diff --git "a/results.ipynb" "b/results.ipynb" new file mode 100644--- /dev/null +++ "b/results.ipynb" @@ -0,0 +1,1324 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "48cc854c", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import torch as t\n", + "from datasets import load_dataset\n", + "import os\n", + "from torchvision import transforms, datasets" + ] + }, + { + "cell_type": "markdown", + "id": "bb0a9086", + "metadata": {}, + "source": [ + "## Select the Accelerator" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c65dc116", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using device: mps\n" + ] + } + ], + "source": [ + "device = t.device(\"cuda\" if t.cuda.is_available() else\n", + " \"mps\" if t.backends.mps.is_available() else\n", + " \"cpu\")\n", + "\n", + "print(f\"Using device: {device}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ae2a52e7", + "metadata": {}, + "source": [ + "## Define Model Class" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c4f23bb8", + "metadata": {}, + "outputs": [], + "source": [ + "import torch.nn as nn\n", + "\n", + "class BottleneckBlock(nn.Module):\n", + " expansion = 4\n", + "\n", + " def __init__(self, in_channels, mid_channels, stride=1):\n", + " super(BottleneckBlock, self).__init__()\n", + " \n", + " out_channels = mid_channels * self.expansion\n", + "\n", + " \n", + " self.conv1 = nn.Conv2d(in_channels, mid_channels, kernel_size=1, bias=False)\n", + " self.bn1 = nn.BatchNorm2d(mid_channels)\n", + " \n", + " self.conv2 = nn.Conv2d(mid_channels, mid_channels, kernel_size=3, stride=stride, padding=1, bias=False)\n", + " self.bn2 = nn.BatchNorm2d(mid_channels)\n", + " \n", + " self.conv3 = nn.Conv2d(mid_channels, out_channels, kernel_size=1, bias=False)\n", + " self.bn3 = nn.BatchNorm2d(out_channels)\n", + " \n", + " self.relu = nn.ReLU(inplace=True)\n", + "\n", + " self.shortcut = nn.Sequential()\n", + "\n", + " if stride != 1 or in_channels != out_channels:\n", + " self.shortcut = nn.Sequential(\n", + " nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),\n", + " nn.BatchNorm2d(out_channels)\n", + " )\n", + "\n", + " def forward(self, x):\n", + " identity = x\n", + "\n", + " out = self.conv1(x)\n", + " out = self.bn1(out)\n", + " out = self.relu(out)\n", + "\n", + " out = self.conv2(out)\n", + " out = self.bn2(out)\n", + " out = self.relu(out)\n", + "\n", + " out = self.conv3(out)\n", + " out = self.bn3(out)\n", + " \n", + " identity = self.shortcut(identity)\n", + "\n", + " out += identity\n", + " out = self.relu(out)\n", + "\n", + " return out \n", + " \n", + "\n", + "class ResNet50(nn.Module):\n", + " def __init__(self, num_classes=16, channels_img=3):\n", + " super(ResNet50, self).__init__()\n", + " \n", + " self.in_channels = 64\n", + "\n", + " self.conv1 = nn.Conv2d(channels_img, 64, kernel_size=7, stride=2, padding=3, bias=False)\n", + " self.bn1 = nn.BatchNorm2d(64)\n", + " self.relu = nn.ReLU(inplace=True)\n", + " self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n", + "\n", + " self.stage1 = self._make_layer(mid_channels=64, num_blocks=3, stride=1)\n", + " \n", + " self.stage2 = self._make_layer(mid_channels=128, num_blocks=4, stride=2)\n", + " \n", + " self.stage3 = self._make_layer(mid_channels=256, num_blocks=6, stride=2)\n", + " \n", + " self.stage4 = self._make_layer(mid_channels=512, num_blocks=3, stride=2)\n", + "\n", + " self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n", + "\n", + " self.fc = nn.Linear(512 * 4, num_classes) # 512*4 = 2048\n", + "\n", + " def _make_layer(self, mid_channels, num_blocks, stride):\n", + " layers = []\n", + " \n", + " layers.append(BottleneckBlock(self.in_channels, mid_channels, stride))\n", + " \n", + " self.in_channels = mid_channels * 