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{
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"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU",
"gpuClass": "standard"
},
"cells": [
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "L6gytYO-DHMK",
"outputId": "b0c87fe1-77a4-45c7-8ea4-b8211cc0c4a7"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
]
}
],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "code",
"source": [
"%pip install efficientnet-pytorch"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OoBBN22XDRNG",
"outputId": "c63a35aa-a077-44c7-93e5-bc9ba9732770"
},
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: efficientnet-pytorch in /usr/local/lib/python3.9/dist-packages (0.7.1)\n",
"Requirement already satisfied: torch in /usr/local/lib/python3.9/dist-packages (from efficientnet-pytorch) (2.0.0+cu118)\n",
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from torch->efficientnet-pytorch) (4.5.0)\n",
"Requirement already satisfied: sympy in /usr/local/lib/python3.9/dist-packages (from torch->efficientnet-pytorch) (1.11.1)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from torch->efficientnet-pytorch) (3.11.0)\n",
"Requirement already satisfied: networkx in /usr/local/lib/python3.9/dist-packages (from torch->efficientnet-pytorch) (3.1)\n",
"Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.9/dist-packages (from torch->efficientnet-pytorch) (2.0.0)\n",
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.9/dist-packages (from torch->efficientnet-pytorch) (3.1.2)\n",
"Requirement already satisfied: lit in /usr/local/lib/python3.9/dist-packages (from triton==2.0.0->torch->efficientnet-pytorch) (16.0.1)\n",
"Requirement already satisfied: cmake in /usr/local/lib/python3.9/dist-packages (from triton==2.0.0->torch->efficientnet-pytorch) (3.25.2)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from jinja2->torch->efficientnet-pytorch) (2.1.2)\n",
"Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.9/dist-packages (from sympy->torch->efficientnet-pytorch) (1.3.0)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"from PIL import Image\n",
"import torch\n",
"from torch import nn, optim\n",
"import torch.nn.functional as F\n",
"from torch.utils.data import DataLoader, Dataset\n",
"import albumentations as A\n",
"from albumentations.pytorch import ToTensorV2 \n",
"from tqdm import tqdm\n",
"from torchvision import models\n",
"from efficientnet_pytorch import EfficientNet\n",
"from sklearn import metrics"
],
"metadata": {
"id": "phJgllqcDSuH"
},
"execution_count": 34,
"outputs": []
},
{
"cell_type": "code",
"source": [
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
],
"metadata": {
"id": "DyUTFa31DTdp"
},
"execution_count": 35,
"outputs": []
},
{
"cell_type": "code",
"source": [
"class Dataset(Dataset):\n",
" def __init__(self, root_images, root_file, transform = None):\n",
" self.root_images = root_images\n",
" self.root_file = root_file\n",
" self.transform = transform\n",
" self.file = pd.read_csv(root_file)\n",
"\n",
"\n",
" def __len__(self):\n",
" return self.file.shape[0]\n",
" \n",
" def __getitem__(self,index):\n",
" img_path = os.path.join(self.root_images, self.file['id'][index])\n",
" image = np.array(Image.open(img_path).convert('RGB'))\n",
" \n",
" if self.transform is not None:\n",
" augmentations = self.transform(image = image)\n",
" image = augmentations['image'] \n",
" \n",
" return image"
],
"metadata": {
"id": "kTk-mXXUDUUA"
},
"execution_count": 36,
"outputs": []
},
{
"cell_type": "code",
"source": [
"learning_rate = 0.0001\n",
"batch_size = 32\n",
"epochs = 10\n",
"height = 224 \n",
"width = 224\n",
"IMG = '/content/drive/MyDrive/Colab Notebooks/AI images or Not/test'\n",
"FILE = '/content/sample_submission.csv'"
],
"metadata": {
"id": "HXEpa4PlDU85"
},
"execution_count": 37,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def get_loader(image, file, batch_size, test_transform):\n",
" \n",
" test_ds = Dataset(image , file, test_transform)\n",
" test_loader = DataLoader(test_ds, batch_size= batch_size, shuffle= False)\n",
"\n",
"\n",
"\n",
