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{"cells":[{"cell_type":"markdown","metadata":{"id":"JmYRMwEOYkbU"},"source":["# `Deepfake Classification - ViT`"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"c6rbUun0tdC5","outputId":"066a743c-d35f-41d8-c773-9db21d0b7da8","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1762229521609,"user_tz":-540,"elapsed":41533,"user":{"displayName":"변해민","userId":"17789843679127612047"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting evaluate\n"," Downloading evaluate-0.4.6-py3-none-any.whl.metadata (9.5 kB)\n","Requirement already satisfied: datasets in /usr/local/lib/python3.12/dist-packages (4.0.0)\n","Requirement already satisfied: accelerate in /usr/local/lib/python3.12/dist-packages (1.11.0)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from evaluate) (2.0.2)\n","Requirement already satisfied: dill in /usr/local/lib/python3.12/dist-packages (from 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https://github.com/huggingface/transformers.git to /tmp/pip-req-build-4ge6zrso\n"," Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-req-build-4ge6zrso\n"," Resolved https://github.com/huggingface/transformers.git to commit 64397a830188a35b5e9056036e13bb6ccfe434d8\n"," Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n"," Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n"," Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from transformers==5.0.0.dev0) (3.20.0)\n","Collecting huggingface-hub==1.0.0.rc6 (from transformers==5.0.0.dev0)\n"," Downloading huggingface_hub-1.0.0rc6-py3-none-any.whl.metadata (14 kB)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from transformers==5.0.0.dev0) (2.0.2)\n","Requirement already satisfied: packaging>=20.0 in 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in directory: /tmp/pip-ephem-wheel-cache-4hqgm5ed/wheels/54/cb/3f/83103de5575c534436d6a4686686dead458238dfaf1147e98d\n","Successfully built transformers\n","Installing collected packages: typer-slim, huggingface-hub, transformers\n"," Attempting uninstall: huggingface-hub\n"," Found existing installation: huggingface-hub 0.36.0\n"," Uninstalling huggingface-hub-0.36.0:\n"," Successfully uninstalled huggingface-hub-0.36.0\n"," Attempting uninstall: transformers\n"," Found existing installation: transformers 4.57.1\n"," Uninstalling transformers-4.57.1:\n"," Successfully uninstalled transformers-4.57.1\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","sentence-transformers 5.1.2 requires transformers<5.0.0,>=4.41.0, but you have transformers 5.0.0.dev0 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed huggingface-hub-1.0.0rc6 transformers-5.0.0.dev0 typer-slim-0.20.0\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["huggingface_hub"]},"id":"a8b8d20b0edd456693c40bc4fbb916c2"}},"metadata":{}}],"source":["!pip install evaluate datasets accelerate\n","!pip install git+https://github.com/huggingface/transformers.git"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dhLosa2Utm5M"},"outputs":[],"source":["import warnings\n","warnings.filterwarnings(\"ignore\")\n","\n","import gc\n","import numpy as np\n","import pandas as pd\n","import itertools\n","from collections import Counter\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import accuracy_score, roc_auc_score, confusion_matrix, classification_report, f1_score\n","from imblearn.over_sampling import RandomOverSampler\n","import evaluate\n","from datasets import Dataset, Image, ClassLabel\n","from transformers import (\n"," TrainingArguments,\n"," Trainer,\n"," ViTImageProcessor,\n"," ViTForImageClassification,\n"," DefaultDataCollator\n",")\n","import torch\n","from torch.utils.data import DataLoader\n","from torchvision.transforms import (\n"," CenterCrop,\n"," Compose,\n"," Normalize,\n"," RandomRotation,\n"," RandomResizedCrop,\n"," RandomHorizontalFlip,\n"," RandomAdjustSharpness,\n"," Resize,\n"," ToTensor\n",")\n","from PIL import Image as PILImage\n","from PIL import ImageFile\n","\n","# Enable loading truncated images\n","ImageFile.LOAD_TRUNCATED_IMAGES = True"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"20RJuU8_uY2k","executionInfo":{"status":"error","timestamp":1762238553711,"user_tz":-540,"elapsed":46,"user":{"displayName":"변해민","userId":"17789843679127612047"}},"colab":{"base_uri":"https://localhost:8080/","height":444},"outputId":"4ce8d5a8-c0ef-44bd-8f34-fc21498700ef"},"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"Couldn't find any data file at /content/AIImage12345/AI_images/train/real.","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipython-input-918001142.