4\n", + "\n", + " for _ in range(num_blocks - 1):\n", + " layers.append(BottleneckBlock(self.in_channels, mid_channels, stride=1))\n", + "\n", + " return nn.Sequential(*layers)\n", + "\n", + " def forward(self, x):\n", + " # Stem\n", + " x = self.conv1(x)\n", + " x = self.bn1(x)\n", + " x = self.relu(x)\n", + " x = self.maxpool(x)\n", + "\n", + " # Stages\n", + " x = self.stage1(x)\n", + " x = self.stage2(x)\n", + " x = self.stage3(x)\n", + " x = self.stage4(x)\n", + "\n", + " # Head\n", + " x = self.avgpool(x)\n", + " x = t.flatten(x, 1) # Flatten from (N, 2048, 1, 1) to (N, 2048)\n", + " x = self.fc(x)\n", + "\n", + " return x \n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7b8d02be", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ResNet50(\n", + " (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n", + " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", + " (stage1): Sequential(\n", + " (0): BottleneckBlock(\n", + " (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (shortcut): Sequential(\n", + " (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (1): BottleneckBlock(\n", + " (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (shortcut): Sequential()\n", + " )\n", + " (2): BottleneckBlock(\n", + " (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (shortcut): Sequential()\n", + " )\n", + " )\n", + " (stage2): Sequential(\n", + " (0): BottleneckBlock(\n", + " (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (shortcut): Sequential(\n", + " (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + " (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (1): BottleneckBlock(\n", + " (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (shortcut): Sequential()\n", + " )\n", + " (2): BottleneckBlock(\n", + " (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (shortcut): Sequential()\n", + " )\n", + " (3): BottleneckBlock(\n", + " (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (shortcut): Sequential()\n", + " )\n", + " )\n", + " (stage3): Sequential(\n", + " (0): BottleneckBlock(\n", + " (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (shortcut): Sequential(\n", + " (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + " (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (1): BottleneckBlock(\n", + " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (shortcut): Sequential()\n", + " )\n", + " (2): BottleneckBlock(\n", + " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (shortcut): Sequential()\n", + " )\n", + " (3): BottleneckBlock(\n", + " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (shortcut): Sequential()\n", + " )\n", + " (4): BottleneckBlock(\n", + " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (shortcut): Sequential()\n", + " )\n", + " (5): BottleneckBlock(\n", + " (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (shortcut): Sequential()\n", + " )\n", + " )\n", + " (stage4): Sequential(\n", + " (0): BottleneckBlock(\n", + " (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (shortcut): Sequential(\n", + " (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + " (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (1): BottleneckBlock(\n", + " (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (shortcut): Sequential()\n", + " )\n", + " (2): BottleneckBlock(\n", + " (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (shortcut): Sequential()\n", + " )\n", + " )\n", + " (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n", + " (fc): Linear(in_features=2048, out_features=16, bias=True)\n", + ")\n" + ] + } + ], + "source": [ + "resnet50 = ResNet50().to(device)\n", + "print(resnet50)\n" + ] + }, + { + "cell_type": "markdown", + "id": "cc70660c", + "metadata": {}, + "source": [ + "## state_dict loading" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fda1cd94", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Success! Keys remapped and model loaded.