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

notebooks/experiments/01-tutorial-cnn.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "7a37220a",
    "metadata": {},
    "outputs": [
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "d318d1f0",
    "metadata": {},
    "outputs": [
@@ -42,7 +42,7 @@
    "source": [
     "import os\n",
     "\n",
-    "data_dir = '../data/raw/vehicle_classification'\n",
+    "data_dir = '../../data/raw/vehicle_classification'\n",
     "\n",
     "for class_name in os.listdir(data_dir):\n",
     "    class_path = os.path.join(data_dir, class_name)\n",
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "5604ace3",
    "metadata": {},
    "outputs": [
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "3cedd586",
    "metadata": {},
    "outputs": [
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "8f556b22",
    "metadata": {},
    "outputs": [
@@ -184,7 +184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "37793c77",
    "metadata": {},
    "outputs": [
@@ -222,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "f68c1a25",
    "metadata": {},
    "outputs": [
@@ -257,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "e1539eaa",
    "metadata": {},
    "outputs": [
@@ -289,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "d1b7d9ca",
    "metadata": {},
    "outputs": [
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "54d11a04",
    "metadata": {},
    "outputs": [],
@@ -370,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "374d0590",
    "metadata": {},
    "outputs": [
@@ -378,16 +378,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1: Loss=1.575, Accuracy=50.54%\n",
-      "Epoch 2: Loss=1.196, Accuracy=60.35%\n",
-      "Epoch 3: Loss=1.018, Accuracy=64.98%\n",
-      "Epoch 4: Loss=0.909, Accuracy=68.58%\n",
-      "Epoch 5: Loss=0.842, Accuracy=70.87%\n",
-      "Epoch 6: Loss=0.784, Accuracy=72.83%\n",
-      "Epoch 7: Loss=0.738, Accuracy=74.24%\n",
-      "Epoch 8: Loss=0.703, Accuracy=75.30%\n",
-      "Epoch 9: Loss=0.671, Accuracy=76.30%\n",
-      "Epoch 10: Loss=0.641, Accuracy=77.64%\n",
+      "Epoch 1: Loss=1.518, Accuracy=51.03%\n",
+      "Epoch 2: Loss=1.110, Accuracy=62.02%\n",
+      "Epoch 3: Loss=0.976, Accuracy=66.24%\n",
+      "Epoch 4: Loss=0.906, Accuracy=68.52%\n",
+      "Epoch 5: Loss=0.838, Accuracy=70.77%\n",
+      "Epoch 6: Loss=0.782, Accuracy=72.33%\n",
+      "Epoch 7: Loss=0.735, Accuracy=74.25%\n",
+      "Epoch 8: Loss=0.696, Accuracy=75.42%\n",
+      "Epoch 9: Loss=0.665, Accuracy=76.53%\n",
+      "Epoch 10: Loss=0.634, Accuracy=77.40%\n",
       "Finished Training\n"
      ]
     }
@@ -425,12 +425,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "id": "2bf2b9a2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "PATH = '../models/tutorial-cnn.pth'\n",
+    "PATH = '../../models/tutorial-cnn.pth'\n",
     "torch.save(model.state_dict(), PATH)"
    ]
   },
@@ -444,7 +444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "id": "bc158602",
    "metadata": {},
    "outputs": [
@@ -452,7 +452,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test Accuracy: 74.53%\n"
+      "Test Accuracy: 75.66%\n"
      ]
     }
    ],
@@ -474,7 +474,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "id": "8cc7ed40",
    "metadata": {},
    "outputs": [
@@ -482,14 +482,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy for class: Bicycle    is 43.5%\n",
-      "Accuracy for class: Bus        is 53.8%\n",
-      "Accuracy for class: Car        is 87.6%\n",
-      "Accuracy for class: Motorcycle is 75.2%\n",
-      "Accuracy for class: NonVehicles is 99.0%\n",
-      "Accuracy for class: Taxi       is 20.0%\n",
-      "Accuracy for class: Truck      is 13.9%\n",
-      "Accuracy for class: Van        is 24.7%\n"
+      "Accuracy for class: Bicycle    is 73.6%\n",
+      "Accuracy for class: Bus        is 65.1%\n",
+      "Accuracy for class: Car        is 86.2%\n",
+      "Accuracy for class: Motorcycle is 55.7%\n",
+      "Accuracy for class: NonVehicles is 99.3%\n",
+      "Accuracy for class: Taxi       is 32.9%\n",
+      "Accuracy for class: Truck      is 24.8%\n",
+      "Accuracy for class: Van        is 22.9%\n"
      ]
     }
    ],

requirements.txt

@@ -1,67 +1,8 @@
-asttokens==3.0.1
-comm==0.2.3
-contourpy==1.3.3
-cuda-bindings==12.9.4
-cuda-pathfinder==1.2.2
-cuda-toolkit==12.8.1
-cycler==0.12.1
-debugpy==1.8.20
-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
+torch>=2.0.0
+torchvision>=0.15.0
+numpy>=1.24.0
+matplotlib>=3.7.0
+Pillow>=9.0.0
+scikit-learn>=1.0.0
+jupyter>=1.0.0
+ipykernel>=6.0.0