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"AIImage12345/AI_images/train/real\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# 가짜 레이블 붙이기 (예시)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"label\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 전부 라벨 1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/datasets/load.py\u001b[0m in \u001b[0;36mload_dataset\u001b[0;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, **config_kwargs)\u001b[0m\n\u001b[1;32m 1390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1391\u001b[0m \u001b[0;31m# Create a dataset builder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1392\u001b[0;31m builder_instance = load_dataset_builder(\n\u001b[0m\u001b[1;32m 1393\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1394\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/datasets/load.py\u001b[0m in \u001b[0;36mload_dataset_builder\u001b[0;34m(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, storage_options, **config_kwargs)\u001b[0m\n\u001b[1;32m 1130\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfeatures\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1131\u001b[0m \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_fix_for_backward_compatible_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1132\u001b[0;31m dataset_module = dataset_module_factory(\n\u001b[0m\u001b[1;32m 1133\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1134\u001b[0m \u001b[0mrevision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrevision\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/datasets/load.py\u001b[0m in \u001b[0;36mdataset_module_factory\u001b[0;34m(path, revision, download_config, download_mode, data_dir, data_files, cache_dir, **download_kwargs)\u001b[0m\n\u001b[1;32m 1031\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1032\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1033\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Couldn't find any data file at {relative_to_absolute_path(path)}.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1034\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1035\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: Couldn't find any data file at /content/AIImage12345/AI_images/train/real."]}],"source":["from datasets import load_dataset\n","dataset = load_dataset(\"AIImage12345/AI_images/train/real\")\n","\n","# 가짜 레이블 붙이기 (예시)\n","dataset = dataset.add_column(\"label\", [1] * len(dataset)) # 전부 라벨 1\n"]},{"cell_type":"code","source":["from datasets import load_dataset, Features, Value, ClassLabel, Image\n","\n","# 1. Hugging Face 리포지토리 ID\n","repo_id = \"AIImage12345/AI_images\"\n","\n","try:\n"," # 1. 'csv' 빌더를 사용하여 train.csv를 로드합니다.\n"," dataset = load_dataset(\n"," 'csv',\n"," data_files={\n"," 'train': 'train.csv' # <--- 루트에 있는 CSV 파일 지정\n"," },\n"," data_dir=repo_id,\n"," name=\"ai_images_dataset\",\n"," split='train',\n"," # use_auth_token=True # Private이라면 주석 해제\n"," )\n","\n"," # 2. 로드된 CSV 데이터셋의 컬럼을 이미지 타입으로 변환합니다.\n"," features = dataset.features.copy()\n","\n"," # CSV의 'image_file_path' 컬럼이 리포지토리 루트에 있는 이미지 파일을 참조하도록 설정\n"," features['image_file_path'] = Image()\n","\n"," # 라벨을 'fake', 'real'로 설정 (train.csv에 이 두 라벨이 있다고 가정)\n"," features['label'] = ClassLabel(names=['fake', 'real'])\n","\n"," dataset = dataset.cast(features)\n","\n"," # 3. 컬럼 이름을 표준 'image'로 변경\n"," dataset = dataset.rename_column('image_file_path', 'image')\n","\n"," print(\"🎉 데이터셋 로드 성공! (CSV 루트 파일 참조)\")\n"," print(dataset)\n","\n"," label_names = dataset.features['label'].names\n"," print(f\"\\n✅ 로드된 라벨 이름: {label_names}\")\n"," print(f\"첫 번째 데이터 포인트의 'image' 컬럼 타입: {type(dataset[0]['image'])}\")\n","\n","except Exception as e:\n"," print(f\"❌ 데이터셋 로드 실패: {e}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lE0eluHKjLJh","executionInfo":{"status":"ok","timestamp":1762239222108,"user_tz":-540,"elapsed":813,"user":{"displayName":"변해민","userId":"17789843679127612047"}},"outputId":"3e8f9f1f-0f29-4bfa-dcf1-00d9f5be7c46"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["❌ 데이터셋 로드 실패: Unable to find '/content/AIImage12345/AI_images/train.csv'\n"]}]},{"cell_type":"code","source":["print(dataset)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KHQNeDfYDotF","executionInfo":{"status":"ok","timestamp":1762229633815,"user_tz":-540,"elapsed":19,"user":{"displayName":"변해민","userId":"17789843679127612047"}},"outputId":"5ce106c9-1ee2-4df9-dca0-eef5e5693df9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Dataset({\n"," features: ['image', 'label'],\n"," num_rows: 64\n","})\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"o8rgwG0nuc00"},"outputs":[],"source":["file_names = []\n","labels = []\n","\n","for example in dataset:\n"," file_path = example['image'].filename # Get the file path\n"," label = example['label'] # Get the label\n"," file_names.append(file_path) # Add the file path to the list\n"," labels.append(label) # Add the label to the list"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Yz8qs87tuhjs","outputId":"7ef9dfa3-efe4-4669-d48c-c75a3c4dbd37","executionInfo":{"status":"ok","timestamp":1762229634211,"user_tz":-540,"elapsed":8,"user":{"displayName":"변해민","userId":"17789843679127612047"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["64 64\n"]}],"source":["# Print the total number of file names and labels\n","print(len(file_names), len(labels))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7CW5l8Td_V-4"},"outputs":[],"source":["# Create a pandas dataframe from the collected file names and labels\n","df = pd.DataFrame.from_dict({\"image\": file_names, \"label\": labels})"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9SZz49oNBSHf","outputId":"7e7e3c15-f475-4e4a-e4e2-f80f4f2a503e","executionInfo":{"status":"ok","timestamp":1762229634331,"user_tz":-540,"elapsed":30,"user":{"displayName":"변해민","userId":"17789843679127612047"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["(64, 2)\n"]}],"source":["print(df.