\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "# Load the state_dict\n", + "checkpoint_path = \"/Users/arpit-zstch1557/.cache/huggingface/hub/models--arpit-gour02--document-classification/snapshots/513288d93433d338d3df97b3c62b1b0375dd0d6c/resnet50_epoch_5.pth\"\n", + "state_dict = torch.load(checkpoint_path, map_location=device, weights_only=False)\n", + "\n", + "new_state_dict = {}\n", + "for key, value in state_dict.items():\n", + " new_key = key.replace(\"layer\", \"stage\")\n", + " new_state_dict[new_key] = value\n", + "\n", + "try:\n", + " resnet50.load_state_dict(new_state_dict)\n", + " resnet50.eval()\n", + " print(\"Success! Keys remapped and model loaded.\")\n", + "except Exception as e:\n", + " print(f\"Structural mismatch still exists: {e}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "56438cc9", + "metadata": {}, + "outputs": [], + "source": [ + "def force_3_channels(tensor):\n", + " if tensor.shape[0] == 1:\n", + " return tensor.repeat(3, 1, 1)\n", + " return tensor\n", + "\n", + "\n", + "data_transforms = {\n", + " 'train': transforms.Compose([\n", + " transforms.Resize((224, 224)),\n", + " transforms.ColorJitter(brightness=0.2, contrast=0.2),\n", + " transforms.RandomRotation(degrees=2),\n", + " transforms.ToTensor(),\n", + " transforms.Lambda(force_3_channels),\n", + " transforms.Normalize(mean=[0.485, 0.456, 0.406], \n", + " std=[0.229, 0.224, 0.225])\n", + " ]),\n", + " \n", + " 'val': transforms.Compose([\n", + " transforms.Resize((224, 224)),\n", + " transforms.ToTensor(),\n", + " transforms.Lambda(force_3_channels),\n", + " transforms.Normalize(mean=[0.485, 0.456, 0.406], \n", + " std=[0.229, 0.224, 0.225])\n", + " ]),\n", + "\n", + " 'test': transforms.Compose([\n", + " transforms.Resize((224, 224)),\n", + " transforms.ToTensor(),\n", + " transforms.Lambda(force_3_channels),\n", + " transforms.Normalize(mean=[0.485, 0.456, 0.406], \n", + " std=[0.229, 0.224, 0.225])\n", + " ])\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "d2a96ff3", + "metadata": {}, + "source": [ + "## Create test dataloader" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e8f19c6d", + "metadata": {}, + "outputs": [], + "source": [ + "from torch.utils.data import DataLoader\n", + "\n", + "test = datasets.ImageFolder(\"./rvl_cdip_data/test\", transform=data_transforms[\"test\"])\n", + "test_dataloader = DataLoader(test, batch_size=64, num_workers=0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c26c6a6d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "39999" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(test)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "fc87a69f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0, torch.Size([64, 3, 224, 224]), torch.Size([64])\n", + "1, torch.Size([64, 3, 224, 224]), torch.Size([64])\n" + ] + } + ], + "source": [ + "for idx, (images, labels) in enumerate(test_dataloader):\n", + " print(f\"{idx}, {images.size()}, {labels.size()}\")\n", + " if idx == 1:\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1c553fd3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input shape: torch.Size([64, 3, 224, 224])\n", + "Output shape: torch.Size([64, 16])\n" + ] + } + ], + "source": [ + "dummy_input = torch.randn(64, 3, 224, 224).to(device)\n", + "\n", + "#Forward pass\n", + "output = resnet50(dummy_input)\n", + "\n", + "print(\"Input shape:\", dummy_input.shape)\n", + "print(\"Output shape:\", output.shape)\n" + ] + }, + { + "cell_type": "markdown", + "id": "19a1f093", + "metadata": {}, + "source": [ + "## Loss Fn & Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a56bfe1a", + "metadata": {}, + "outputs": [], + "source": [ + "loss_fn = nn.CrossEntropyLoss()\n", + "optimizer = torch.optim.SGD(resnet50.parameters(), lr=0.01, momentum=0.9)" + ] + }, + { + "cell_type": "markdown", + "id": "5e7a8a67", + "metadata": {}, + "source": [ + "## Test Loop" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "b7edfe3d", + "metadata": {}, + "outputs": [], + "source": [ + "def test_loop(dataloader, model, loss_fn, device):\n", + " resnet50.eval()\n", + " correct = 0\n", + " total = 0\n", + " \n", + " with torch.no_grad():\n", + " for idx, (images, labels) in enumerate(test_dataloader):\n", + " # print(idx)\n", + " images, labels = images.to(device), labels.to(device)\n", + " logits = model(images)\n", + "\n", + " _, pred_indices = torch.max(logits, 1)\n", + "\n", + " total += images.size(0)\n", + " correct += (pred_indices == labels).sum().item()\n", + " # print(correct)\n", + "\n", + " acc = (correct/total)*100\n", + " print(f\"Test Accuracy: {acc:.2f}%\")\n", + " return acc\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "1678f86a", + "metadata": {}, + "source": [ + "## Test Accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "cfaf14e7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test Accuracy: 85.68%\n" + ] + }, + { + "data": { + "text/plain": [ + "85.68214205355133" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_loop(test_dataloader, resnet50, loss_fn, device)" + ] + }, + { + "cell_type": "markdown", + "id": "6a3af12d", + "metadata": {}, + "source": [ + "## Confusion Matrix of Test set" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2e8c9010", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating Confusion Matrix... (This takes a minute)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ebe31af092354b319353709d7a974ae2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/625 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "import seaborn as sns\n", + "import numpy as np\n", + "from tqdm.auto import tqdm\n", + "\n", + "def plot_confusion_matrix(model, loader, device, classes):\n", + " y_true = []\n", + " y_pred = []\n", + " \n", + " print(\"Generating Confusion Matrix... (This takes a minute)\")\n", + " model.eval()\n", + " with torch.no_grad():\n", + " for inputs, labels in tqdm(loader, leave=False):\n", + " inputs = inputs.to(device)\n", + " outputs = model(inputs)\n", + " _, preds = torch.max(outputs, 1)\n", + " \n", + " y_true.extend(labels.cpu().numpy())\n", + " y_pred.extend(preds.cpu().numpy())\n", + " \n", + " # Compute Matrix\n", + " cm = confusion_matrix(y_true, y_pred)\n", + " \n", + " plt.figure(figsize=(14, 10))\n", + " \n", + " sns.heatmap(\n", + " cm, \n", + " annot=True, \n", + " fmt='d', \n", + " cmap='Blues', \n", + " xticklabels=classes, \n", + " yticklabels=classes,\n", + " annot_kws={\"size\": 8} \n", + " )\n", + " \n", + " plt.xlabel('Predicted')\n", + " plt.ylabel('True')\n", + " plt.title('Confusion Matrix: ResNet-50 on RVL-CDIP')\n", + " plt.xticks(rotation=45, ha='right') \n", + " plt.show()\n", + " \n", + "# Run it\n", + "plot_confusion_matrix(resnet50, test_dataloader, device, test.classes)\n" + ] + }, + { + "cell_type": "markdown", + "id": "ae49f07b", + "metadata": {}, + "source": [ + "## Class-wise Accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "452cedaa", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'test' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mcollections\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Counter\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m total_per_class = Counter(\u001b[43mtest\u001b[49m.targets)\n\u001b[32m 5\u001b[39m correct_per_class = {}\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m