final notebookk run

notebooks/Final-Submission.ipynb

@@ -11,10 +11,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "d318d1f0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Van: 1111 images\n",
+      "Taxi: 748 images\n",
+      "Bicycle: 1618 images\n",
+      "Bus: 2133 images\n",
+      "Car: 6781 images\n",
+      "Motorcycle: 2986 images\n",
+      "Truck: 2033 images\n",
+      "NonVehicles: 8968 images\n",
+      "Total Count: 26378 images\n"
+     ]
+    }
+   ],
    "source": [
     "import os\n",
     "\n",
@@ -42,10 +58,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "5604ace3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Size: (64, 64)\n",
+      "Mode: RGB\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from PIL import Image\n",
     "import matplotlib.pyplot as plt\n",
@@ -72,10 +107,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "3cedd586",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique sizes: {(64, 64)}\n",
+      "Unique modes: {'RGB'}\n"
+     ]
+    }
+   ],
    "source": [
     "sizes = set()\n",
     "modes = set()\n",
@@ -103,10 +147,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "8f556b22",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GPU: NVIDIA GeForce RTX 5080\n",
+      "Running on: cuda\n"
+     ]
+    }
+   ],
    "source": [
     "import torch\n",
     "\n",
@@ -135,10 +188,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "f68c1a25",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train: 21102, Test: 5276\n"
+     ]
+    }
+   ],
    "source": [
     "import math \n",
     "from torchvision import datasets, transforms\n",
@@ -188,10 +249,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "e1539eaa",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n",
+      "['Bicycle', 'Bus', 'Car', 'Motorcycle', 'NonVehicles', 'Taxi', 'Truck', 'Van']\n",
+      "8\n"
+     ]
+    }
+   ],
    "source": [
     "NUM_WORKERS = min(4, os.cpu_count() or 1)\n",
     "PIN_MEMORY  = (DEVICE.type == 'cuda') #  Pin memory if GPU available for CUDA\n",
@@ -218,10 +289,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "d1b7d9ca",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Net(\n",
+      "  (features): Sequential(\n",
+      "    (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (2): ReLU()\n",
+      "    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+      "    (4): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "    (5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (6): ReLU()\n",
+      "    (7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+      "    (8): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "    (9): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (10): ReLU()\n",
+      "    (11): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+      "  )\n",
+      "  (classifier): Sequential(\n",
+      "    (0): Flatten(start_dim=1, end_dim=-1)\n",
+      "    (1): Linear(in_features=8192, out_features=512, bias=True)\n",
+      "    (2): ReLU()\n",
+      "    (3): Dropout(p=0.5, inplace=False)\n",
+      "    (4): Linear(in_features=512, out_features=8, bias=True)\n",
+      "  )\n",
+      ")\n"
+     ]
+    }
+   ],
    "source": [
     "import torch.nn as nn\n",
     "\n",
@@ -285,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "54d11a04",
    "metadata": {},
    "outputs": [],
@@ -310,10 +411,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "374d0590",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: Loss=1.137, Accuracy=62.59%\n",
+      "Epoch 2: Loss=0.866, Accuracy=69.80%\n",
+      "Epoch 3: Loss=0.796, Accuracy=71.93%\n",
+      "Epoch 4: Loss=0.752, Accuracy=74.03%\n",
+      "Epoch 5: Loss=0.712, Accuracy=74.89%\n",
+      "Epoch 6: Loss=0.683, Accuracy=76.17%\n",
+      "Epoch 7: Loss=0.659, Accuracy=76.99%\n",
+      "Epoch 8: Loss=0.635, Accuracy=77.73%\n",
+      "Epoch 9: Loss=0.615, Accuracy=78.54%\n",
+      "Epoch 10: Loss=0.597, Accuracy=78.92%\n",
+      "Epoch 11: Loss=0.575, Accuracy=79.91%\n",
+      "Epoch 12: Loss=0.567, Accuracy=80.05%\n",
+      "Epoch 13: Loss=0.554, Accuracy=80.72%\n",
+      "Epoch 14: Loss=0.542, Accuracy=81.12%\n",
+      "Epoch 15: Loss=0.532, Accuracy=81.31%\n",
+      "Epoch 16: Loss=0.522, Accuracy=81.75%\n",
+      "Epoch 17: Loss=0.505, Accuracy=82.06%\n",
+      "Epoch 18: Loss=0.489, Accuracy=82.72%\n",
+      "Epoch 19: Loss=0.487, Accuracy=82.87%\n",
+      "Epoch 20: Loss=0.488, Accuracy=82.76%\n",
+      "Epoch 21: Loss=0.471, Accuracy=83.04%\n",
+      "Epoch 22: Loss=0.455, Accuracy=83.86%\n",
+      "Epoch 23: Loss=0.449, Accuracy=83.77%\n",
+      "Epoch 24: Loss=0.444, Accuracy=84.12%\n",
+      "Epoch 25: Loss=0.448, Accuracy=84.23%\n",
+      "Epoch 26: Loss=0.431, Accuracy=84.57%\n",
+      "Epoch 27: Loss=0.425, Accuracy=85.00%\n",
+      "Epoch 28: Loss=0.414, Accuracy=84.98%\n",
+      "Epoch 29: Loss=0.412, Accuracy=85.57%\n",
+      "Epoch 30: Loss=0.399, Accuracy=85.84%\n",
+      "Finished Training\n"
+     ]
+    }
+   ],
    "source": [
     "train_losses, train_accs = [], [] #To store accs for visualization\n",
     "\n",
@@ -361,10 +500,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "c71ee0ff",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
     "\n",
@@ -401,7 +551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "2bf2b9a2",
    "metadata": {},
    "outputs": [],
@@ -422,10 +572,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "9e54f566",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final Train Accuracy : 86.68%\n",
+      "Final Test  Accuracy : 84.67%\n"
+     ]
+    }
+   ],
    "source": [
     "model.eval() # Switch to eval mode (disabled droupout --> higher acc) (credit: Claude)\n",
     "train_correct, train_total = 0, 0\n",
@@ -461,10 +620,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "8cc7ed40",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy for class: Bicycle    is 81.3%\n",
+      "Accuracy for class: Bus        is 87.0%\n",
+      "Accuracy for class: Car        is 86.1%\n",
+      "Accuracy for class: Motorcycle is 89.9%\n",
+      "Accuracy for class: NonVehicles is 99.8%\n",
+      "Accuracy for class: Taxi       is 50.0%\n",
+      "Accuracy for class: Truck      is 46.4%\n",
+      "Accuracy for class: Van        is 36.7%\n"
+     ]
+    }
+   ],
    "source": [
     "correct_pred = {classname: 0 for classname in classes} # Correct predicitions per class\n",
     "total_pred = {classname: 0 for classname in classes} # total images seen per class\n",