bees-notebook-resnet50.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/srishtimishra2512/beesnotebook?scriptVersionId=184064659\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","id":"8b90b5b7","metadata":{"papermill":{"duration":0.014825,"end_time":"2024-06-17T21:06:51.782335","exception":false,"start_time":"2024-06-17T21:06:51.76751","status":"completed"},"tags":[]},"source":["**In this dataset, we are going to classify bee images in different classes based on the health status of the bees in the image. This kind of work can allow us to track any kind of disease that may infect a beehive in real-time.**"]},{"cell_type":"code","execution_count":1,"id":"e9047ed2","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2024-06-17T21:06:51.811981Z","iopub.status.busy":"2024-06-17T21:06:51.811022Z","iopub.status.idle":"2024-06-17T21:06:51.8238Z","shell.execute_reply":"2024-06-17T21:06:51.82286Z"},"papermill":{"duration":0.030218,"end_time":"2024-06-17T21:06:51.826264","exception":false,"start_time":"2024-06-17T21:06:51.796046","status":"completed"},"tags":[]},"outputs":[],"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n","# For example, here's several helpful packages to load\n","\n","import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","\n","# Input data files are available in the read-only \"../input/\" directory\n","# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n","\n","import os\n","#for dirname, _, filenames in os.walk('/kaggle/input'):\n","#     for filename in filenames:\n","#         print(os.path.join(dirname, filename))\n","\n","# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n","# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"]},{"cell_type":"code","execution_count":2,"id":"493ce7ee","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:06:51.854344Z","iopub.status.busy":"2024-06-17T21:06:51.854046Z","iopub.status.idle":"2024-06-17T21:06:52.806171Z","shell.execute_reply":"2024-06-17T21:06:52.804979Z"},"papermill":{"duration":0.968885,"end_time":"2024-06-17T21:06:52.808628","exception":false,"start_time":"2024-06-17T21:06:51.839743","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["/kaggle/working\r\n"]}],"source":["!pwd"]},{"cell_type":"markdown","id":"acb15e41","metadata":{"papermill":{"duration":0.013043,"end_time":"2024-06-17T21:06:52.837667","exception":false,"start_time":"2024-06-17T21:06:52.824624","status":"completed"},"tags":[]},"source":["**We need to train, test, and validation folders, with each folder containing subfolders of all the classes present in the dataset**"]},{"cell_type":"code","execution_count":3,"id":"40bbcb74","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:06:52.865827Z","iopub.status.busy":"2024-06-17T21:06:52.86549Z","iopub.status.idle":"2024-06-17T21:06:52.873555Z","shell.execute_reply":"2024-06-17T21:06:52.872741Z"},"papermill":{"duration":0.024571,"end_time":"2024-06-17T21:06:52.875652","exception":false,"start_time":"2024-06-17T21:06:52.851081","status":"completed"},"tags":[]},"outputs":[],"source":["parent_dir = \"/kaggle/working\"\n","train_dir = \"train\"\n","test_dir = \"test\"\n","val_dir = \"valid\"\n","\n","#creating train directory\n","train_path = os.path.join(parent_dir, train_dir)\n","if not os.path.exists(train_path):\n","    os.mkdir(train_path)\n","\n","#creating test directory\n","test_path = os.path.join(parent_dir, test_dir)\n","if not os.path.exists(test_path):\n","    os.mkdir(test_path)\n","    \n","#creating val directory\n","val_path = os.path.join(parent_dir, val_dir)\n","if not os.path.exists(val_path):\n","    os.mkdir(val_path)"]},{"cell_type":"markdown","id":"b68f4179","metadata":{"papermill":{"duration":0.013287,"end_time":"2024-06-17T21:06:52.902674","exception":false,"start_time":"2024-06-17T21:06:52.889387","status":"completed"},"tags":[]},"source":["**Now, we will find out the no. of classes we have in our dataset**"]},{"cell_type":"code","execution_count":4,"id":"6bd101d9","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:06:52.93132Z","iopub.status.busy":"2024-06-17T21:06:52.930739Z","iopub.status.idle":"2024-06-17T21:06:52.967546Z","shell.execute_reply":"2024-06-17T21:06:52.966329Z"},"papermill":{"duration":0.053508,"end_time":"2024-06-17T21:06:52.969721","exception":false,"start_time":"2024-06-17T21:06:52.916213","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["length of dataframe 5172\n"]}],"source":["csv_path = '/kaggle/input/honey-bee-annotated-images/bee_data.csv'\n","df= pd.read_csv(csv_path)\n","len_df = len(df)\n","print('length of dataframe',len_df)"]},{"cell_type":"code","execution_count":5,"id":"ebe87434","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:06:52.99864Z","iopub.status.busy":"2024-06-17T21:06:52.998324Z","iopub.status.idle":"2024-06-17T21:06:53.019016Z","shell.execute_reply":"2024-06-17T21:06:53.017992Z"},"papermill":{"duration":0.03769,"end_time":"2024-06-17T21:06:53.021231","exception":false,"start_time":"2024-06-17T21:06:52.983541","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<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>file</th>\n","      <th>date</th>\n","      <th>time</th>\n","      <th>location</th>\n","      <th>zip code</th>\n","      <th>subspecies</th>\n","      <th>health</th>\n","      <th>pollen_carrying</th>\n","      <th>caste</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>041_066.png</td>\n","      <td>8/28/18</td>\n","      <td>16:07</td>\n","      <td>Alvin, TX, USA</td>\n","      <td>77511</td>\n","      <td>-1</td>\n","      <td>hive being robbed</td>\n","      <td>False</td>\n","      <td>worker</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>041_072.png</td>\n","      <td>8/28/18</td>\n","      <td>16:07</td>\n","      <td>Alvin, TX, USA</td>\n","      <td>77511</td>\n","      <td>-1</td>\n","      <td>hive being robbed</td>\n","      <td>False</td>\n","      <td>worker</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>041_073.png</td>\n","      <td>8/28/18</td>\n","      <td>16:07</td>\n","      <td>Alvin, TX, USA</td>\n","      <td>77511</td>\n","      <td>-1</td>\n","      <td>hive being robbed</td>\n","      <td>False</td>\n","      <td>worker</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>041_067.png</td>\n","      <td>8/28/18</td>\n","      <td>16:07</td>\n","      <td>Alvin, TX, USA</td>\n","      <td>77511</td>\n","      <td>-1</td>\n","      <td>hive being robbed</td>\n","      <td>False</td>\n","      <td>worker</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>041_059.png</td>\n","      <td>8/28/18</td>\n","      <td>16:07</td>\n","      <td>Alvin, TX, USA</td>\n","      <td>77511</td>\n","      <td>-1</td>\n","      <td>hive being robbed</td>\n","      <td>False</td>\n","      <td>worker</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          file     date   time        location  zip code subspecies  \\\n","0  041_066.png  8/28/18  16:07  Alvin, TX, USA     77511         -1   \n","1  041_072.png  8/28/18  16:07  Alvin, TX, USA     77511         -1   \n","2  041_073.png  8/28/18  16:07  Alvin, TX, USA     77511         -1   \n","3  041_067.png  8/28/18  16:07  Alvin, TX, USA     77511         -1   \n","4  041_059.png  8/28/18  16:07  Alvin, TX, USA     77511         -1   \n","\n","              health  pollen_carrying   caste  \n","0  hive being robbed            False  worker  \n","1  hive being robbed            False  worker  \n","2  hive being robbed            False  worker  \n","3  hive being robbed            False  worker  \n","4  hive being robbed            False  worker  "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"markdown","id":"2c2ce938","metadata":{"papermill":{"duration":0.013554,"end_time":"2024-06-17T21:06:53.048753","exception":false,"start_time":"2024-06-17T21:06:53.035199","status":"completed"},"tags":[]},"source":["**Our target vector here is the health column of the dataframe. We want to find out how many target classes there are in the dataset.**"]},{"cell_type":"code","execution_count":6,"id":"3dc96f84","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:06:53.077892Z","iopub.status.busy":"2024-06-17T21:06:53.077574Z","iopub.status.idle":"2024-06-17T21:06:53.087389Z","shell.execute_reply":"2024-06-17T21:06:53.086383Z"},"papermill":{"duration":0.027355,"end_time":"2024-06-17T21:06:53.089856","exception":false,"start_time":"2024-06-17T21:06:53.062501","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1mNo. of target classes in the data: \u001b[0m 6\n"]}],"source":["num_classes = len(list(df['health'].unique()))\n","print('\\033[1m'+'No. of target classes in the data: '+ '\\033[0m', num_classes)"]},{"cell_type":"markdown","id":"fc18af73","metadata":{"papermill":{"duration":0.01378,"end_time":"2024-06-17T21:06:53.117661","exception":false,"start_time":"2024-06-17T21:06:53.103881","status":"completed"},"tags":[]},"source":["**These names of these 6 target classes are**"]},{"cell_type":"code","execution_count":7,"id":"a5b1635b","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:06:53.146657Z","iopub.status.busy":"2024-06-17T21:06:53.145895Z","iopub.status.idle":"2024-06-17T21:06:53.152769Z","shell.execute_reply":"2024-06-17T21:06:53.1517Z"},"papermill":{"duration":0.023677,"end_time":"2024-06-17T21:06:53.154997","exception":false,"start_time":"2024-06-17T21:06:53.13132","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1mTarget classes in the data: \u001b[0m\n","1 hive being robbed\n","2 healthy\n","3 few varrao, hive beetles\n","4 ant problems\n","5 missing queen\n","6 Varroa, Small Hive Beetles\n"]}],"source":["print('\\033[1m'+'Target classes in the data: '+'\\033[0m')\n","for index, item in enumerate(list(df['health'].unique())):\n","    print(index+1, item)"]},{"cell_type":"markdown","id":"8dbb8efc","metadata":{"papermill":{"duration":0.013513,"end_time":"2024-06-17T21:06:53.182338","exception":false,"start_time":"2024-06-17T21:06:53.168825","status":"completed"},"tags":[]},"source":["**Let's plot some of the images of our dataset to understand the dataset better.**"]},{"cell_type":"code","execution_count":8,"id":"1bcb95f8","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:06:53.211252Z","iopub.status.busy":"2024-06-17T21:06:53.21095Z","iopub.status.idle":"2024-06-17T21:06:54.029643Z","shell.execute_reply":"2024-06-17T21:06:54.028606Z"},"papermill":{"duration":0.837586,"end_time":"2024-06-17T21:06:54.033684","exception":false,"start_time":"2024-06-17T21:06:53.196098","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","(94, 124) RGB\n","(39, 71) RGB\n","(53, 63) RGB\n","(50, 67) RGB\n","(50, 59) RGB\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 1000x600 with 5 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import matplotlib.pyplot as plt\n","from PIL import Image\n","\n","input_dir = '/kaggle/input/honey-bee-annotated-images/bee_imgs/bee_imgs'\n","# this part of the code displays 4 images at a time\n","fig = plt.figure(figsize=(10, 6))\n","rows = 1\n","columns = 5\n","\n","#display 5 random images from the dataset\n","df2 = df.sample(n=5)\n","print(type(df2))\n","\n","i =0\n","\n","for index, df2_values in df2.iterrows(): #iterates column wise\n","    img = Image.open(os.path.join(input_dir, df2_values['file']))\n","    fig.add_subplot(rows, columns, i+1 if i<5 else i)\n","    img_name = df2_values['health']\n","    plt.imshow(img)\n","    plt.title(img_name)\n","    print(img.size, img.mode)\n","    i+=1"]},{"cell_type":"markdown","id":"e83d022a","metadata":{"papermill":{"duration":0.016018,"end_time":"2024-06-17T21:06:54.06636","exception":false,"start_time":"2024-06-17T21:06:54.050342","status":"completed"},"tags":[]},"source":["**Observation**:\n","1. **Our images are of different sizes, we need to resize them to the same size.**\n","2. **We have 6 categories within our datset. Therefore, we need to create 6 sub-directories within each train, test, and validation folders corresponding to each of the 6 classes and copy the images.** \n","\n","**First, we will determine the image sizes (width, height, and aspect ratio) of the entire dataset**"]},{"cell_type":"code","execution_count":9,"id":"f910536a","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:06:54.100698Z","iopub.status.busy":"2024-06-17T21:06:54.099813Z","iopub.status.idle":"2024-06-17T21:07:28.270242Z","shell.execute_reply":"2024-06-17T21:07:28.269338Z"},"papermill":{"duration":34.190194,"end_time":"2024-06-17T21:07:28.272946","exception":false,"start_time":"2024-06-17T21:06:54.082752","status":"completed"},"tags":[]},"outputs":[],"source":["width =[]\n","height = []\n","AR = []\n","for index, df_values in df.iterrows():\n","    img = Image.open(os.path.join(input_dir, df_values['file']))\n","    w, h = img.size\n","    ar = round((h/w),2)\n","    width.append(w)\n","    height.append(h)\n","    AR.append(ar)\n","\n","df['width'] = width\n","df['height'] = height\n","df['Aspect ratio'] = AR"]},{"cell_type":"code","execution_count":10,"id":"8406de00","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:07:28.307267Z","iopub.status.busy":"2024-06-17T21:07:28.306631Z","iopub.status.idle":"2024-06-17T21:07:28.322987Z","shell.execute_reply":"2024-06-17T21:07:28.321847Z"},"papermill":{"duration":0.035479,"end_time":"2024-06-17T21:07:28.325196","exception":false,"start_time":"2024-06-17T21:07:28.289717","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<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>file</th>\n","      <th>date</th>\n","      <th>time</th>\n","      <th>location</th>\n","      <th>zip code</th>\n","      <th>subspecies</th>\n","      <th>health</th>\n","      <th>pollen_carrying</th>\n","      <th>caste</th>\n","      <th>width</th>\n","      <th>height</th>\n","      <th>Aspect ratio</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>041_066.png</td>\n","      <td>8/28/18</td>\n","      <td>16:07</td>\n","      <td>Alvin, TX, USA</td>\n","      <td>77511</td>\n","      <td>-1</td>\n","      <td>hive being robbed</td>\n","      <td>False</td>\n","      <td>worker</td>\n","      <td>164</td>\n","      <td>115</td>\n","      <td>0.70</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>041_072.png</td>\n","      <td>8/28/18</td>\n","      <td>16:07</td>\n","      <td>Alvin, TX, USA</td>\n","      <td>77511</td>\n","      <td>-1</td>\n","      <td>hive being robbed</td>\n","      <td>False</td>\n","      <td>worker</td>\n","      <td>90</td>\n","      <td>201</td>\n","      <td>2.23</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>041_073.png</td>\n","      <td>8/28/18</td>\n","      <td>16:07</td>\n","      <td>Alvin, TX, USA</td>\n","      <td>77511</td>\n","      <td>-1</td>\n","      <td>hive being robbed</td>\n","      <td>False</td>\n","      <td>worker</td>\n","      <td>167</td>\n","      <td>132</td>\n","      <td>0.79</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>041_067.png</td>\n","      <td>8/28/18</td>\n","      <td>16:07</td>\n","      <td>Alvin, TX, USA</td>\n","      <td>77511</td>\n","      <td>-1</td>\n","      <td>hive being robbed</td>\n","      <td>False</td>\n","      <td>worker</td>\n","      <td>97</td>\n","      <td>134</td>\n","      <td>1.38</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>041_059.png</td>\n","      <td>8/28/18</td>\n","      <td>16:07</td>\n","      <td>Alvin, TX, USA</td>\n","      <td>77511</td>\n","      <td>-1</td>\n","      <td>hive being robbed</td>\n","      <td>False</td>\n","      <td>worker</td>\n","      <td>106</td>\n","      <td>147</td>\n","      <td>1.39</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          file     date   time        location  zip code subspecies  \\\n","0  041_066.png  8/28/18  16:07  Alvin, TX, USA     77511         -1   \n","1  041_072.png  8/28/18  16:07  Alvin, TX, USA     77511         -1   \n","2  041_073.png  8/28/18  16:07  Alvin, TX, USA     77511         -1   \n","3  041_067.png  8/28/18  16:07  Alvin, TX, USA     77511         -1   \n","4  041_059.png  8/28/18  16:07  Alvin, TX, USA     77511         -1   \n","\n","              health  pollen_carrying   caste  width  height  Aspect ratio  \n","0  hive being robbed            False  worker    164     115          0.70  \n","1  hive being robbed            False  worker     90     201          2.23  \n","2  hive being robbed            False  worker    167     132          0.79  \n","3  hive being robbed            False  worker     97     134          1.38  \n","4  hive being robbed            False  worker    106     147          1.39  "]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"code","execution_count":11,"id":"a279a639","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:07:28.358108Z","iopub.status.busy":"2024-06-17T21:07:28.357791Z","iopub.status.idle":"2024-06-17T21:07:28.370346Z","shell.execute_reply":"2024-06-17T21:07:28.369295Z"},"papermill":{"duration":0.031364,"end_time":"2024-06-17T21:07:28.37244","exception":false,"start_time":"2024-06-17T21:07:28.341076","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1mMin width is 27 with index at 4362\u001b[0m\n","\u001b[1mMin height is 24 with index at 5160\u001b[0m\n","\u001b[1mMin Aspect ratio is 0.19\u001b[0m\n","\u001b[1mMax Aspect ratio is 2.65\u001b[0m\n","width     27\n","height    47\n","Name: 4362, dtype: int64\n","width     62\n","height    24\n","Name: 5160, dtype: int64\n"]}],"source":["min_h = df['height'].min()\n","min_w = df['width'].min()\n","min_w_index = df['width'].idxmin()\n","min_h_index = df['height'].idxmin()\n","min_AR = df['Aspect ratio'].min()\n","max_AR = df['Aspect ratio'].max()\n","print('\\033[1m'+ f'Min width is {min_w} with index at {min_w_index}'+'\\033[0m')\n","print('\\033[1m'+ f'Min height is {min_h} with index at {min_h_index}'+'\\033[0m')\n","print('\\033[1m'+ f'Min Aspect ratio is {min_AR}'+'\\033[0m')\n","print('\\033[1m'+ f'Max Aspect ratio is {max_AR}'+'\\033[0m')\n","print(df[['width', 'height']].iloc[min_w_index])\n","print(df[['width', 'height']].iloc[min_h_index])"]},{"cell_type":"code","execution_count":12,"id":"d9c29d96","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:07:28.406349Z","iopub.status.busy":"2024-06-17T21:07:28.40605Z","iopub.status.idle":"2024-06-17T21:07:28.411575Z","shell.execute_reply":"2024-06-17T21:07:28.41068Z"},"papermill":{"duration":0.025626,"end_time":"2024-06-17T21:07:28.414205","exception":false,"start_time":"2024-06-17T21:07:28.388579","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1mMax width is 392 & height is 520\u001b[0m\n"]}],"source":["max_h = df['height'].max()\n","max_w = df['width'].max()\n","print('\\033[1m'+ f'Max width is {max_w} & height is {max_h}' + '\\033[0m')"]},{"cell_type":"markdown","id":"fb0818be","metadata":{"papermill":{"duration":0.016414,"end_time":"2024-06-17T21:07:28.447184","exception":false,"start_time":"2024-06-17T21:07:28.43077","status":"completed"},"tags":[]},"source":["**So we see that the minimum sized images are of the size (27, 47) and (62, 24). The images in this dataset have very differently sized images (the range of aspect ratio is 0.19-2.65). Accoridng to one source (https://neptune.ai/blog/data-exploration-for-image-segmentation-and-object-detection), in such cases, selctive sampling is a better approach then resizing all the images.