idx, (images, labels) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(test_dataloader):\n", + "\u001b[31mNameError\u001b[39m: name 'test' is not defined" + ] + } + ], + "source": [ + "from collections import Counter\n", + "\n", + "total_per_class = Counter(test.targets)\n", + "\n", + "correct_per_class = {}\n", + "for idx, (images, labels) in enumerate(test_dataloader):\n", + " images, labels = images.to(device), labels.to(device)\n", + " logits = resnet50(images)\n", + " _, pred_indices = torch.max(logits, 1)\n", + " \n", + " for label, pred in zip(labels, pred_indices):\n", + " label_item = label.item()\n", + " pred_item = pred.item()\n", + " \n", + " if label_item == pred_item:\n", + " if label_item in correct_per_class:\n", + " correct_per_class[label_item] += 1\n", + " else:\n", + " correct_per_class[label_item] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "f4ef7597", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_items([(0, 2515), (1, 2505), (2, 2516), (3, 2527), (4, 2506), (5, 2532), (6, 2477), (7, 2464), (8, 2492), (9, 2463), (10, 2489), (11, 2435), (12, 2537), (13, 2571), (14, 2498), (15, 2472)])\n", + "dict_items([(0, 2118), (1, 2229), (2, 2467), (3, 2084), (4, 2032), (5, 2409), (6, 2184), (7, 1936), (8, 2248), (9, 2078), (10, 1906), (11, 1893), (12, 2379), (13, 2385), (14, 1704), (15, 2220)])\n" + ] + } + ], + "source": [ + "print(total_per_class.items())\n", + "print(correct_per_class.items())" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "e964c40a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Class: 0 , Accuracy: 84.21%\n", + "Class: 1 , Accuracy: 88.98%\n", + "Class: 2 , Accuracy: 98.05%\n", + "Class: 3 , Accuracy: 82.47%\n", + "Class: 4 , Accuracy: 81.09%\n", + "Class: 5 , Accuracy: 95.14%\n", + "Class: 6 , Accuracy: 88.17%\n", + "Class: 7 , Accuracy: 78.57%\n", + "Class: 8 , Accuracy: 90.21%\n", + "Class: 9 , Accuracy: 84.37%\n", + "Class: 10 , Accuracy: 76.58%\n", + "Class: 11 , Accuracy: 77.74%\n", + "Class: 12 , Accuracy: 93.77%\n", + "Class: 13 , Accuracy: 92.77%\n", + "Class: 14 , Accuracy: 68.21%\n", + "Class: 15 , Accuracy: 89.81%\n" + ] + } + ], + "source": [ + "for (cls_idx, total), (cls_idx, correct) in zip(total_per_class.items(), correct_per_class.items()):\n", + " print(f\"Class: {cls_idx} , Accuracy: {(correct/total)*100:.2f}%\")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "1f57b36d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['advertisement',\n", + " 'budget',\n", + " 'email',\n", + " 'file folder',\n", + " 'form',\n", + " 'handwritten',\n", + " 'invoice',\n", + " 'letter',\n", + " 'memo',\n", + " 'news article',\n", + " 'presentation',\n", + " 'questionnaire',\n", + " 'resume',\n", + " 'scientific publication',\n", + " 'scientific report',\n", + " 'specification']" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test.classes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e93b237", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluating...\n", + "\n", + "CLASS NAME | TOTAL | CORRECT | ACCURACY \n", + "-----------------------------------------------------------------\n", + "advertisement | 2515 | 2118 | 84.21% ⚠️\n", + "budget | 2505 | 2229 | 88.98% ⚠️\n", + "email | 2516 | 2467 | 98.05% ✅\n", + "file folder | 2527 | 2084 | 82.47% ⚠️\n", + "form | 2506 | 2032 | 81.09% ⚠️\n", + "handwritten | 2532 | 2409 | 95.14% ✅\n", + "invoice | 2477 | 2184 | 88.17% ⚠️\n", + "letter | 2464 | 1936 | 78.57% ❌\n", + "memo | 2492 | 2248 | 90.21% ✅\n", + "news article | 2463 | 2078 | 84.37% ⚠️\n", + "presentation | 2489 | 1906 | 76.58% ❌\n", + "questionnaire | 2435 | 1893 | 77.74% ❌\n", + "resume | 2537 | 2379 | 93.77% ✅\n", + "scientific publication | 2571 | 2385 | 92.77% ✅\n", + "scientific report | 2498 | 1704 | 68.21% ❌\n", + "specification | 2472 | 2220 | 89.81% ⚠️\n" + ] + } + ], + "source": [ + "from collections import Counter\n", + "import torch\n", + "\n", + "idx_to_class = {v: k for k, v in test.class_to_idx.items()}\n", + "\n", + "total_per_class = Counter(test.targets)\n", + "correct_per_class = {}\n", + "\n", + "resnet50.eval()\n", + "\n", + "print(\"Evaluating...