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"ZubCrfrhBZGo","outputId":"4e4d5658-0dcf-47eb-e4e7-c7a71d7c4080","executionInfo":{"status":"ok","timestamp":1762229635705,"user_tz":-540,"elapsed":12,"user":{"displayName":"변해민","userId":"17789843679127612047"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":[" image label\n","0 1\n","1 1\n","2 1\n","3 1\n","4 1"],"text/html":["\n"," <div id=\"df-b7e513dc-1997-4fa4-a3f6-ce6b1cce60a3\" class=\"colab-df-container\">\n"," <div>\n","<style scoped>\n"," .dataframe tbody tr th:only-of-type {\n"," vertical-align: middle;\n"," }\n","\n"," .dataframe tbody tr th {\n"," vertical-align: top;\n"," }\n","\n"," .dataframe thead th {\n"," text-align: right;\n"," }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: right;\">\n"," <th></th>\n"," <th>image</th>\n"," <th>label</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td></td>\n"," <td>1</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td></td>\n"," <td>1</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td></td>\n"," <td>1</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td></td>\n"," <td>1</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td></td>\n"," <td>1</td>\n"," </tr>\n"," </tbody>\n","</table>\n","</div>\n"," <div class=\"colab-df-buttons\">\n","\n"," <div class=\"colab-df-container\">\n"," <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b7e513dc-1997-4fa4-a3f6-ce6b1cce60a3')\"\n"," title=\"Convert this dataframe to an interactive table.\"\n"," style=\"display:none;\">\n","\n"," <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n"," <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n"," </svg>\n"," </button>\n","\n"," <style>\n"," .colab-df-container {\n"," display:flex;\n"," gap: 12px;\n"," }\n","\n"," .colab-df-convert {\n"," background-color: #E8F0FE;\n"," border: none;\n"," border-radius: 50%;\n"," cursor: pointer;\n"," display: none;\n"," fill: #1967D2;\n"," height: 32px;\n"," padding: 0 0 0 0;\n"," width: 32px;\n"," }\n","\n"," .colab-df-convert:hover {\n"," background-color: #E2EBFA;\n"," box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n"," fill: #174EA6;\n"," }\n","\n"," .colab-df-buttons div {\n"," margin-bottom: 4px;\n"," }\n","\n"," [theme=dark] .colab-df-convert {\n"," background-color: #3B4455;\n"," fill: #D2E3FC;\n"," }\n","\n"," [theme=dark] .colab-df-convert:hover {\n"," background-color: #434B5C;\n"," box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n"," filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n"," fill: #FFFFFF;\n"," }\n"," </style>\n","\n"," <script>\n"," const buttonEl =\n"," document.querySelector('#df-b7e513dc-1997-4fa4-a3f6-ce6b1cce60a3 button.colab-df-convert');\n"," buttonEl.style.display =\n"," google.colab.kernel.accessAllowed ? 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Visit the ' +\n"," '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n"," + ' to learn more about interactive tables.';\n"," element.innerHTML = '';\n"," dataTable['output_type'] = 'display_data';\n"," await google.colab.output.renderOutput(dataTable, element);\n"," const docLink = document.createElement('div');\n"," docLink.innerHTML = docLinkHtml;\n"," element.appendChild(docLink);\n"," }\n"," </script>\n"," </div>\n","\n","\n"," <div id=\"df-93498a37-52a7-4861-9ea7-a1b7f2b89558\">\n"," <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-93498a37-52a7-4861-9ea7-a1b7f2b89558')\"\n"," title=\"Suggest charts\"\n"," style=\"display:none;\">\n","\n","<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n"," width=\"24px\">\n"," <g>\n"," <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n"," </g>\n","</svg>\n"," </button>\n","\n","<style>\n"," .colab-df-quickchart {\n"," --bg-color: #E8F0FE;\n"," --fill-color: #1967D2;\n"," --hover-bg-color: #E2EBFA;\n"," --hover-fill-color: #174EA6;\n"," --disabled-fill-color: #AAA;\n"," --disabled-bg-color: #DDD;\n"," }\n","\n"," [theme=dark] .colab-df-quickchart {\n"," --bg-color: #3B4455;\n"," --fill-color: #D2E3FC;\n"," --hover-bg-color: #434B5C;\n"," --hover-fill-color: #FFFFFF;\n"," --disabled-bg-color: #3B4455;\n"," --disabled-fill-color: #666;\n"," }\n","\n"," .colab-df-quickchart {\n"," background-color: var(--bg-color);\n"," border: none;\n"," border-radius: 50%;\n"," cursor: pointer;\n"," display: none;\n"," fill: var(--fill-color);\n"," height: 32px;\n"," padding: 0;\n"," width: 32px;\n"," }\n","\n"," .colab-df-quickchart:hover {\n"," background-color: var(--hover-bg-color);\n"," box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n"," fill: var(--button-hover-fill-color);\n"," }\n","\n"," .colab-df-quickchart-complete:disabled,\n"," .colab-df-quickchart-complete:disabled:hover {\n"," background-color: var(--disabled-bg-color);\n"," fill: var(--disabled-fill-color);\n"," box-shadow: none;\n"," }\n","\n"," .colab-df-spinner {\n"," border: 2px solid var(--fill-color);\n"," border-color: transparent;\n"," border-bottom-color: var(--fill-color);\n"," animation:\n"," spin 1s steps(1) infinite;\n"," }\n","\n"," @keyframes spin {\n"," 0% {\n"," border-color: transparent;\n"," border-bottom-color: var(--fill-color);\n"," border-left-color: var(--fill-color);\n"," }\n"," 20% {\n"," border-color: transparent;\n"," border-left-color: var(--fill-color);\n"," border-top-color: var(--fill-color);\n"," }\n"," 30% {\n"," border-color: transparent;\n"," border-left-color: var(--fill-color);\n"," border-top-color: var(--fill-color);\n"," border-right-color: var(--fill-color);\n"," }\n"," 40% {\n"," border-color: transparent;\n"," border-right-color: var(--fill-color);\n"," border-top-color: var(--fill-color);\n"," }\n"," 60% {\n"," border-color: transparent;\n"," border-right-color: var(--fill-color);\n"," }\n"," 80% {\n"," border-color: transparent;\n"," border-right-color: var(--fill-color);\n"," border-bottom-color: var(--fill-color);\n"," }\n"," 90% {\n"," border-color: transparent;\n"," border-bottom-color: var(--fill-color);\n"," }\n"," }\n","</style>\n","\n"," <script>\n"," async function quickchart(key) {\n"," const quickchartButtonEl =\n"," document.querySelector('#' + key + ' button');\n"," quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n"," quickchartButtonEl.classList.add('colab-df-spinner');\n"," try {\n"," const charts = await google.colab.kernel.invokeFunction(\n"," 'suggestCharts', [key], {});\n"," } catch (error) {\n"," console.error('Error during call to suggestCharts:', error);\n"," }\n"," quickchartButtonEl.classList.remove('colab-df-spinner');\n"," quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n"," }\n"," (() => {\n"," let quickchartButtonEl =\n"," document.querySelector('#df-93498a37-52a7-4861-9ea7-a1b7f2b89558 button');\n"," quickchartButtonEl.style.display =\n"," google.colab.kernel.accessAllowed ? 'block' : 'none';\n"," })();\n"," </script>\n"," </div>\n","\n"," </div>\n"," </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"df","summary":"{\n \"name\": \"df\",\n \"rows\": 64,\n \"fields\": [\n {\n \"column\": \"image\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 1,\n \"max\": 1,\n \"num_unique_values\": 1,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"}},"metadata":{},"execution_count":8}],"source":["df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oFwJ-2_B_br5","outputId":"103b1047-1612-48be-8d51-9fa960886407","executionInfo":{"status":"ok","timestamp":1762229635863,"user_tz":-540,"elapsed":70,"user":{"displayName":"변해민","userId":"17789843679127612047"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1])"]},"metadata":{},"execution_count":9}],"source":["df['label'].unique()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":389},"id":"ZzX_P-onunr7","outputId":"a673aca1-e618-467f-e085-ce468b66b295","executionInfo":{"status":"error","timestamp":1762229636020,"user_tz":-540,"elapsed":13,"user":{"displayName":"변해민","userId":"17789843679127612047"}}},"outputs":[{"output_type":"error","ename":"ValueError","evalue":"The target 'y' needs to have more than 1 class. Got 1 class instead","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipython-input-2031916903.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'label'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mros\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomOverSampler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m83\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_resampled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mros\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_resample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'label'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_resampled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/imblearn/base.py\u001b[0m in \u001b[0;36mfit_resample\u001b[0;34m(self, X, y, **params)\u001b[0m\n\u001b[1;32m 200\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mcorresponding\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0mof\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mX_resampled\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 201\u001b[0m \"\"\"\n\u001b[0;32m--> 202\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_resample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 204\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_more_tags\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1387\u001b[0m )\n\u001b[1;32m 1388\u001b[0m ):\n\u001b[0;32m-> 1389\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfit_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1391\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/imblearn/base.py\u001b[0m in \u001b[0;36mfit_resample\u001b[0;34m(self, X, y, **params)\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbinarize_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m self.sampling_strategy_ = check_sampling_strategy(\n\u001b[0m\u001b[1;32m 102\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msampling_strategy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sampling_type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 103\u001b[0m )\n","\u001b[0;32m/usr/local/lib/python3.12/dist-packages/imblearn/utils/_validation.py\u001b[0m in \u001b[0;36mcheck_sampling_strategy\u001b[0;34m(sampling_strategy, y, sampling_type, **kwargs)\u001b[0m\n\u001b[1;32m 533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 534\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 535\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 536\u001b[0m \u001b[0;34m\"The target 'y' needs to have more than 1 class. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 537\u001b[0m \u001b[0;34mf\"Got {np.unique(y).size} class instead\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: The target 'y' needs to have more than 1 class. Got 1 class instead"]}],"source":["y = df[['label']]\n","df = df.drop(['label'], axis=1)\n","ros = RandomOverSampler(random_state=83)\n","df, y_resampled = ros.fit_resample(df, y)\n","del y\n","df['label'] = y_resampled\n","del y_resampled\n","gc.collect()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WaJ_C30L_N_L"},"outputs":[],"source":["import pandas as pd\n","\n","# Create a DataFrame from the collected file names and labels\n","df = pd.DataFrame.from_dict({\"image\": file_names, \"label\": labels})"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ha4Bpgoz7dfu"},"outputs":[],"source":["dataset[10][\"image\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-MfUvn2A-tBc","outputId":"d759e0be-5d53-41db-e4e6-b4f4e7f6fc9b"},"outputs":[{"output_type":"stream","name":"stdout","text":["[0, 0, 0, 0, 0]\n"]}],"source":["labels_subset = labels[:5]\n","\n","# Printing the subset of labels to inspect the content.\n","print(labels_subset)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9u1W0MBhBpMA"},"outputs":[],"source":["# Define the list of unique labels\n","labels_list = ['Realism', 'Deepfake']\n","\n","# Initialize dictionaries to map labels to IDs and vice versa\n","label2id, id2label = dict(), dict()\n","for i, label in enumerate(labels_list):\n"," label2id[label] = i\n"," id2label[i] = label\n","\n","# Create ClassLabels object\n","ClassLabels = ClassLabel(num_classes=len(labels_list), names=labels_list)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4CfU5GJkByam","outputId":"4b574301-b814-4d6c-eddb-40c3b249e8f9"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mapping of IDs to Labels: {0: 'Realism', 1: 'Deepfake'} \n","\n","Mapping of Labels to IDs: {'Realism': 0, 'Deepfake': 1}\n"]}],"source":["# Print the resulting dictionaries for reference\n","print(\"Mapping of IDs to Labels:\", id2label, '\\n')\n","print(\"Mapping of Labels to IDs:\", label2id)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["ff5d03a5e9d54811800161e21dd2dd12","eb53be2ed3a14b26bc3b493c2ca35e86","4710ac0613994e2db6d4e8148089946a","d530164b2e244100acf68446dd3743ba","abadbf366a2240889d9708f61e50066b","5f32f51e829845a885530ec403220678","eb90d6438b0845719913b6f143172d12","da6f2bfbde864756a13866a09115e8c2","a9ac614517e947e4b167e82ba1a5701c","828bdece7426436fa86247a7eadb461f","a56175af22384734aa96ca9d5aa68edf","ada586a0f4db41a3a40f0e7b8479d8e6","e7043d60b0514f54a525262216104d3b","71017d29dc214dc5936c8cdda719f259","585eaf54fbf144e597ab6c8529494cb7","ec552938453246069d0877033b93ee1e","455c66d82a27415486a8c9fdd1b60342","a6931cf4219142c3ac2ef26269ea3bfe","6c10be6fa6cf44b992a5f0ccb6db7692","d6197b0b1ba9492e9d166198bc6b48b7","9eed55f2beec4553943cb8ba2e6195d2","e56b01211eda4a8ca6d737d50235568d"]},"id":"M9XI2VNYB35G","outputId":"1f616bfc-9f6c-4a88-f137-29ee415a9870"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/140002 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ff5d03a5e9d54811800161e21dd2dd12"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Casting the dataset: 0%| | 0/140002 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ada586a0f4db41a3a40f0e7b8479d8e6"}},"metadata":{}}],"source":["\n","# Mapping labels to IDs\n","def map_label2id(example):\n"," example['label'] = ClassLabels.str2int(example['label'])\n"," return example\n","\n","dataset = dataset.map(map_label2id, batched=True)\n","\n","# Casting label column to ClassLabel Object\n","dataset = dataset.cast_column('label', ClassLabels)\n","\n","# Splitting the dataset into training and testing sets using an 60-40 split ratio.\n","dataset = dataset.train_test_split(test_size=0.4, shuffle=True, stratify_by_column=\"label\")\n","\n","# Extracting the training data from the split dataset.\n","train_data = dataset['train']\n","\n","# Extracting the testing data from the split dataset.