" return test_loader "
],
"metadata": {
"id": "i-VOTQp2DVbK"
},
"execution_count": 38,
"outputs": []
},
{
"cell_type": "code",
"source": [
"normalize = A.Normalize(\n",
" mean = [0.485 , 0.456 , 0.406],\n",
" std = [0.229 , 0.224, 0.255],\n",
" max_pixel_value= 255.0\n",
")\n",
"\n",
"\n",
"test_transform = A.Compose(\n",
" [A.Resize(width=width , height= height),\n",
" normalize,\n",
" ToTensorV2()\n",
" ]\n",
")\n"
],
"metadata": {
"id": "RD4GnrT6DVpr"
},
"execution_count": 39,
"outputs": []
},
{
"cell_type": "code",
"source": [
"class Net(nn.Module):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.model = EfficientNet.from_pretrained('efficientnet-b4')\n",
" self.fct = nn.Linear(1000,1)\n",
" \n",
" def forward(self,img):\n",
" x = self.model(img)\n",
" # print(x.shape)\n",
" x = self.fct(x)\n",
" return x"
],
"metadata": {
"id": "HYH0pBe9DV3M"
},
"execution_count": 40,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def load_checkpoint(checkpoint, model, optimizer):\n",
" print('====> Loading...')\n",
" model.load_state_dict(checkpoint['state_dict'])\n",
" optimizer.load_state_dict(checkpoint['optimizer'])"
],
"metadata": {
"id": "1Ype_u3qDV-n"
},
"execution_count": 41,
"outputs": []
},
{
"cell_type": "code",
"source": [
"test = pd.read_csv(FILE)\n",
"test"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 424
},
"id": "Jf_Is1qDGz-W",
"outputId": "cf79a4c0-2bca-473c-886e-726d7956015d"
},
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"cell_type": "code",
"source": [
"model = Net().to(device)\n",
"optimizer = optim.Adam(model.parameters(), lr= learning_rate)\n",
"\n",
"checkpoint_file = '/content/drive/MyDrive/Colab Notebooks/AI images or Not/baseline_V0.pth.tar'\n",
"test_loader = get_loader(IMG, FILE, batch_size, test_transform)\n",
"checkpoint = torch.load(checkpoint_file, map_location=torch.device('cpu'))\n",
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"\n",
" for i in range(len(p)):\n",
" test['label'][k] = (p[i] > 0.75).astype('float')\n",
" k += 1"
],
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"id": "qWB6WzrlDWD7",
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},
"outputId": "52e74e4b-96e7-40e7-d1b3-a22c7b70098d"
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"execution_count": 43,
"outputs": [
{
"output_type": "stream",
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"text": [
"Loaded pretrained weights for efficientnet-b4\n",
"====> Loading...\n"
]
},
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" 0%| | 0/1358 [00:00<?, ?it/s]<ipython-input-43-383dee41b09a>:16: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" test['label'][k] = (p[i] > 0.75).astype('float')\n",
"100%|██████████| 1358/1358 [4:56:02<00:00, 13.08s/it]\n"
]
}
]
},
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"outputId": "4a4c0b81-ff75-4ed7-a5df-6f98644f03e2"
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"execution_count": 44,
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" <th>id</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.jpg</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.jpg</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10.jpg</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>100.jpg</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1000.jpg</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43437</th>\n",
" <td>9995.jpg</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43438</th>\n",
" <td>9996.jpg</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43439</th>\n",
" <td>9997.jpg</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43440</th>\n",
" <td>9998.jpg</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43441</th>\n",
" <td>9999.jpg</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>43442 rows × 2 columns</p>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-00389cce-5634-451c-81fb-649bced26029')\"\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 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\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",