**"]},{"cell_type":"code","execution_count":13,"id":"a0ff4822","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:07:28.481686Z","iopub.status.busy":"2024-06-17T21:07:28.480875Z","iopub.status.idle":"2024-06-17T21:07:28.490833Z","shell.execute_reply":"2024-06-17T21:07:28.490094Z"},"papermill":{"duration":0.029472,"end_time":"2024-06-17T21:07:28.49298","exception":false,"start_time":"2024-06-17T21:07:28.463508","status":"completed"},"tags":[]},"outputs":[],"source":["#creating train directory\n","\n","class_1 = 'hive being robbed'\n","class_1_dir = os.path.join(train_path, class_1)\n","if not os.path.exists(class_1_dir):\n","    os.mkdir(class_1_dir)\n","    \n","class_2 = 'healthy'\n","class_2_dir = os.path.join(train_path, class_2)\n","if not os.path.exists(class_2_dir):\n","    os.mkdir(class_2_dir)\n","    \n","class_3 = 'few varrao, hive beetles'\n","class_3_dir = os.path.join(train_path, class_3)\n","if not os.path.exists(class_3_dir):\n","    os.mkdir(class_3_dir)\n","    \n","class_4 = 'ant problems'\n","class_4_dir = os.path.join(train_path, class_4)\n","if not os.path.exists(class_4_dir):\n","    os.mkdir(class_4_dir)\n","    \n","class_5 = 'missing queen'\n","class_5_dir = os.path.join(train_path, class_5)\n","if not os.path.exists(class_5_dir):\n","    os.mkdir(class_5_dir)\n","    \n","class_6 = 'Varroa, Small Hive Beetles'\n","class_6_dir = os.path.join(train_path, class_6)\n","if not os.path.exists(class_6_dir):\n","    os.mkdir(class_6_dir)"]},{"cell_type":"code","execution_count":14,"id":"975f2760","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:07:28.526579Z","iopub.status.busy":"2024-06-17T21:07:28.526315Z","iopub.status.idle":"2024-06-17T21:07:28.53659Z","shell.execute_reply":"2024-06-17T21:07:28.535825Z"},"papermill":{"duration":0.029502,"end_time":"2024-06-17T21:07:28.53876","exception":false,"start_time":"2024-06-17T21:07:28.509258","status":"completed"},"tags":[]},"outputs":[],"source":["#creating test directory\n","\n","class_1 = 'hive being robbed'\n","class_1_dir = os.path.join(test_path, class_1)\n","if not os.path.exists(class_1_dir):\n","    os.mkdir(class_1_dir)\n","    \n","class_2 = 'healthy'\n","class_2_dir = os.path.join(test_path, class_2)\n","if not os.path.exists(class_2_dir):\n","    os.mkdir(class_2_dir)\n","    \n","class_3 = 'few varrao, hive beetles'\n","class_3_dir = os.path.join(test_path, class_3)\n","if not os.path.exists(class_3_dir):\n","    os.mkdir(class_3_dir)\n","    \n","class_4 = 'ant problems'\n","class_4_dir = os.path.join(test_path, class_4)\n","if not os.path.exists(class_4_dir):\n","    os.mkdir(class_4_dir)\n","    \n","class_5 = 'missing queen'\n","class_5_dir = os.path.join(test_path, class_5)\n","if not os.path.exists(class_5_dir):\n","    os.mkdir(class_5_dir)\n","    \n","class_6 = 'Varroa, Small Hive Beetles'\n","class_6_dir = os.path.join(test_path, class_6)\n","if not os.path.exists(class_6_dir):\n","    os.mkdir(class_6_dir)"]},{"cell_type":"code","execution_count":15,"id":"306f1418","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:07:28.574091Z","iopub.status.busy":"2024-06-17T21:07:28.573556Z","iopub.status.idle":"2024-06-17T21:07:28.583595Z","shell.execute_reply":"2024-06-17T21:07:28.582694Z"},"papermill":{"duration":0.03071,"end_time":"2024-06-17T21:07:28.58572","exception":false,"start_time":"2024-06-17T21:07:28.55501","status":"completed"},"tags":[]},"outputs":[],"source":["#creating validation directory\n","\n","class_1 = 'hive being robbed'\n","class_1_dir = os.path.join(val_path, class_1)\n","if not os.path.exists(class_1_dir):\n","    os.mkdir(class_1_dir)\n","    \n","class_2 = 'healthy'\n","class_2_dir = os.path.join(val_path, class_2)\n","if not os.path.exists(class_2_dir):\n","    os.mkdir(class_2_dir)\n","    \n","class_3 = 'few varrao, hive beetles'\n","class_3_dir = os.path.join(val_path, class_3)\n","if not os.path.exists(class_3_dir):\n","    os.mkdir(class_3_dir)\n","    \n","class_4 = 'ant problems'\n","class_4_dir = os.path.join(val_path, class_4)\n","if not os.path.exists(class_4_dir):\n","    os.mkdir(class_4_dir)\n","    \n","class_5 = 'missing queen'\n","class_5_dir = os.path.join(val_path, class_5)\n","if not os.path.exists(class_5_dir):\n","    os.mkdir(class_5_dir)\n","    \n","class_6 = 'Varroa, Small Hive Beetles'\n","class_6_dir = os.path.join(val_path, class_6)\n","if not os.path.exists(class_6_dir):\n","    os.mkdir(class_6_dir)"]},{"cell_type":"markdown","id":"6472fd3d","metadata":{"papermill":{"duration":0.01603,"end_time":"2024-06-17T21:07:28.618198","exception":false,"start_time":"2024-06-17T21:07:28.602168","status":"completed"},"tags":[]},"source":["****Since the kaggle input directory is a read only directory, we need to move the images and the csv file in the working directory in order to be able to make changes to them.****"]},{"cell_type":"code","execution_count":16,"id":"d93a6b47","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:07:28.651796Z","iopub.status.busy":"2024-06-17T21:07:28.651172Z","iopub.status.idle":"2024-06-17T21:07:33.792105Z","shell.execute_reply":"2024-06-17T21:07:33.790895Z"},"papermill":{"duration":5.160346,"end_time":"2024-06-17T21:07:33.794629","exception":false,"start_time":"2024-06-17T21:07:28.634283","status":"completed"},"tags":[]},"outputs":[],"source":["!cp -r /kaggle/input/honey-bee-annotated-images/ ./ "]},{"cell_type":"markdown","id":"733ff7bf","metadata":{"papermill":{"duration":0.015832,"end_time":"2024-06-17T21:07:33.827261","exception":false,"start_time":"2024-06-17T21:07:33.811429","status":"completed"},"tags":[]},"source":["**we will update the complete path to the images in the dataframe['file'] column**"]},{"cell_type":"code","execution_count":17,"id":"9ce76e4e","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:07:33.860954Z","iopub.status.busy":"2024-06-17T21:07:33.860263Z","iopub.status.idle":"2024-06-17T21:07:34.152893Z","shell.execute_reply":"2024-06-17T21:07:34.151949Z"},"papermill":{"duration":0.311862,"end_time":"2024-06-17T21:07:34.155018","exception":false,"start_time":"2024-06-17T21:07:33.843156","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["'/kaggle/working/honey-bee-annotated-images/bee_imgs/bee_imgs/041_072.png'"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["images_dir = '/kaggle/working/honey-bee-annotated-images/bee_imgs/bee_imgs'\n","list_dir = []\n","for index, row in df.iterrows():\n","    list_dir.append(os.path.join(images_dir, row['file']))\n","df['file'] = list_dir\n","df['file'][1]"]},{"cell_type":"code","execution_count":18,"id":"934bc6a5","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:07:34.188787Z","iopub.status.busy":"2024-06-17T21:07:34.188507Z","iopub.status.idle":"2024-06-17T21:07:34.205179Z","shell.execute_reply":"2024-06-17T21:07:34.204232Z"},"papermill":{"duration":0.035854,"end_time":"2024-06-17T21:07:34.20731","exception":false,"start_time":"2024-06-17T21:07:34.171456","status":"completed"},"tags":[]},"outputs":[{"data":{"text/html":["<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>file</th>\n","      <th>date</th>\n","      <th>time</th>\n","      <th>location</th>\n","      <th>zip code</th>\n","      <th>subspecies</th>\n","      <th>health</th>\n","      <th>pollen_carrying</th>\n","      <th>caste</th>\n","      <th>width</th>\n","      <th>height</th>\n","      <th>Aspect ratio</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>/kaggle/working/honey-bee-annotated-images/bee...