\n","test_data = dataset['test']"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["a7a5f6ff026849c98f6eac30a313adf4","cd4caf1de6a44c95993175a15bf30aaf","17ad83a197144790a514e913092579fb","4dfb06515c154f25b305edaff42e69a9","4b3a3ba289ef4eab8c58709825a6b7dd","26edd7af5bdb4647aa2bfb72f346fd46","7e396fdf4f634560bd0b696e6def8fcd","38d4f84b4fbc47e0967f08078ad67f54","482afba929494672b2dee0b1b2965d32","3a1e416f0ec243eb8c372fd0ef3c10ee","195c81385e1b49ee9afef2ed6ea0009e"]},"id":"FvEEn_iDB8uX","outputId":"09809c77-f21d-4173-dd6c-adc2e81c3456"},"outputs":[{"output_type":"display_data","data":{"text/plain":["preprocessor_config.json: 0%| | 0.00/160 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a7a5f6ff026849c98f6eac30a313adf4"}},"metadata":{}}],"source":["# Define the pre-trained ViT model string\n","model_str = \"google/vit-base-patch16-224-in21k\"\n","\n","# Create a processor for ViT model input\n","processor = ViTImageProcessor.from_pretrained(model_str)\n","\n","# Retrieve the image mean and standard deviation used for normalization\n","image_mean, image_std = processor.image_mean, processor.image_std\n","size = processor.size[\"height\"]\n","\n","# Define transformations for training and validation data\n","_train_transforms = Compose(\n"," [\n"," Resize((size, size)),\n"," RandomRotation(90),\n"," RandomAdjustSharpness(2),\n"," ToTensor(),\n"," Normalize(mean=image_mean, std=image_std)\n"," ]\n",")\n","\n","_val_transforms = Compose(\n"," [\n"," Resize((size, size)),\n"," ToTensor(),\n"," Normalize(mean=image_mean, std=image_std)\n"," ]\n",")\n","\n","# Define functions to apply transformations\n","def train_transforms(examples):\n"," examples['pixel_values'] = [_train_transforms(image.convert(\"RGB\")) for image in examples['image']]\n"," return examples\n","\n","def val_transforms(examples):\n"," examples['pixel_values'] = [_val_transforms(image.convert(\"RGB\")) for image in examples['image']]\n"," return examples\n","\n","# Set transforms for training and test data\n","train_data.set_transform(train_transforms)\n","test_data.set_transform(val_transforms)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9nEcswLACNVt"},"outputs":[],"source":["def collate_fn(examples):\n"," pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n"," labels = torch.tensor([example['label'] for example in examples])\n"," return {\"pixel_values\": pixel_values, \"labels\": labels}"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":153,"referenced_widgets":["a6696944967746799da06e078174aea0","e8010e0e07de424e8421fd738d70460b","a641c547e7194727b2f4dc0c67345492","458defb4b3094091994527c8afeb11eb","d94c1d14da6448418be3cf5235d7aae4","4c4393d3e9b7498cbafa168e19371015","0969e51641e947e2a7320400a3aab0f6","4a9931ad59184859bd54c29f4297af0f","deb35747a4fd4f47890408c52a665894","ad5ef517caca41edbd67f8f7abe83aef","51d6ab5373b147038a9b313779a6a3e1","50fda86107eb4394a12651e892815fd3","32ac1d055dd94a2e862141ec5a084b81","e980b97847ad4427ae6d9b7d382687a6","9fda87dc751046c9ad724c666affc68b","5a1ffba7a0194842a2419cafe8f5cb82","38c471ab8d234e04850f797f09df5072","0ee427083efc40e6a834cfd93a34d161","69f605449d2346b1ae9fd0c031dd12e3","2eb5968d83fc4eb4922a0240ef22c5f3","89060c5cfbb74b298538857f040f85ce","01b5619abe444eaeace211e7a9d4ee55"]},"id":"goTy8XdcCS7t","outputId":"c6b221dd-6e2d-4ce4-a6d7-f44c808e5718"},"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json: 0%| | 0.00/502 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a6696944967746799da06e078174aea0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors: 0%| | 0.00/346M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"50fda86107eb4394a12651e892815fd3"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]},{"output_type":"stream","name":"stdout","text":["85.800194\n"]}],"source":["# Create a ViTForImageClassification model\n","model = ViTForImageClassification.from_pretrained(model_str, num_labels=len(labels_list))\n","model.config.id2label = id2label\n","model.config.label2id = label2id\n","\n","# Calculate and print the number of trainable parameters in millions for the model.\n","print(model.num_parameters(only_trainable=True) / 1e6)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["dfb7908182f24697999180bea428fbf2","a5dc0e63e24c4a6593814d2fcc4d104e","fad30e1bffba42299b26019110cd4924","8d3521937fc9408dbf6a09f360101100","af665b734f014cfe9e3ed334797c40d2","962ecd80fc9a4d80bdf32f73c3468d22","8cd3684c3fe74b3fb5ab2cdf7c2a1265","bfddc98484b14f7fb6562945f19ae065","1ca04865aa5a42fea7695ee9751bd363","c5fb9d26bbc14c108d4d2dbeed1daab3","0723d29852574c50ab3b87dd8d51c612"]},"id":"bRleo0I-CqPv","outputId":"904e2b46-c079-4d34-a030-de111517351b"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script: 0%| | 0.00/4.20k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dfb7908182f24697999180bea428fbf2"}},"metadata":{}}],"source":["# Load the accuracy metric from a module named 'evaluate'\n","accuracy = evaluate.load(\"accuracy\")\n","\n","# Define a function 'compute_metrics' to calculate evaluation metrics\n","def compute_metrics(eval_pred):\n"," # Extract model predictions from the evaluation prediction object\n"," predictions = eval_pred.predictions\n","\n"," # Extract true labels from the evaluation prediction object\n"," label_ids = eval_pred.label_ids\n","\n"," # Calculate accuracy using the loaded accuracy metric\n"," # Convert model predictions to class labels by selecting the class with the highest probability (argmax)\n"," predicted_labels = predictions.argmax(axis=1)\n","\n"," # Calculate accuracy score by comparing predicted labels to true labels\n"," acc_score = accuracy.compute(predictions=predicted_labels, references=label_ids)['accuracy']\n","\n"," # Return the computed accuracy as a dictionary with the key \"accuracy\"\n"," return {\n"," \"accuracy\": acc_score\n"," }"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xtOJErdaCcy-"},"outputs":[],"source":["# Define training arguments\n","args = TrainingArguments(\n"," output_dir=\"Deep-Fake-Detector-v2-Model\",\n"," logging_dir='./logs',\n"," evaluation_strategy=\"epoch\",\n"," learning_rate=1e-6,\n"," per_device_train_batch_size=32,\n"," per_device_eval_batch_size=8,\n"," num_train_epochs=2,\n"," weight_decay=0.02,\n"," warmup_steps=50,\n"," remove_unused_columns=False,\n"," save_strategy='epoch',\n"," load_best_model_at_end=True,\n"," save_total_limit=1,\n"," report_to=\"none\"\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qX_SWsL3C2DN"},"outputs":[],"source":["# Create a Trainer instance\n","trainer = Trainer(\n"," model,\n"," args,\n"," train_dataset=train_data,\n"," eval_dataset=test_data,\n"," data_collator=collate_fn,\n"," compute_metrics=compute_metrics,\n"," tokenizer=processor,\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":141},"id":"0CnRoQf5C4D-","outputId":"a9829f76-54c7-472c-e9e0-3ff916a569f6"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='7001' max='7001' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [7001/7001 12:12]\n"," </div>\n"," "]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["{'eval_loss': 0.6953830718994141,\n"," 'eval_model_preparation_time': 0.0045,\n"," 'eval_accuracy': 0.4956340065355976,\n"," 'eval_runtime': 734.5409,\n"," 'eval_samples_per_second': 76.239,\n"," 'eval_steps_per_second': 9.531}"]},"metadata":{},"execution_count":23}],"source":["trainer.evaluate()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":210},"id":"iZUij-VwELXU","outputId":"bfb07abe-2c71-4896-d289-2c3dfc5c0116"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='5252' max='5252' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [5252/5252 2:05:27, Epoch 2/2]\n"," </div>\n"," <table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: left;\">\n"," <th>Epoch</th>\n"," <th>Training Loss</th>\n"," <th>Validation Loss</th>\n"," <th>Model Preparation Time</th>\n"," <th>Accuracy</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <td>1</td>\n"," <td>0.251400</td>\n"," <td>0.328844</td>\n"," <td>0.004500</td>\n"," <td>0.901430</td>\n"," </tr>\n"," <tr>\n"," <td>2</td>\n"," <td>0.194400</td>\n"," <td>0.263013</td>\n"," <td>0.004500</td>\n"," <td>0.921162</td>\n"," </tr>\n"," </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='14002' max='7001' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [7001/7001 1:14:30]\n"," </div>\n"," "]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=5252, training_loss=0.3096036569634559, metrics={'train_runtime': 7529.7371, 'train_samples_per_second': 22.312, 'train_steps_per_second': 0.698, 'total_flos': 1.3018809239230685e+19, 'train_loss': 0.3096036569634559, 'epoch': 2.0})"]},"metadata":{},"execution_count":24}],"source":["trainer.train()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":159},"id":"b8caa0kpH9uf","outputId":"b35c476a-76fc-4cca-9115-77ef9fc46aff"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='7001' max='7001' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [7001/7001 11:44]\n"," </div>\n"," "]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["{'eval_loss': 0.26301252841949463,\n"," 'eval_model_preparation_time': 0.0045,\n"," 'eval_accuracy': 0.9211621221049624,\n"," 'eval_runtime': 704.6584,\n"," 'eval_samples_per_second': 79.473,\n"," 'eval_steps_per_second': 9.935,\n"," 'epoch': 2.0}"]},"metadata":{},"execution_count":25}],"source":["trainer.evaluate()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":54},"collapsed":true,"id":"GQo5AFJ1N1GC","outputId":"9fad62a4-b67c-4dc3-c7d5-12f2b7d2d225"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["{'test_loss': 0.26301252841949463, 'test_model_preparation_time': 0.0045, 'test_accuracy': 0.9211621221049624, 'test_runtime': 707.3371, 'test_samples_per_second': 79.172, 'test_steps_per_second': 9.898}\n"]}],"source":["outputs = trainer.predict(test_data)\n","print(outputs.metrics)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":850},"id":"vZE0GZXnIVtZ","outputId":"a2ddddb1-ee5e-4ada-f647-9612eb861408"},"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy: 0.9212\n","F1 Score: 0.9210\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 2 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Classification report:\n","\n"," precision recall f1-score support\n","\n"," Realism 0.9683 0.8708 0.9170 28001\n"," Deepfake 0.8826 0.9715 0.9249 28000\n","\n"," accuracy 0.9212 56001\n"," macro avg 0.9255 0.9212 0.9210 56001\n","weighted avg 0.9255 0.9212 0.9210 56001\n","\n"]}],"source":["# Extract the true labels from the model outputs\n","y_true = outputs.label_ids\n","\n","# Predict the labels by selecting the class with the highest probability\n","y_pred = outputs.predictions.argmax(1)\n","\n","# Define a function to plot a confusion matrix\n","def plot_confusion_matrix(cm, classes, title='Confusion Matrix', cmap=plt.cm.Blues, figsize=(10, 8)):\n"," \"\"\"\n"," This function plots a confusion matrix.