265,11 +269,15 @@ def score(self, x: Union[torch.Tensor, torch.utils.data.DataLoader], Temperature
         scores = self.Viyog_Score(centered, Temperature=Temperature)
         return scores
 
-    def score_loader(self, loader: torch.utils.data.DataLoader, Temperature: float = 1000.0) -> torch.Tensor:
+    def score_loader(
+        self,
+        loader: torch.utils.data.DataLoader,
+        Temperature: float = 1000.0,
+    ) -> torch.Tensor:
         """Helper that returns a single 1D tensor of scores for the whole loader."""
         return self.score(loader, Temperature=Temperature)
 
-    def __del__(self):
+    def __del__(self) -> None:
         # ensure hook removed on deletion (best-effort)
         try:
             self.close()
@@ -278,7 +286,11 @@ def __del__(self):
 
 
 # ---------------- Viyog Metrics ----------------
-def viyog_metrics(id_scores, ood_scores, recall_level: float = 0.95) -> dict:
+def viyog_metrics(
+    id_scores: Sequence[float],
+    ood_scores: Sequence[float],
+    recall_level: float = 0.95,
+) -> dict[str, Any]:
     """
     Compute a collection of OOD detection metrics from id and ood scores.
 
@@ -291,7 +303,7 @@ def viyog_metrics(id_scores, ood_scores, recall_level: float = 0.95) -> dict:
     recall_level : float, optional
         TPR level for which to compute FPR (default 0.95).
 
-    Returns
+    Returns:
     -------
     dict
         Dictionary with keys "AUROC", "AUPR_IN", "AUPR_OUT", "FPR95", "DetectionError",
@@ -301,10 +313,12 @@ def viyog_metrics(id_scores, ood_scores, recall_level: float = 0.95) -> dict:
     ood_scores = np.asarray(ood_scores)
 
     scores = np.concatenate([id_scores, ood_scores])
-    labels = np.concatenate([
-        np.zeros(len(id_scores)),  # in-distribution -> label 0
-        np.ones(len(ood_scores))   # out-of-distribution -> label 1
-    ])
+    labels = np.concatenate(
+        [
+            np.zeros(len(id_scores)),  # in-distribution -> label 0
+            np.ones(len(ood_scores)),  # out-of-distribution -> label 1
+        ]
+    )
 
     auroc = roc_auc_score(labels, scores)
     aupr_out = average_precision_score(labels, scores)
@@ -323,9 +337,9 @@ def viyog_metrics(id_scores, ood_scores, recall_level: float = 0.95) -> dict:
         tpr = tpr[::-1]
 
     # area under FPR vs threshold
-    aufpr = float(np.trapz(fpr, thresholds))
+    aufpr = float(np.trapezoid(fpr, thresholds))
     # area under (1 - TPR) vs threshold
-    aufnr = float(np.trapz(1.0 - tpr, thresholds))
+    aufnr = float(np.trapezoid(1.0 - tpr, thresholds))
     autc = 0.5 * (aufpr + aufnr)
 
