Final working, with good req.txt and readme

README.md

@@ -1,6 +1,92 @@
-# vehicle-classification
+# Vehicle Classification — UTD Deep Learning Assessment
 
-UTD Vehicle Classification Assessment Code
+A Convolutional Neural Network (CNN) trained to classify 8 vehicle types:
+**Bicycle, Bus, Car, Motorcycle, NonVehicles, Taxi, Truck, Van**
 
-Download Zip and move to data/raw/
-Extract zip into data/raw/vehicle_classification
+---
+
+## Requirements
+- Python 3.11 (see `.python-version`)
+- pip
+
+---
+
+## Setup
+
+### 1. Clone the repository
+```bash
+git clone https://git.keshavanand.net/KeshavAnandCode/vehicle-classification.git
+cd utd-vehicle-classification
+```
+
+### 2. Create and activate a virtual environment
+```bash
+python3.11 -m venv .venv
+```
+**Mac/Linux:**
+```bash
+source .venv/bin/activate
+```
+**Windows:**
+```bash
+.venv\Scripts\activate
+```
+
+### 3. Install dependencies
+```bash
+pip install -r requirements.txt
+```
+
+### 4. Download the dataset
+- Download `vehicle_classification.zip`
+- Place and extract it so the structure looks exactly like this:
+```
+data/
+└── raw/
+    └── vehicle_classification/
+        ├── Bicycle/
+        ├── Bus/
+        ├── Car/
+        ├── Motorcycle/
+        ├── NonVehicles/
+        ├── Taxi/
+        ├── Truck/
+        └── Van/
+```
+
+### 5. Run the notebook
+```bash
+jupyter notebook notebooks/submission.ipynb
+```
+Once open: **Kernel → Restart & Run All**
+
+---
+
+## Project Structure
+```
+utd-vehicle-classification/
+├── notebooks/
+│   ├── submission.ipynb          ← main submission, run this
+│   └── experiments/              ← exploratory notebooks (ignore)
+├── data/
+│   └── raw/
+│       └── vehicle_classification/   ← dataset goes here
+├── models/                       ← saved model weights (auto-created on run)
+├── results/                      ← training curves (auto-created on run)
+├── requirements.txt
+└── README.md
+```
+
+---
+
+## Expected Output
+```
+Final Train Accuracy : ~88%
+Final Test  Accuracy : ~82%
+```
+
+---
+
+## Notes
+- Device is detected automatically — runs on NVIDIA GPU, Apple Silicon (MPS), or CPU with no code changes required
+- `models/` and `results/` directories are created automatically when the notebook runs