\n","\n"," Parameters:\n"," cm (array-like): Confusion matrix as returned by sklearn.metrics.confusion_matrix.\n"," classes (list): List of class names, e.g., ['Class 0', 'Class 1'].\n"," title (str): Title for the plot.\n"," cmap (matplotlib colormap): Colormap for the plot.\n"," \"\"\"\n"," # Create a figure with a specified size\n"," plt.figure(figsize=figsize)\n","\n"," # Display the confusion matrix as an image with a colormap\n"," plt.imshow(cm, interpolation='nearest', cmap=cmap)\n"," plt.title(title)\n"," plt.colorbar()\n","\n"," # Define tick marks and labels for the classes on the axes\n"," tick_marks = np.arange(len(classes))\n"," plt.xticks(tick_marks, classes, rotation=90)\n"," plt.yticks(tick_marks, classes)\n","\n"," fmt = '.0f'\n"," # Add text annotations to the plot indicating the values in the cells\n"," thresh = cm.max() / 2.0\n"," for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n"," plt.text(j, i, format(cm[i, j], fmt), horizontalalignment=\"center\", color=\"white\" if cm[i, j] > thresh else \"black\")\n","\n"," # Label the axes\n"," plt.ylabel('True label')\n"," plt.xlabel('Predicted label')\n","\n"," # Ensure the plot layout is tight\n"," plt.tight_layout()\n"," # Display the plot\n"," plt.show()\n","\n","# Calculate accuracy and F1 score\n","accuracy = accuracy_score(y_true, y_pred)\n","f1 = f1_score(y_true, y_pred, average='macro')\n","\n","# Display accuracy and F1 score\n","print(f\"Accuracy: {accuracy:.4f}\")\n","print(f\"F1 Score: {f1:.4f}\")\n","\n","# Get the confusion matrix if there are a small number of labels\n","if len(labels_list) <= 150:\n"," # Compute the confusion matrix\n"," cm = confusion_matrix(y_true, y_pred)\n","\n"," # Plot the confusion matrix using the defined function\n"," plot_confusion_matrix(cm, labels_list, figsize=(8, 6))\n","\n","# Finally, display classification report\n","print()\n","print(\"Classification report:\")\n","print()\n","print(classification_report(y_true, y_pred, target_names=labels_list, digits=4))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Qj1F9FLgIedG"},"outputs":[],"source":["trainer.save_model()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"F-jYKwyWIiue"},"outputs":[],"source":["# Import the 'pipeline' function from the 'transformers' library.\n","from transformers import pipeline\n","pipe = pipeline('image-classification', model=model_name, device=0)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Iy0WsizHIm_m"},"outputs":[],"source":["image = test_data[1][\"image\"]\n","image"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TG7rBUMnIpXl"},"outputs":[],"source":["pipe(image)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CtTHa2_DIq0x"},"outputs":[],"source":["id2label[test_data[1][\"label\"]]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":17,"referenced_widgets":["c543d54a69be477cb3ea44e00740f423","209e4203357c45ecb30ddd8a7d8ea397","a49757c942a54305b0f4d77ef5f6c705","3ef1efe303554fb0b67e3363d2ae7dab","274e6f592492429ea36b4c3e9ab11b58","f6334b3881124c02b97eb3812eaf1386","e3365a3b13f54ab8ab254a5691267dd8","c12d616c6130493284db4c72d337f6b6","0e52eccb2dcc4753bec560735b60e4c7","296fae32c62c44d09a8b085ec0103b37","5b223faa2ea142668b0d53f367f45088","30731cb6a4e34d599da0ee31d9304fe9","b02cc310878840c883a4719d24cf67dc","a1734c3ce27d4a00aa1cda7f1eb1e186","462692903bac4146af1d260c70fb5ac2","5fb0e2512e014af5a7f14bee4dff573e","0be1941c3f96441c8ac52e140d55fd7b","17e13a9d20b240439f8a8d5cd653ba01","a56c3bdec83549af9c63ca8211811c83","a8ff81ead86244c28747e34e2e266efa"]},"id":"BYY0rKBJIsgI","outputId":"11cba13b-0b0f-413c-e946-3616b377f37e"},"outputs":[{"output_type":"display_data","data":{"text/plain":["VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c543d54a69be477cb3ea44e00740f423"}},"metadata":{}}],"source":["from huggingface_hub import notebook_login\n","notebook_login()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7AQz322sS3Zc","outputId":"b186ab7e-aed2-41f7-d7f3-180bb889b663","colab":{"base_uri":"https://localhost:8080/"}},"outputs":[{"output_type":"stream","name":"stdout","text":["Repo prithivMLmods/Deep-Fake-Detector-v2-Model created\n"]}],"source":["# Import the HfApi class from the huggingface_hub library.\n","from huggingface_hub import HfApi\n","\n","api = HfApi()\n","repo_id = f\"prithivMLmods/Deep-Fake-Detector-v2-Model\"\n","\n","try:\n"," # Attempt to create a new repository on the Hugging Face Model Hub using the specified repo_id.\n"," api.create_repo(repo_id)\n","\n"," # If the repository creation is successful, print a message indicating that the repository was created.\n"," print(f\"Repo {repo_id} created\")\n","except:\n"," # If an exception is raised, print a message indicating that the repository already exists.\n"," print(f\"Repo {repo_id} already 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