     return {
@@ -335,8 +349,5 @@ def viyog_metrics(id_scores, ood_scores, recall_level: float = 0.95) -> dict:
         "FPR95": float(fpr95),
         "DetectionError": float(det_error),
         "AUTC": float(autc),
-        "AUTC_components": {
-            "AUFPR": float(aufpr),
-            "AUFNR": float(aufnr)
-        }
+        "AUTC_components": {"AUFPR": float(aufpr), "AUFNR": float(aufnr)},
     }
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..d5093a3
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,22 @@
+# make the src/ layout importable (adjust if your layout differs)
+import random
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+import torch
+
+ROOT = Path(__file__).resolve().parents[1]
+SRC = ROOT / "src"
+if str(SRC) not in sys.path:
+    sys.path.insert(0, str(SRC))
+
+
+@pytest.fixture(autouse=True)
+def deterministic_seed() -> None:
+    """Deterministic RNG for tests that use random numbers."""
+    seed = 12345
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
diff --git a/tests/test_main.py b/tests/test_main.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
new file mode 100644
index 0000000..735fb5e
--- /dev/null
+++ b/tests/test_metrics.py
@@ -0,0 +1,21 @@
+import numpy as np
+
+from viyog import viyog_metrics
+
+
+def test_viyog_metrics_basic_separation() -> None:
+    # generate separable scores: ID centered near 0, OOD higher values
+    id_scores = np.random.normal(loc=0.0, scale=0.5, size=500)
+    ood_scores = np.random.normal(loc=3.0, scale=0.5, size=200)
+
+    metrics = viyog_metrics(id_scores, ood_scores, recall_level=0.95)
+    # basic expected keys exist
+    for k in ("AUROC", "AUPR_IN", "AUPR_OUT", "FPR95", "DetectionError", "AUTC", "AUTC_components"):
+        assert k in metrics
+
+    # AUROC should be well above chance for clearly separable scores
+    assert 0.0 <= metrics["AUROC"] <= 1.0
+    assert metrics["AUROC"] > 0.9
+
+    # FPR95 is a rate between 0 and 1
+    assert 0.0 <= metrics["FPR95"] <= 1.0
diff --git a/tests/test_viyog.py b/tests/test_viyog.py
new file mode 100644
index 0000000..1f6fd92
--- /dev/null
+++ b/tests/test_viyog.py
@@ -0,0 +1,94 @@
+import torch
+from torch.utils.data import DataLoader, TensorDataset
+
+from viyog import Viyog
+
+
+class _SmallConvNet(torch.nn.Module):
+    """Tiny model with attribute `conv1` so Viyog finds it predictably."""
+
+    def __init__(self) -> None:
+        super().__init__()
+        # conv1 will be discovered by Viyog
+        self.conv1 = torch.nn.Conv2d(3, 4, kernel_size=3, padding=1)
+        self.pool = torch.nn.AdaptiveAvgPool2d(1)
+        self.fc = torch.nn.Linear(4, 1)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.conv1(x)
+        x = torch.relu(x)
+        x = self.pool(x)
+        x = x.reshape(x.shape[0], -1)
+        return self.fc(x)
+
+
+def make_random_loader(total: int = 8, batch_size: int = 4) -> DataLoader:
+    # simple inputs shaped (N, 3, 8, 8)
+    inputs = torch.randn(total, 3, 8, 8)
+    labels = torch.zeros(total, dtype=torch.long)
+    ds = TensorDataset(inputs, labels)
+    return DataLoader(ds, batch_size=batch_size)
+
+
+def test_hook_captures_features() -> None:
+    model = _SmallConvNet()
+    v = Viyog(model)
+    # single batch
+    x = torch.randn(2, 3, 8, 8)
+    feats = v._get_first_layer_features(x)
+    assert isinstance(feats, torch.Tensor)
+    # conv1 output channels = 4
+    assert feats.ndim >= 4
+    assert feats.shape[1] == 4
+    # detached (no grad)
+    assert not feats.requires_grad
+    v.close()
+
+
+def test_fit_and_score_tensor() -> None:
+    model = _SmallConvNet()
+    v = Viyog(model)
+    loader = make_random_loader(total=8, batch_size=4)
+    mean = v.fit(loader)
+    assert isinstance(mean, float)
+    assert mean >= 0.0
+
+    # score a single batch tensor
+    batch = torch.randn(3, 3, 8, 8)
+    scores = v.score(batch, Temperature=100.0)
+    assert isinstance(scores, torch.Tensor)
+    assert scores.shape[0] == 3
+    # scores in reasonable numeric range (-1, 1)
+    assert torch.all(scores <= 1.0 + 1e-6)
+    assert torch.all(scores >= -1.0 - 1e-6)
+    v.close()
+
+
+def test_score_loader_concat() -> None:
+    model = _SmallConvNet()
+    v = Viyog(model)
+    loader = make_random_loader(total=10, batch_size=3)  # yields 4 batches (3+3+3+1)
+    v.fit(make_random_loader(total=6, batch_size=3))
+    out = v.score(loader)
+    # total samples should match
+    total = sum(
+        len(batch[0]) if isinstance(batch, (list, tuple)) else len(batch) for batch in loader
+    )
+    assert out.shape[0] == total
+    v.close()
+
+
+def test_context_manager_and_hook_removal() -> None:
+    model = _SmallConvNet()
+    # use context manager to ensure hook is removed on exit
+    with Viyog(model) as v:
+        x = torch.randn(2, 3, 8, 8)
+        _ = v._get_first_layer_features(x)
+        # features should be present immediately after forward
+        assert "first" in v._features
+
+    # after exiting context, the hook should be removed
+    # run a forward; _features should not be populated
+    v._features.pop("first", None)
+    _ = model(torch.randn(2, 3, 8, 8))
+    assert "first" not in v._features
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new file mode 100644
index 0000000..f6db9f1
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@@ -0,0 +1,900 @@
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