" [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-00389cce-5634-451c-81fb-649bced26029 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-00389cce-5634-451c-81fb-649bced26029');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? 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",
" </div>\n",
" "
]
},
"metadata": {},
"execution_count": 44
}
]
},
{
"cell_type": "code",
"source": [
"test.to_csv('sub.csv', index=False)"
],
"metadata": {
"id": "nX_vnorKDWKK"
},
"execution_count": 45,
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "JmJa1KolDWM5"
},
"execution_count": 45,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def predict(image):\n",
" image = np.array(image)\n",
" transform = A.Compose(\n",
" [A.Resize(width=width, height=height),\n",
" normalize,\n",
" ToTensorV2()\n",
" ]\n",
" )\n",
" image = transform(image=image)[\"image\"].unsqueeze(0).to(device).to(torch.float32)\n",
" with torch.no_grad():\n",
" model.eval()\n",
" output = torch.sigmoid(model(image))\n",
" label = (output > 0.75).item()\n",
" return \"AI Image\" if label else \"Not AI Image\""
],
"metadata": {
"id": "TKs8s0TyDWP0"
},
"execution_count": 46,
"outputs": []
},
{
"cell_type": "code",
"source": [
"%pip install gradio"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "k7bGi6MqqO-r",
"outputId": "120d9571-3381-418a-9056-ff8b84199ca7"
},
"execution_count": 47,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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"Collecting h11>=0.8\n",
" Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
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"Collecting uc-micro-py\n",
" Downloading uc_micro_py-1.0.1-py3-none-any.whl (6.2 kB)\n",
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"Building wheels for collected packages: ffmpy\n",
" Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4707 sha256=030fcfbd0063a8e91f56986ba5a3eaeb8d3d94a5b7f0a2c726f9367cfd7d2fbf\n",
" Stored in directory: /root/.cache/pip/wheels/91/e2/96/f676aa08bfd789328c6576cd0f1fde4a3d686703bb0c247697\n",
"Successfully built ffmpy\n",
"Installing collected packages: pydub, ffmpy, websockets, uc-micro-py, semantic-version, python-multipart, orjson, multidict, h11, frozenlist, async-timeout, aiofiles, yarl, uvicorn, starlette, mdit-py-plugins, linkify-it-py, huggingface-hub, httpcore, aiosignal, httpx, fastapi, aiohttp, gradio-client, gradio\n",
"Successfully installed aiofiles-23.1.0 aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 fastapi-0.95.1 ffmpy-0.3.0 frozenlist-1.3.3 gradio-3.27.0 gradio-client-0.1.3 h11-0.14.0 httpcore-0.17.0 httpx-0.24.0 huggingface-hub-0.13.4 linkify-it-py-2.0.0 mdit-py-plugins-0.3.3 multidict-6.0.4 orjson-3.8.10 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.26.1 uc-micro-py-1.0.1 uvicorn-0.21.1 websockets-11.0.2 yarl-1.8.2\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import gradio as gr"
],
"metadata": {
"id": "Q5a9SQbcqLH7"
},
"execution_count": 48,
"outputs": []
},
{
"cell_type": "code",
"source": [
"\n",
"\n",
"inputs = gr.inputs.Image()\n",
"outputs = gr.outputs.Textbox()\n",
"iface = gr.Interface(fn=predict, inputs=inputs, outputs=outputs, capture_session=True)\n",
"iface.launch()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 775
},
"id": "sEsxRg9IqLue",
"outputId": "1ea4931d-4001-4c37-a0f9-97017b2e55a6"
},
"execution_count": 49,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.9/dist-packages/gradio/inputs.py:257: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
" warnings.warn(\n",
"/usr/local/lib/python3.9/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
" warnings.warn(value)\n",
"/usr/local/lib/python3.9/dist-packages/gradio/outputs.py:22: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
" warnings.warn(\n",
"/usr/local/lib/python3.9/dist-packages/gradio/deprecation.py:40: UserWarning: `capture_session` parameter is deprecated, and it has no effect\n",
" warnings.warn(value)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
"Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"(async (port, path, width, height, cache, element) => {\n",
" if (!google.colab.kernel.accessAllowed && !cache) {\n",