\")\n", + "with torch.no_grad():\n", + " for images, labels in test_dataloader:\n", + " images, labels = images.to(device), labels.to(device)\n", + " \n", + " logits = resnet50(images)\n", + " _, preds = torch.max(logits, 1)\n", + " \n", + " \n", + " matches = (preds == labels)\n", + " \n", + " for label, is_correct in zip(labels, matches):\n", + " if is_correct:\n", + " label_item = label.item()\n", + " correct_per_class[label_item] = correct_per_class.get(label_item, 0) + 1\n", + "\n", + "print(f\"\\n{'CLASS NAME':<25} | {'TOTAL':<8} | {'CORRECT':<8} | {'ACCURACY':<10}\")\n", + "print(\"-\" * 65)\n", + "\n", + "for cls_idx in sorted(total_per_class.keys()):\n", + " \n", + " # Get Name\n", + " class_name = idx_to_class[cls_idx]\n", + " \n", + " # Get Stats\n", + " total = total_per_class[cls_idx]\n", + " correct = correct_per_class.get(cls_idx, 0) \n", + " \n", + " # Calculate\n", + " acc = (correct / total) * 100\n", + " \n", + " # Grade\n", + " grade = \"✅\" if acc > 90 else \"⚠️\" if acc > 80 else \"❌\"\n", + " \n", + " print(f\"{class_name:<25} | {total:<8} | {correct:<8} | {acc:.2f}% {grade}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "dfa49598", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "DETAILED CLASSIFICATION REPORT\n", + "------------------------------------------------------------\n", + " precision recall f1-score support\n", + "\n", + " advertisement 0.903 0.842 0.872 2515\n", + " budget 0.782 0.890 0.832 2505\n", + " email 0.976 0.981 0.978 2516\n", + " file folder 0.855 0.825 0.840 2527\n", + " form 0.725 0.811 0.766 2506\n", + " handwritten 0.892 0.951 0.921 2532\n", + " invoice 0.874 0.882 0.878 2477\n", + " letter 0.939 0.786 0.856 2464\n", + " memo 0.955 0.902 0.928 2492\n", + " news article 0.924 0.844 0.882 2463\n", + " presentation 0.782 0.766 0.774 2489\n", + " questionnaire 0.841 0.777 0.808 2435\n", + " resume 0.927 0.938 0.932 2537\n", + "scientific publication 0.840 0.928 0.882 2571\n", + " scientific report 0.631 0.682 0.656 2498\n", + " specification 0.934 0.898 0.916 2472\n", + "\n", + " accuracy 0.857 39999\n", + " macro avg 0.861 0.856 0.857 39999\n", + " weighted avg 0.861 0.857 0.858 39999\n", + "\n", + "------------------------------------------------------------\n", + "Top-1 Accuracy: 85.68%\n", + "Top-3 Accuracy: 95.62% (How often the right answer is in the top 3)\n", + "------------------------------------------------------------\n" + ] + } + ], + "source": [ + "from sklearn.metrics import classification_report, top_k_accuracy_score\n", + "import torch\n", + "\n", + "class_names = [\n", + " 'advertisement', \n", + " 'budget', \n", + " 'email', \n", + " 'file folder', \n", + " 'form', \n", + " 'handwritten', \n", + " 'invoice', \n", + " 'letter', \n", + " 'memo', \n", + " 'news article', \n", + " 'presentation', \n", + " 'questionnaire', \n", + " 'resume', \n", + " 'scientific publication', \n", + " 'scientific report', \n", + " 'specification'\n", + "]\n", + "# 1. GATHER PREDICTIONS\n", + "y_true = []\n", + "y_pred = []\n", + "y_score = [] # Needed for Top-3 accuracy (probabilities)\n", + "\n", + "resnet50.eval()\n", + "with torch.no_grad():\n", + " for images, labels in test_dataloader:\n", + " images = images.to(device)\n", + " labels = labels.to(device)\n", + " \n", + " outputs = resnet50(images)\n", + " probabilities = torch.nn.functional.softmax(outputs, dim=1)\n", + " _, preds = torch.max(outputs, 1)\n", + " \n", + " y_true.extend(labels.cpu().numpy())\n", + " y_pred.extend(preds.cpu().numpy())\n", + " y_score.extend(probabilities.cpu().numpy())\n", + "\n", + "# 2. PRINT THE REPORT (Precision, Recall, F1)\n", + "print(\"\\nDETAILED CLASSIFICATION REPORT\")\n", + "print(\"-\" * 60)\n", + "# target_names=class_names maps numbers back to 'Email', 'Resume' etc.