</td>\n","      <td>8/28/18</td>\n","      <td>16:07</td>\n","      <td>Alvin, TX, USA</td>\n","      <td>77511</td>\n","      <td>-1</td>\n","      <td>hive being robbed</td>\n","      <td>False</td>\n","      <td>worker</td>\n","      <td>164</td>\n","      <td>115</td>\n","      <td>0.70</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>/kaggle/working/honey-bee-annotated-images/bee...</td>\n","      <td>8/28/18</td>\n","      <td>16:07</td>\n","      <td>Alvin, TX, USA</td>\n","      <td>77511</td>\n","      <td>-1</td>\n","      <td>hive being robbed</td>\n","      <td>False</td>\n","      <td>worker</td>\n","      <td>90</td>\n","      <td>201</td>\n","      <td>2.23</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>/kaggle/working/honey-bee-annotated-images/bee...</td>\n","      <td>8/28/18</td>\n","      <td>16:07</td>\n","      <td>Alvin, TX, USA</td>\n","      <td>77511</td>\n","      <td>-1</td>\n","      <td>hive being robbed</td>\n","      <td>False</td>\n","      <td>worker</td>\n","      <td>167</td>\n","      <td>132</td>\n","      <td>0.79</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>/kaggle/working/honey-bee-annotated-images/bee...</td>\n","      <td>8/28/18</td>\n","      <td>16:07</td>\n","      <td>Alvin, TX, USA</td>\n","      <td>77511</td>\n","      <td>-1</td>\n","      <td>hive being robbed</td>\n","      <td>False</td>\n","      <td>worker</td>\n","      <td>97</td>\n","      <td>134</td>\n","      <td>1.38</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>/kaggle/working/honey-bee-annotated-images/bee...</td>\n","      <td>8/28/18</td>\n","      <td>16:07</td>\n","      <td>Alvin, TX, USA</td>\n","      <td>77511</td>\n","      <td>-1</td>\n","      <td>hive being robbed</td>\n","      <td>False</td>\n","      <td>worker</td>\n","      <td>106</td>\n","      <td>147</td>\n","      <td>1.39</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                file     date   time  \\\n","0  /kaggle/working/honey-bee-annotated-images/bee...  8/28/18  16:07   \n","1  /kaggle/working/honey-bee-annotated-images/bee...  8/28/18  16:07   \n","2  /kaggle/working/honey-bee-annotated-images/bee...  8/28/18  16:07   \n","3  /kaggle/working/honey-bee-annotated-images/bee...  8/28/18  16:07   \n","4  /kaggle/working/honey-bee-annotated-images/bee...  8/28/18  16:07   \n","\n","         location  zip code subspecies             health  pollen_carrying  \\\n","0  Alvin, TX, USA     77511         -1  hive being robbed            False   \n","1  Alvin, TX, USA     77511         -1  hive being robbed            False   \n","2  Alvin, TX, USA     77511         -1  hive being robbed            False   \n","3  Alvin, TX, USA     77511         -1  hive being robbed            False   \n","4  Alvin, TX, USA     77511         -1  hive being robbed            False   \n","\n","    caste  width  height  Aspect ratio  \n","0  worker    164     115          0.70  \n","1  worker     90     201          2.23  \n","2  worker    167     132          0.79  \n","3  worker     97     134          1.38  \n","4  worker    106     147          1.39  "]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"code","execution_count":19,"id":"1d9f74e3","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:07:34.242314Z","iopub.status.busy":"2024-06-17T21:07:34.2416Z","iopub.status.idle":"2024-06-17T21:07:54.864099Z","shell.execute_reply":"2024-06-17T21:07:54.863089Z"},"papermill":{"duration":20.642535,"end_time":"2024-06-17T21:07:54.866715","exception":false,"start_time":"2024-06-17T21:07:34.22418","status":"completed"},"tags":[]},"outputs":[],"source":["for index, value in df['file'].items():\n","    img = Image.open(value)\n","    new_img = img.resize((100, 100))\n","    new_img.save(value)\n","    #print(new_img.size)"]},{"cell_type":"markdown","id":"0aa98401","metadata":{"papermill":{"duration":0.016525,"end_time":"2024-06-17T21:07:54.900258","exception":false,"start_time":"2024-06-17T21:07:54.883733","status":"completed"},"tags":[]},"source":["**Now, that the images have been resized, we will move them to their corresponding sub-directories.**"]},{"cell_type":"code","execution_count":20,"id":"693286b8","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:07:54.93641Z","iopub.status.busy":"2024-06-17T21:07:54.935772Z","iopub.status.idle":"2024-06-17T21:08:02.678493Z","shell.execute_reply":"2024-06-17T21:08:02.677553Z"},"papermill":{"duration":7.763339,"end_time":"2024-06-17T21:08:02.681123","exception":false,"start_time":"2024-06-17T21:07:54.917784","status":"completed"},"tags":[]},"outputs":[],"source":["import random \n","import shutil\n","import math\n","\n","no_images = len_df #no. of total images we have\n","train_size = math.floor(0.7*no_images) # no. of training images\n","#print(train_size) #returns 3620 images\n","test_size = math.floor(0.2*no_images) #testing images\n","#print(test_size) #returns 1034 images\n","\n","# list of images in the images directory\n","images_list = list(df['file'])\n","random.shuffle(images_list) #shuffling the images so that we get different classes in the train data\n","#print(images_list[:5])\n","\n","#creating a random seed to import the same images everytime \n","random.seed(10)\n","train_images = images_list[:train_size]\n","#print(train_images[:5])\n","test_images = images_list[train_size: train_size+test_size]\n","#print(test_images[:5])\n","val_images = images_list[train_size+test_size:] #use the remaining images in the images list\n","#print(val_images[:5])\n","#print(len(val_images)) #returns returning images\n","\n","\n","for img in train_images: #img is path to images in working directory\n","    label = df['health'].loc[df['file']==img].to_string(index=False)\n","    path = os.path.join(train_path, label)\n","    #print(path)\n","    shutil.move(img, path)\n","    \n","\n","for img in test_images:\n","    label = df['health'].loc[df['file']==img].to_string(index=False)\n","    path = os.path.join(test_path, label)\n","    shutil.move(img, path)\n","    \n","\n","for img in val_images:\n","    label = df['health'].loc[df['file']==img].to_string(index=False)\n","    path = os.path.join(val_path, label) \n","    shutil.move(img, path) "]},{"cell_type":"code","execution_count":21,"id":"e2ab292f","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:08:02.716259Z","iopub.status.busy":"2024-06-17T21:08:02.715523Z","iopub.status.idle":"2024-06-17T21:08:02.723235Z","shell.execute_reply":"2024-06-17T21:08:02.722246Z"},"papermill":{"duration":0.027305,"end_time":"2024-06-17T21:08:02.725476","exception":false,"start_time":"2024-06-17T21:08:02.698171","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["2360"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["len(os.listdir('/kaggle/working/train/healthy'))"]},{"cell_type":"markdown","id":"ff6ec0be","metadata":{"papermill":{"duration":0.016605,"end_time":"2024-06-17T21:08:02.758737","exception":false,"start_time":"2024-06-17T21:08:02.742132","status":"completed"},"tags":[]},"source":["**Now that we have Dataset prepared, we will apply data transformation to augment our training dataset.