From f4b1b19978256c154099be890984ffac16f36454 Mon Sep 17 00:00:00 2001 From: 2300032079 <143174934+poojithasree2@users.noreply.github.com> Date: Fri, 8 Mar 2024 19:25:02 +0530 Subject: [PATCH 1/2] 2300032079 --- index.html | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/index.html b/index.html index 5522981..ed801f7 100644 --- a/index.html +++ b/index.html @@ -26,8 +26,17 @@

Helloww ZeroOne!

CSE Hons Namasthe! + + + + + 2300032079 + Poojitha + CSE + Hello ZeroOne + - \ No newline at end of file + From a5374eb108b68c9c014dc0090c3643e0d1c2620f Mon Sep 17 00:00:00 2001 From: Poojitha <143174934+poojithasree2@users.noreply.github.com> Date: Sat, 1 Nov 2025 19:41:06 +0530 Subject: [PATCH 2/2] Created using Colab --- dogs_vs_cats.ipynb | 460 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 460 insertions(+) create mode 100644 dogs_vs_cats.ipynb diff --git a/dogs_vs_cats.ipynb b/dogs_vs_cats.ipynb new file mode 100644 index 0000000..b9b05fc --- /dev/null +++ b/dogs_vs_cats.ipynb @@ -0,0 +1,460 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true, + "authorship_tag": "ABX9TyOdbgeOmsd22ZH45NbCSWnS", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fil7dKfzZGmX" + }, + "outputs": [], + "source": [ + "!wget https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_5340.zip" + ] + }, + { + "cell_type": "code", + "source": [ + "!unzip kagglecatsanddogs_5340.zip\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "b-pjyr9VrGta", + "outputId": "1891054b-423b-406e-9cd6-7646ec3c094f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Archive: kagglecatsanddogs_5340.zip\n", + "replace PetImages/Cat/0.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: " + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "##Import modules" + ], + "metadata": { + "id": "nGN-7wcPrpeE" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import warnings\n", + "import os\n", + "import tqdm\n", + "import random\n", + "from keras.preprocessing.image import load_img\n", + "warnings.filterwarnings('ignore')" + ], + "metadata": { + "id": "Z1eQ6888rvtQ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "##create dataframe for i/p and o/p" + ], + "metadata": { + "id": "zmwYpOLasNiA" + } + }, + { + "cell_type": "code", + "source": [ + "input_path = []\n", + "label = []\n", + "\n", + "for class_name in os.listdir(\"PetImages\"):\n", + " for path in os.listdir(\"PetImages/\"+class_name):\n", + " if class_name == 'Cat':\n", + " label.append(0)\n", + " else:\n", + " label.append(1)\n", + " input_path.append(os.path.join(\"PetImages\", class_name, path))\n", + "print(input_path[0], label[0])" + ], + "metadata": { + "id": "hKurPC0KsV93" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df = pd.DataFrame()\n", + "df['images'] = input_path\n", + "df['label'] = label\n", + "df = df.sample(frac=1).reset_index(drop=True)\n", + "df.head()" + ], + "metadata": { + "id": "Y_-IjF6Bt2g_" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "for i in df['images']:\n", + " if '.jpg' not in i:\n", + " print(i)\n" + ], + "metadata": { + "id": "yeNGws-5udOt" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import PIL\n", + "l = []\n", + "for image in df['images']:\n", + " try:\n", + " img = PIL.Image.open(image)\n", + " except:\n", + " l.append(image)\n", + "l" + ], + "metadata": { + "id": "2hWYhKNTudRI" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# delete db files\n", + "df = df[df['images']!='PetImages/Dog/Thumbs.db']\n", + "df = df[df['images']!='PetImages/Cat/Thumbs.db']\n", + "df = df[df['images']!='PetImages/Cat/666.jpg']\n", + "df = df[df['images']!='PetImages/Dog/11702.jpg']\n", + "len(df)" + ], + "metadata": { + "id": "tfwjbZcyudTu" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "##Exploratory Data Analysis" + ], + "metadata": { + "id": "s-TMLAuOunuT" + } + }, + { + "cell_type": "code", + "source": [ + "# to display grid of images\n", + "plt.figure(figsize=(25,25))\n", + "temp = df[df['label']==1]['images']\n", + "start = random.randint(0, len(temp))\n", + "files = temp[start:start+25]\n", + "\n", + "for index, file in enumerate(files):\n", + " plt.subplot(5,5, index+1)\n", + " img = load_img(file)\n", + " img = np.array(img)\n", + " plt.imshow(img)\n", + " plt.title('Dogs')\n", + " plt.axis('off')" + ], + "metadata": { + "id": "l4OvK_SnudWO" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# to display grid of images\n", + "plt.figure(figsize=(25,25))\n", + "temp = df[df['label']==0]['images']\n", + "start = random.randint(0, len(temp))\n", + "files = temp[start:start+25]\n", + "\n", + "for index, file in enumerate(files):\n", + " plt.subplot(5,5, index+1)\n", + " img = load_img(file)\n", + " img = np.array(img)\n", + " plt.imshow(img)\n", + " plt.title('Cats')\n", + " plt.axis('off')" + ], + "metadata": { + "id": "2v3efyHSudYy" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import seaborn as sns\n", + "sns.countplot(df['label'])\n" + ], + "metadata": { + "id": "iiKdQQp3uda4" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "##Create DataGenerator for the Images" + ], + "metadata": { + "id": "-bDltrp0vbBT" + } + }, + { + "cell_type": "code", + "source": [ + "df['label'] = df['label'].astype('str')" + ], + "metadata": { + "id": "P5ED7VSruddI" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "df.head()" + ], + "metadata": { + "id": "vPMSiHStudfn" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# input split\n", + "from sklearn.model_selection import train_test_split\n", + "train, test = train_test_split(df, test_size=0.2, random_state=42)" + ], + "metadata": { + "id": "iz1J5CAiudhw" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from keras.preprocessing.image import ImageDataGenerator\n", + "train_generator = ImageDataGenerator(\n", + " rescale = 1./255, # normalization of images\n", + " rotation_range = 40, # augmention of images to avoid overfitting\n", + " shear_range = 0.2,\n", + " zoom_range = 0.2,\n", + " horizontal_flip = True,\n", + " fill_mode = 'nearest'\n", + ")\n", + "\n", + "val_generator = ImageDataGenerator(rescale = 1./255)\n", + "\n", + "train_iterator = train_generator.flow_from_dataframe(\n", + " train,\n", + " x_col='images',\n", + " y_col='label',\n", + " target_size=(128,128),\n", + " batch_size=512,\n", + " class_mode='binary'\n", + ")\n", + "\n", + "val_iterator = val_generator.flow_from_dataframe(\n", + " test,\n", + " x_col='images',\n", + " y_col='label',\n", + " target_size=(128,128),\n", + " batch_size=512,\n", + " class_mode='binary'\n", + ")" + ], + "metadata": { + "id": "ZaLQIUOZudkL" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "##Model Creation" + ], + "metadata": { + "id": "89ITQEgUvUXB" + } + }, + { + "cell_type": "code", + "source": [ + "from keras import Sequential\n", + "from keras.layers import Conv2D, MaxPool2D, Flatten, Dense\n", + "\n", + "model = Sequential([\n", + " Conv2D(16, (3,3), activation='relu', input_shape=(128,128,3)),\n", + " MaxPool2D((2,2)),\n", + " Conv2D(32, (3,3), activation='relu'),\n", + " MaxPool2D((2,2)),\n", + " Conv2D(64, (3,3), activation='relu'),\n", + " MaxPool2D((2,2)),\n", + " Flatten(),\n", + " Dense(512, activation='relu'),\n", + " Dense(1, activation='sigmoid')\n", + "])" + ], + "metadata": { + "id": "PK6v-ZVmudnh" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n", + "model.summary()" + ], + "metadata": { + "id": "aeNRSoIxu6bh" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "history = model.fit(train_iterator, epochs=10, validation_data=val_iterator)" + ], + "metadata": { + "id": "mPhWhJ3Eu6eJ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "##Visualization of Results" + ], + "metadata": { + "id": "PwAhwxOuvN8R" + } + }, + { + "cell_type": "code", + "source": [ + "acc = history.history['accuracy']\n", + "val_acc = history.history['val_accuracy']\n", + "epochs = range(len(acc))\n", + "\n", + "plt.plot(epochs, acc, 'b', label='Training Accuracy')\n", + "plt.plot(epochs, val_acc, 'r', label='Validation Accuracy')\n", + "plt.title('Accuracy Graph')\n", + "plt.legend()\n", + "plt.figure()\n", + "\n", + "loss = history.history['loss']\n", + "val_loss = history.history['val_loss']\n", + "plt.plot(epochs, loss, 'b', label='Training Loss')\n", + "plt.plot(epochs, val_loss, 'r', label='Validation Loss')\n", + "plt.title('Loss Graph')\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "id": "Lss4cGXJu6hf" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "##Test with Real Image" + ], + "metadata": { + "id": "7H8Swv98vGhP" + } + }, + { + "cell_type": "code", + "source": [ + "image_path = \"test.jpg\" # path of the image\n", + "img = load_img(image_path, target_size=(128, 128))\n", + "img = np.array(img)\n", + "img = img / 255.0 # normalize the image\n", + "img = img.reshape(1, 128, 128, 3) # reshape for prediction\n", + "pred = model.predict(img)\n", + "if pred[0] > 0.5:\n", + " label = 'Dog'\n", + "else:\n", + " label = 'Cat'\n", + "print(label)" + ], + "metadata": { + "id": "0vjWGKIevCHO" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file