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

requirements.txt

@@ -1,67 +1,8 @@
-asttokens==3.0.1
+torch>=2.0.0
-comm==0.2.3
+torchvision>=0.15.0
-contourpy==1.3.3
+numpy>=1.24.0
-cuda-bindings==12.9.4
+matplotlib>=3.7.0
-cuda-pathfinder==1.2.2
+Pillow>=9.0.0
-cuda-toolkit==12.8.1
+scikit-learn>=1.0.0
-cycler==0.12.1
+jupyter>=1.0.0
-debugpy==1.8.20
+ipykernel>=6.0.0
-decorator==5.2.1
-executing==2.2.1
-filelock==3.25.2
-fonttools==4.62.1
-fsspec==2026.2.0
-ipykernel==7.2.0
-ipython==9.10.0
-ipython_pygments_lexers==1.1.1
-jedi==0.19.2
-Jinja2==3.1.6
-jupyter_client==8.8.0
-jupyter_core==5.9.1
-kiwisolver==1.5.0
-MarkupSafe==3.0.2
-matplotlib==3.10.8
-matplotlib-inline==0.2.1
-mpmath==1.3.0
-nest-asyncio==1.6.0
-networkx==3.6.1
-numpy==2.4.3
-nvidia-cublas-cu12==12.8.4.1
-nvidia-cuda-cupti-cu12==12.8.90
-nvidia-cuda-nvrtc-cu12==12.8.93
-nvidia-cuda-runtime-cu12==12.8.90
-nvidia-cudnn-cu12==9.20.0.48
-nvidia-cufft-cu12==11.3.3.83
-nvidia-cufile-cu12==1.13.1.3
-nvidia-curand-cu12==10.3.9.90
-nvidia-cusolver-cu12==11.7.3.90
-nvidia-cusparse-cu12==12.5.8.93
-nvidia-cusparselt-cu12==0.7.1
-nvidia-nccl-cu12==2.29.7
-nvidia-nvjitlink-cu12==12.8.93
-nvidia-nvshmem-cu12==3.4.5
-nvidia-nvtx-cu12==12.8.90
-packaging==26.0
-parso==0.8.6
-pexpect==4.9.0
-pillow==12.1.1
-platformdirs==4.9.4
-prompt_toolkit==3.0.52
-psutil==7.2.2
-ptyprocess==0.7.0
-pure_eval==0.2.3
-Pygments==2.19.2
-pyparsing==3.3.2
-python-dateutil==2.9.0.post0
-pyzmq==27.1.0
-six==1.17.0
-stack-data==0.6.3
-sympy==1.14.0
-torch==2.12.0.dev20260318+cu128
-torchaudio==2.11.0.dev20260318+cu128
-torchvision==0.26.0.dev20260318+cu128
-tornado==6.5.5
-traitlets==5.14.3
-triton==3.6.0+git9844da95
-typing_extensions==4.15.0
-wcwidth==0.6.0