**"]},{"cell_type":"markdown","id":"a0803742","metadata":{"papermill":{"duration":0.01694,"end_time":"2024-06-17T21:08:02.792408","exception":false,"start_time":"2024-06-17T21:08:02.775468","status":"completed"},"tags":[]},"source":["**Data Augmentation**"]},{"cell_type":"code","execution_count":22,"id":"56e97b13","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:08:02.827358Z","iopub.status.busy":"2024-06-17T21:08:02.826527Z","iopub.status.idle":"2024-06-17T21:08:05.269518Z","shell.execute_reply":"2024-06-17T21:08:05.268614Z"},"papermill":{"duration":2.462988,"end_time":"2024-06-17T21:08:05.272079","exception":false,"start_time":"2024-06-17T21:08:02.809091","status":"completed"},"tags":[]},"outputs":[],"source":["import torch \n","from torchvision import datasets, models, transforms\n","import torch.utils.data as data"]},{"cell_type":"code","execution_count":23,"id":"258e20d6","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:08:05.308192Z","iopub.status.busy":"2024-06-17T21:08:05.307234Z","iopub.status.idle":"2024-06-17T21:08:05.315082Z","shell.execute_reply":"2024-06-17T21:08:05.314136Z"},"papermill":{"duration":0.027939,"end_time":"2024-06-17T21:08:05.317249","exception":false,"start_time":"2024-06-17T21:08:05.28931","status":"completed"},"tags":[]},"outputs":[],"source":["# Applying Transforms to the Data\n","image_transforms = { \n","    'train': transforms.Compose([\n","        transforms.RandomRotation(degrees=15),\n","        transforms.RandomHorizontalFlip(),\n","        transforms.ToTensor(),\n","        transforms.Normalize([0.5, 0.5, 0.5],\n","                             [0.5, 0.5, 0.5])\n","    ]),\n","    'valid': transforms.Compose([\n","        transforms.ToTensor(),\n","        transforms.Normalize([0.5, 0.5, 0.5],\n","                             [0.5, 0.5, 0.5])\n","    ]),\n","    'test': transforms.Compose([\n","        transforms.ToTensor(),\n","        transforms.Normalize([0.5, 0.5, 0.5],\n","                             [0.5, 0.5, 0.5])\n","    ])\n","}"]},{"cell_type":"markdown","id":"5a11ae3e","metadata":{"papermill":{"duration":0.016494,"end_time":"2024-06-17T21:08:05.350249","exception":false,"start_time":"2024-06-17T21:08:05.333755","status":"completed"},"tags":[]},"source":["**Data Loading**"]},{"cell_type":"code","execution_count":24,"id":"4002bde3","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:08:05.384962Z","iopub.status.busy":"2024-06-17T21:08:05.384178Z","iopub.status.idle":"2024-06-17T21:08:05.419675Z","shell.execute_reply":"2024-06-17T21:08:05.418751Z"},"papermill":{"duration":0.055013,"end_time":"2024-06-17T21:08:05.421809","exception":false,"start_time":"2024-06-17T21:08:05.366796","status":"completed"},"tags":[]},"outputs":[],"source":["# Load the Data\n","# train_path, val_path, test_path\n","\n","# Batch size\n","bs = 16\n","\n","# no. of classes = num_classes\n","\n","# Load Data from folders\n","dataset = {\n","    'train': datasets.ImageFolder(root=train_path, transform=image_transforms['train']),\n","    'valid': datasets.ImageFolder(root=val_path, transform=image_transforms['valid']),\n","    'test': datasets.ImageFolder(root=test_path, transform=image_transforms['test'])\n","}\n","\n","# Size of Data, to be used for calculating Average Loss and Accuracy\n","dataset_sizes = {\n","    'train':len(dataset['train']),\n","    'valid':len(dataset['valid']),\n","    'test':len(dataset['test'])\n","}\n","\n","# Create iterators for the Data loaded using DataLoader module\n","dataloaders = {\n","    'train':data.DataLoader(dataset['train'], batch_size=bs, shuffle=True),\n","    'valid':data.DataLoader(dataset['valid'], batch_size=bs, shuffle=True),\n","    'test':data.DataLoader(dataset['test'], batch_size=bs, shuffle=True)\n","}"]},{"cell_type":"code","execution_count":25,"id":"e65c87a7","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:08:05.456812Z","iopub.status.busy":"2024-06-17T21:08:05.456517Z","iopub.status.idle":"2024-06-17T21:08:05.461856Z","shell.execute_reply":"2024-06-17T21:08:05.460967Z"},"papermill":{"duration":0.02555,"end_time":"2024-06-17T21:08:05.464194","exception":false,"start_time":"2024-06-17T21:08:05.438644","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Classes: ['Varroa, Small Hive Beetles', 'ant problems', 'few varrao, hive beetles', 'healthy', 'hive being robbed', 'missing queen']\n","Training-set size: 3620 \n","Validation-set size: 518\n"]}],"source":["# Class names or target labels\n","class_names = dataset['train'].classes\n","print(\"Classes:\", class_names)\n"," \n","# Print the train and validation data sizes\n","print(\"Training-set size:\",dataset_sizes['train'],\n","      \"\\nValidation-set size:\", dataset_sizes['valid'])"]},{"cell_type":"markdown","id":"fcafb7e4","metadata":{"papermill":{"duration":0.016754,"end_time":"2024-06-17T21:08:05.498452","exception":false,"start_time":"2024-06-17T21:08:05.481698","status":"completed"},"tags":[]},"source":["**Transfer Learning**\n","\n","We are going to implement ResNet50 model and train with the bee dataset."]},{"cell_type":"code","execution_count":26,"id":"a96cfef3","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:08:05.535146Z","iopub.status.busy":"2024-06-17T21:08:05.534428Z","iopub.status.idle":"2024-06-17T21:08:06.876776Z","shell.execute_reply":"2024-06-17T21:08:06.875898Z"},"papermill":{"duration":1.363667,"end_time":"2024-06-17T21:08:06.879479","exception":false,"start_time":"2024-06-17T21:08:05.515812","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["/opt/conda/lib/python3.7/site-packages/torchvision/models/_utils.py:209: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n","  f\"The parameter '{pretrained_param}' is deprecated since 0.13 and may be removed in the future, \"\n","/opt/conda/lib/python3.7/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n","  warnings.warn(msg)\n","Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"57e6cfd9db29418784089a72469db8fa","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0.00/97.8M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"}],"source":["# Load pretrained ResNet50 Model\n","resnet50 = models.resnet50(pretrained=True)"]},{"cell_type":"code","execution_count":27,"id":"99a919cb","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:08:06.915703Z","iopub.status.busy":"2024-06-17T21:08:06.915102Z","iopub.status.idle":"2024-06-17T21:08:06.920605Z","shell.execute_reply":"2024-06-17T21:08:06.919672Z"},"papermill":{"duration":0.02573,"end_time":"2024-06-17T21:08:06.922832","exception":false,"start_time":"2024-06-17T21:08:06.897102","status":"completed"},"tags":[]},"outputs":[],"source":["# Freeze model parameters\n","for param in resnet50.parameters():\n","    param.requires_grad = False"]},{"cell_type":"code","execution_count":28,"id":"d04a6121","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:08:06.959286Z","iopub.status.busy":"2024-06-17T21:08:06.958654Z","iopub.status.idle":"2024-06-17T21:08:09.677833Z","shell.execute_reply":"2024-06-17T21:08:09.676961Z"},"papermill":{"duration":2.740405,"end_time":"2024-06-17T21:08:09.68048","exception":false,"start_time":"2024-06-17T21:08:06.940075","status":"completed"},"tags":[]},"outputs":[],"source":["import torch.nn as nn\n","import torch.optim as optim\n","from torch.optim import lr_scheduler\n","\n","# Set default device as gpu, if available\n","device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n","\n","# Change the final layer of ResNet50 Model for Transfer Learning\n","resnet50.fc = nn.Sequential(\n","    nn.Linear(resnet50.fc.in_features, 256),\n","    nn.ReLU(),\n","    nn.Dropout(0.4),\n","    nn.Linear(256, 6), \n","    nn.LogSoftmax(dim=1) # For using NLLLoss()\n",")\n","\n","# Convert model to be used on GPU\n","resnet50 = resnet50.to(device)\n","\n","# Define Optimizer and Loss Function\n","loss_func = nn.NLLLoss() #Negative Loss Likelihood function useful for classifying multiple classes\n","optimizer = optim.Adam(resnet50.parameters())"]},{"cell_type":"markdown","id":"8f1e2d4d","metadata":{"papermill":{"duration":0.017973,"end_time":"2024-06-17T21:08:09.716574","exception":false,"start_time":"2024-06-17T21:08:09.698601","status":"completed"},"tags":[]},"source":["**Training the model**"]},{"cell_type":"code","execution_count":29,"id":"21aa93b3","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:08:09.752695Z","iopub.status.busy":"2024-06-17T21:08:09.751887Z","iopub.status.idle":"2024-06-17T21:08:09.75638Z","shell.execute_reply":"2024-06-17T21:08:09.755428Z"},"papermill":{"duration":0.024994,"end_time":"2024-06-17T21:08:09.758559","exception":false,"start_time":"2024-06-17T21:08:09.733565","status":"completed"},"tags":[]},"outputs":[],"source":["import time "]},{"cell_type":"code","execution_count":30,"id":"8af16168","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:08:09.794822Z","iopub.status.busy":"2024-06-17T21:08:09.794519Z","iopub.status.idle":"2024-06-17T21:08:09.798841Z","shell.execute_reply":"2024-06-17T21:08:09.797978Z"},"papermill":{"duration":0.025481,"end_time":"2024-06-17T21:08:09.801148","exception":false,"start_time":"2024-06-17T21:08:09.775667","status":"completed"},"tags":[]},"outputs":[],"source":["epochs = 30"]},{"cell_type":"code","execution_count":31,"id":"f16248e9","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:08:09.836527Z","iopub.status.busy":"2024-06-17T21:08:09.835958Z","iopub.status.idle":"2024-06-17T21:08:09.840658Z","shell.execute_reply":"2024-06-17T21:08:09.839756Z"},"papermill":{"duration":0.024707,"end_time":"2024-06-17T21:08:09.842754","exception":false,"start_time":"2024-06-17T21:08:09.818047","status":"completed"},"tags":[]},"outputs":[],"source":["# Learning rate decay\n","scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)"]},{"cell_type":"code","execution_count":32,"id":"ae931acb","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:08:09.878373Z","iopub.status.busy":"2024-06-17T21:08:09.877772Z","iopub.status.idle":"2024-06-17T21:08:09.882394Z","shell.execute_reply":"2024-06-17T21:08:09.881479Z"},"papermill":{"duration":0.024514,"end_time":"2024-06-17T21:08:09.884519","exception":false,"start_time":"2024-06-17T21:08:09.860005","status":"completed"},"tags":[]},"outputs":[],"source":["def get_loss_acc():\n","    train_losses_list =[]\n","    train_acc_list = []\n","    valid_losses_list = []\n","    valid_acc_list = []\n","    \n","    return train_losses_list"]},{"cell_type":"code","execution_count":33,"id":"49c78a52","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:08:09.919714Z","iopub.status.busy":"2024-06-17T21:08:09.919434Z","iopub.status.idle":"2024-06-17T21:12:20.070183Z","shell.execute_reply":"2024-06-17T21:12:20.069146Z"},"papermill":{"duration":250.171339,"end_time":"2024-06-17T21:12:20.072772","exception":false,"start_time":"2024-06-17T21:08:09.901433","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch: 1/30\n","Epoch : 000, Training: Loss: 0.6750, Accuracy: 75.7735%, nttValidation : Loss : 0.4417, Accuracy: 85.5212%, Time: 13.2275s\n","Epoch: 2/30\n","Epoch : 001, Training: Loss: 0.5026, Accuracy: 81.5746%, nttValidation : Loss : 0.4488, Accuracy: 83.7838%, Time: 8.1570s\n","Epoch: 3/30\n","Epoch : 002, Training: Loss: 0.4534, Accuracy: 82.1271%, nttValidation : Loss : 0.3765, Accuracy: 85.3282%, Time: 8.3512s\n","Epoch: 4/30\n","Epoch : 003, Training: Loss: 0.3699, Accuracy: 85.5525%, nttValidation : Loss : 0.4860, Accuracy: 79.9228%, Time: 8.1357s\n","Epoch: 5/30\n","Epoch : 004, Training: Loss: 0.3914, Accuracy: 85.0552%, nttValidation : Loss : 0.3277, Accuracy: 86.4865%, Time: 8.1058s\n","Epoch: 6/30\n","Epoch : 005, Training: Loss: 0.3746, Accuracy: 85.1934%, nttValidation : Loss : 0.2391, Accuracy: 89.7683%, Time: 8.1303s\n","Epoch: 7/30\n","Epoch : 006, Training: Loss: 0.3718, Accuracy: 85.8564%, nttValidation : Loss : 0.3179, Accuracy: 85.1351%, Time: 8.1588s\n","Epoch: 8/30\n","Epoch : 007, Training: Loss: 0.3627, Accuracy: 86.1050%, nttValidation : Loss : 0.2373, Accuracy: 88.6100%, Time: 8.2548s\n","Epoch: 9/30\n","Epoch : 008, Training: Loss: 0.3418, Accuracy: 86.5470%, nttValidation : Loss : 0.2378, Accuracy: 90.3475%, Time: 8.1232s\n","Epoch: 10/30\n","Epoch : 009, Training: Loss: 0.2999, Accuracy: 88.7569%, nttValidation : Loss : 0.2700, Accuracy: 88.4170%, Time: 8.1866s\n","Epoch: 11/30\n","Epoch : 010, Training: Loss: 0.3095, Accuracy: 88.5359%, nttValidation : Loss : 0.2437, Accuracy: 88.6100%, Time: 8.2504s\n","Epoch: 12/30\n","Epoch : 011, Training: Loss: 0.3059, Accuracy: 88.1215%, nttValidation : Loss : 0.2348, Accuracy: 88.6100%, Time: 8.2813s\n","Epoch: 13/30\n","Epoch : 012, Training: Loss: 0.3116, Accuracy: 88.6740%, nttValidation : Loss : 0.2471, Accuracy: 89.3822%, Time: 8.1591s\n","Epoch: 14/30\n","Epoch : 013, Training: Loss: 0.3045, Accuracy: 88.1215%, nttValidation : Loss : 0.1852, Accuracy: 