" return;\n",
" }\n",
" element.appendChild(document.createTextNode(''));\n",
" const url = await google.colab.kernel.proxyPort(port, {cache});\n",
"\n",
" const external_link = document.createElement('div');\n",
" external_link.innerHTML = `\n",
" <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
" Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
" https://localhost:${port}${path}\n",
" </a>\n",
" </div>\n",
" `;\n",
" element.appendChild(external_link);\n",
"\n",
" const iframe = document.createElement('iframe');\n",
" iframe.src = new URL(path, url).toString();\n",
" iframe.height = height;\n",
" iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
" iframe.width = width;\n",
" iframe.style.border = 0;\n",
" element.appendChild(iframe);\n",
" })(7860, \"/\", \"100%\", 500, false, window.element)"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": []
},
"metadata": {},
"execution_count": 49
}
]
},
{
"cell_type": "code",
"source": [
"import gradio as gr\n",
"import torch\n",
"import numpy as np\n",
"from PIL import Image\n",
"\n",
"# define the predict function\n",
"def predict(image):\n",
" # preprocess the image\n",
" image = np.array(image)\n",
" image = test_transform(image=image)['image']\n",
" image = image.unsqueeze(0).to(device)\n",
"\n",
" # get the model prediction\n",
" with torch.no_grad():\n",
" output = model(image)\n",
" pred = torch.sigmoid(output).cpu().numpy().squeeze()\n",
" \n",
" # return the prediction as a string\n",
" return f\"This image is {'AI generated' if pred > 0.75 else 'NOT AI generated'}\"\n",
"\n",
"# define the input interface with examples\n",
"inputs = gr.inputs.Image(shape=(224, 224))\n",
"outputs = gr.outputs.Textbox()\n",
"examples = [\n",
" ['/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/3.jpg'],\n",
" ['/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/10.jpg'],\n",
" ['/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/14.jpg'],\n",
" ['/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/4515.jpg']\n",
" ['/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/4518.jpg'],\n",
"]\n",
"iface = gr.Interface(fn=predict, inputs=inputs, outputs=outputs, examples=examples)\n",
"\n",
"# launch the gradio app\n",
"iface.launch()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 428
},
"id": "nMuNn5FCvEuS",
"outputId": "ad4760a5-9458-483a-b9bc-c655f0bf6429"
},
"execution_count": 55,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"<>:28: SyntaxWarning: list indices must be integers or slices, not str; perhaps you missed a comma?\n",
"<>:28: SyntaxWarning: list indices must be integers or slices, not str; perhaps you missed a comma?\n",
"/usr/local/lib/python3.9/dist-packages/gradio/inputs.py:257: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
" warnings.warn(\n",
"/usr/local/lib/python3.9/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
" warnings.warn(value)\n",
"/usr/local/lib/python3.9/dist-packages/gradio/outputs.py:22: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
" warnings.warn(\n",
"<ipython-input-55-ad9875932060>:28: SyntaxWarning: list indices must be integers or slices, not str; perhaps you missed a comma?\n",
" ['/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/4515.jpg']\n"
]
},
{
"output_type": "error",
"ename": "TypeError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-55-ad9875932060>\u001b[0m in \u001b[0;36m<cell line: 25>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/10.jpg'\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[1;32m 27\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/14.jpg'\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---> 28\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0;34m'/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/4515.jpg'\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 29\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'/content/drive/MyDrive/Colab Notebooks/AI images or Not/train/4518.jpg'\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[1;32m 30\u001b[0m ]\n",
"\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not str"
]
}
]
}
]
} |