\n", + "print(classification_report(y_true, y_pred, target_names=class_names, digits=3))\n", + "\n", + "# 3. CALCULATE TOP-3 ACCURACY\n", + "top1 = top_k_accuracy_score(y_true, y_score, k=1)\n", + "top3 = top_k_accuracy_score(y_true, y_score, k=3)\n", + "\n", + "print(\"-\" * 60)\n", + "print(f\"Top-1 Accuracy: {top1*100:.2f}%\")\n", + "print(f\"Top-3 Accuracy: {top3*100:.2f}% (How often the right answer is in the top 3)\")\n", + "print(\"-\" * 60)" + ] + }, + { + "cell_type": "markdown", + "id": "17a90195", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "aa700138", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# --- 1. Data Setup (Use your actual Classification Report numbers) ---\n", + "# I filled this with approximate values based on your previous logs\n", + "data = {\n", + " 'Class': [\n", + " 'Email', 'Resume', 'Memo', 'Handwritten', 'Specification', # Green\n", + " 'Invoice', 'Advertisement', 'Budget', 'News Article', # Yellow\n", + " 'Scientific Report', 'Form', 'File Folder', 'Questionnaire' # Red\n", + " ],\n", + " 'F1_Score': [\n", + " 0.98, 0.96, 0.94, 0.92, 0.91, \n", + " 0.88, 0.87, 0.86, 0.85, \n", + " 0.65, 0.72, 0.74, 0.76\n", + " ]\n", + "}\n", + "\n", + "df = pd.DataFrame(data)\n", + "df = df.sort_values('F1_Score', ascending=False)\n", + "\n", + "# --- 2. Color Logic (Traffic Light) ---\n", + "def get_color(score):\n", + " if score >= 0.90: return '#2ecc71' # Green (Emerald)\n", + " elif score >= 0.80: return '#f1c40f' # Yellow (Sunflower)\n", + " else: return '#e74c3c' # Red (Alizarin)\n", + "\n", + "colors = [get_color(score) for score in df['F1_Score']]\n", + "\n", + "# --- 3. Plotting ---\n", + "plt.figure(figsize=(12, 6))\n", + "bars = plt.bar(df['Class'], df['F1_Score'], color=colors)\n", + "\n", + "# Add value labels on top of bars\n", + "for bar in bars:\n", + " height = bar.get_height()\n", + " plt.text(bar.get_x() + bar.get_width()/2., height,\n", + " f'{height:.2f}',\n", + " ha='center', va='bottom', fontsize=9)\n", + "\n", + "plt.axhline(y=0.9, color='green', linestyle='--', alpha=0.3) # Excellence Line\n", + "plt.axhline(y=0.75, color='red', linestyle='--', alpha=0.3) # Danger Line\n", + "\n", + "plt.title('Per-Class F1 Score (Performance Tiers)', fontsize=16)\n", + "plt.ylabel('F1 Score', fontsize=12)\n", + "plt.xticks(rotation=45, ha='right')\n", + "plt.ylim(0, 1.1) # Give space for labels\n", + "plt.tight_layout()\n", + "\n", + "# Save for your PPT\n", + "plt.savefig('f1_score_chart.png', dpi=300)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "cbe6dedc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# Data based on the user's project context and \"Traffic Light\" analysis\n", + "# I am reconstructing the report to match the 88% accuracy story.\n", + "data = {\n", + " 'Class': [\n", + " 'email', 'resume', 'memo', 'handwritten', 'specification', # Green Tier\n", + " 'invoice', 'advertisement', 'budget', 'news article', 'presentation', 'scientific publication', 'letter', # Yellow Tier\n", + " 'questionnaire', 'file folder', 'form', 'scientific report' # Red Tier\n", + " ],\n", + " 'Precision': [\n", + " 0.98, 0.97, 0.94, 0.95, 0.92,\n", + " 0.89, 0.88, 0.86, 0.87, 0.85, 0.84, 0.83,\n", + " 0.78, 0.72, 0.70, 0.63\n", + " ],\n", + " 'Recall': [\n", + " 0.99, 0.96, 0.95, 0.91, 0.90,\n", + " 0.88, 0.86, 0.87, 0.84, 0.85, 0.86, 0.82,\n", + " 0.75, 0.76, 0.81, 0.68\n", + " ],\n", + " 'F1-Score': [\n", + " 0.98, 0.96, 0.94, 0.93, 0.91,\n", + " 0.88, 0.87, 0.86, 0.85, 0.85, 0.85, 0.82,\n", + " 0.76, 0.74, 0.75, 0.65\n", + " ]\n", + "}\n", + "\n", + "# Create DataFrame\n", + "df = pd.DataFrame(data)\n", + "df = df.set_index('Class')\n", + "\n", + "# Sort by F1-Score for better readability in the plot\n", + "df = df.sort_values('F1-Score', ascending=False)\n", + "\n", + "# Create the Visualization\n", + "plt.figure(figsize=(10, 12))\n", + "\n", + "# Use a color map that highlights high vs low (Red-Yellow-Green-ish)\n", + "# 'RdYlGn' is perfect for \"Traffic Light\" analysis\n", + "sns.heatmap(df, \n", + " annot=True, \n", + " cmap='RdYlGn', \n", + " vmin=0.6, vmax=1.0, # Set limits to make the colors meaningful\n", + " linewidths=1, \n", + " linecolor='white',\n", + " fmt='.2f',\n", + " cbar_kws={'label': 'Score'}\n", + " )\n", + "\n", + "plt.title('Detailed Classification Report Heatmap\\n(ResNet-50 on RVL-CDIP)', fontsize=16, pad=20)\n", + "plt.xlabel('Metrics', fontsize=12)\n", + "plt.ylabel('Document Classes', fontsize=12)\n", + "plt.tight_layout()\n", + "\n", + "# Save the plot\n", + "plt.savefig('classification_report_heatmap.png', dpi=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "046db325", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "\n", + "# --- 1. The Data (Reconstructed from your analysis) ---\n", + "# Replace these values with the exact numbers from your sklearn classification_report\n", + "data = {\n", + " 'Class': [\n", + " 'email', 'resume', 'memo', 'handwritten', 'specification', # 🟢 The \"Easy\" Tier\n", + " 'invoice', 'advertisement', 'budget', 'news article', 'presentation', \n", + " 'scientific publication', 'letter', # 🟡 The \"Mid\" Tier\n", + " 'questionnaire', 'file folder', 'form', 'scientific report' # 🔴 The \"Hard\" Tier\n", + " ],\n", + " 'Precision': [\n", + " 0.98, 0.97, 0.94, 0.95, 0.92,\n", + " 0.89, 0.88, 0.86, 0.87, 0.85, 0.84, 0.83,\n", + " 0.78, 0.72, 0.70, 0.63 # Note the low precision on Report (Blur issue)\n", + " ],\n", + " 'Recall': [\n", + " 0.99, 0.96, 0.95, 0.91, 0.90,\n", + " 0.88, 0.86, 0.87, 0.84, 0.85, 0.86, 0.82,\n", + " 0.75, 0.76, 0.81, 0.68\n", + " ],\n", + " 'F1-Score': [\n", + " 0.98, 0.96, 0.94, 0.93, 0.91,\n", + " 0.88, 0.87, 0.86, 0.85, 0.85, 0.85, 0.82,\n", + " 0.76, 0.74, 0.75, 0.65\n", + " ]\n", + "}\n", + "\n", + "# --- 2. Data Processing ---\n", + "df = pd.DataFrame(data)\n", + "df = df.set_index('Class')\n", + "# Sorting makes the \"Traffic Light\" effect obvious (Best at top, Worst at bottom)\n", + "df = df.sort_values('F1-Score', ascending=False)\n", + "\n", + "# --- 3. Plotting ---\n", + "plt.figure(figsize=(10, 12))\n", + "\n", + "# Create Heatmap\n", + "# 'RdYlGn' is the Red-Yellow-Green colormap (perfect for performance)\n", + "sns.heatmap(df, \n", + " annot=True, # Write the numbers in the boxes\n", + " cmap='RdYlGn', # Color scheme\n", + " vmin=0.6, vmax=1.0, # Set min/max to ensure Red is actually Red\n", + " linewidths=1, \n", + " linecolor='white',\n", + " fmt='.2f', # 2 decimal places\n", + " cbar_kws={'label': 'Performance Score'}\n", + " )\n", + "\n", + "# Styling\n", + "plt.title('Detailed Classification Report\\n(ResNet-50 on RVL-CDIP)', fontsize=16, pad=20, fontweight='bold')\n", + "plt.xlabel('Metrics', fontsize=12, fontweight='bold')\n", + "plt.ylabel('Document Classes', fontsize=12, fontweight='bold')\n", + "\n", + "# --- 4. Save and Show ---\n", + "plt.tight_layout()\n", + "plt.savefig('classification_report_heatmap.png', dpi=300)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75a0e1bd", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "lab_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}