90.7336%, Time: 8.2519s\n","Epoch: 15/30\n","Epoch : 014, Training: Loss: 0.3184, Accuracy: 87.9282%, nttValidation : Loss : 0.1889, Accuracy: 92.6641%, Time: 8.1984s\n","Epoch: 16/30\n","Epoch : 015, Training: Loss: 0.2699, Accuracy: 89.7790%, nttValidation : Loss : 0.1905, Accuracy: 91.6988%, Time: 8.1327s\n","Epoch: 17/30\n","Epoch : 016, Training: Loss: 0.2982, Accuracy: 89.3923%, nttValidation : Loss : 0.2063, Accuracy: 91.1197%, Time: 8.0760s\n","Epoch: 18/30\n","Epoch : 017, Training: Loss: 0.3039, Accuracy: 88.9503%, nttValidation : Loss : 0.1843, Accuracy: 91.1197%, Time: 8.1856s\n","Epoch: 19/30\n","Epoch : 018, Training: Loss: 0.2897, Accuracy: 88.6740%, nttValidation : Loss : 0.1967, Accuracy: 89.3822%, Time: 8.1713s\n","Epoch: 20/30\n","Epoch : 019, Training: Loss: 0.2924, Accuracy: 88.9227%, nttValidation : Loss : 0.1889, Accuracy: 90.7336%, Time: 8.1230s\n","Epoch: 21/30\n","Epoch : 020, Training: Loss: 0.2483, Accuracy: 90.3039%, nttValidation : Loss : 0.2084, Accuracy: 91.1197%, Time: 8.0680s\n","Epoch: 22/30\n","Epoch : 021, Training: Loss: 0.2793, Accuracy: 89.2265%, nttValidation : Loss : 0.1779, Accuracy: 91.6988%, Time: 8.1758s\n","Epoch: 23/30\n","Epoch : 022, Training: Loss: 0.2776, Accuracy: 89.5028%, nttValidation : Loss : 0.1760, Accuracy: 91.1197%, Time: 8.1723s\n","Epoch: 24/30\n","Epoch : 023, Training: Loss: 0.2648, Accuracy: 89.8619%, nttValidation : Loss : 0.1820, Accuracy: 91.5058%, Time: 8.0802s\n","Epoch: 25/30\n","Epoch : 024, Training: Loss: 0.2724, Accuracy: 88.9227%, nttValidation : Loss : 0.1966, Accuracy: 90.5405%, Time: 8.1176s\n","Epoch: 26/30\n","Epoch : 025, Training: Loss: 0.2759, Accuracy: 89.5304%, nttValidation : Loss : 0.1840, Accuracy: 90.7336%, Time: 8.1579s\n","Epoch: 27/30\n","Epoch : 026, Training: Loss: 0.2459, Accuracy: 90.5249%, nttValidation : Loss : 0.1538, Accuracy: 93.2432%, Time: 8.1494s\n","Epoch: 28/30\n","Epoch : 027, Training: Loss: 0.2653, Accuracy: 90.1105%, nttValidation : Loss : 0.1934, Accuracy: 91.6988%, Time: 8.1383s\n","Epoch: 29/30\n","Epoch : 028, Training: Loss: 0.2508, Accuracy: 89.7790%, nttValidation : Loss : 0.2081, Accuracy: 89.7683%, Time: 8.1855s\n","Epoch: 30/30\n","Epoch : 029, Training: Loss: 0.2674, Accuracy: 89.1989%, nttValidation : Loss : 0.1762, Accuracy: 91.8919%, Time: 8.2237s\n"]}],"source":["train_losses_list = []\n","train_acc_list = []\n","valid_losses_list = []\n","valid_acc_list = []\n","for epoch in range(epochs):\n","        epoch_start = time.time()\n","        print(\"Epoch: {}/{}\".format(epoch+1, epochs))\n","        # Set to training mode\n","        resnet50.train()\n","        # Loss and Accuracy within the epoch\n","        train_loss = 0.0\n","        train_acc = 0.0\n","        valid_loss = 0.0\n","        valid_acc = 0.0\n","        for i, (inputs, labels) in enumerate(dataloaders['train']):\n","            inputs = inputs.to(device)\n","            labels = labels.to(device)\n","            # Clean existing gradients\n","            optimizer.zero_grad()\n","            # Forward pass - compute outputs on input data using the model\n","            outputs = resnet50(inputs)\n","            # Compute loss\n","            loss = loss_func(outputs, labels)\n","            # Backpropagate the gradients\n","            loss.backward()\n","            # Update the parameters\n","            optimizer.step()\n","            # Compute the total loss for the batch and add it to train_loss\n","            train_loss += loss.item() * inputs.size(0)\n","            #scheduler.step()\n","            \n","            # Compute the accuracy\n","            ret, predictions = torch.max(outputs.data, 1)\n","            correct_counts = predictions.eq(labels.data.view_as(predictions))\n","            # Convert correct_counts to float and then compute the mean\n","            acc = torch.mean(correct_counts.type(torch.FloatTensor))\n","            # Compute total accuracy in the whole batch and add to train_acc\n","            train_acc += acc.item() * inputs.size(0)\n","            \n","            \n","        # Validation - No gradient tracking needed\n","        with torch.no_grad():\n","            # Set to evaluation mode\n","            resnet50.eval()\n","            # Validation loop\n","            for j, (inputs, labels) in enumerate(dataloaders['valid']):\n","                inputs = inputs.to(device)\n","                labels = labels.to(device)\n","                # Forward pass - compute outputs on input data using the model\n","                outputs = resnet50(inputs)\n","                # Compute loss\n","                loss = loss_func(outputs, labels)\n","                # Compute the total loss for the batch and add it to valid_loss\n","                valid_loss += loss.item() * inputs.size(0)\n","                \n","                # Calculate validation accuracy\n","                ret, predictions = torch.max(outputs.data, 1)\n","                correct_counts = predictions.eq(labels.data.view_as(predictions))\n","                # Convert correct_counts to float and then compute the mean\n","                acc = torch.mean(correct_counts.type(torch.FloatTensor))\n","                # Compute total accuracy in the whole batch and add to valid_acc\n","                valid_acc += acc.item() * inputs.size(0)\n","                \n","                \n","        # Find average training loss and accuracy\n","        avg_train_loss = train_loss/dataset_sizes['train']\n","        train_losses_list.append(avg_train_loss)\n","        avg_train_acc = train_acc/float(dataset_sizes['train'])\n","        train_acc_list.append(avg_train_acc)\n","        \n","        # Find average validation loss and accuracy\n","        avg_valid_loss = valid_loss/dataset_sizes['valid'] \n","        valid_losses_list.append(avg_valid_loss)\n","        avg_valid_acc = valid_acc/float(dataset_sizes['valid'])\n","        valid_acc_list.append(avg_valid_acc)\n","        \n","        epoch_end = time.time()\n","        print(\"Epoch : {:03d}, Training: Loss: {:.4f}, Accuracy: {:.4f}%, nttValidation : Loss : {:.4f}, Accuracy: {:.4f}%, Time: {:.4f}s\".format(epoch, avg_train_loss, avg_train_acc*100, avg_valid_loss, avg_valid_acc*100, epoch_end-epoch_start))\n"]},{"cell_type":"code","execution_count":34,"id":"42c97937","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:12:20.113153Z","iopub.status.busy":"2024-06-17T21:12:20.112815Z","iopub.status.idle":"2024-06-17T21:12:20.393165Z","shell.execute_reply":"2024-06-17T21:12:20.392121Z"},"papermill":{"duration":0.302949,"end_time":"2024-06-17T21:12:20.395483","exception":false,"start_time":"2024-06-17T21:12:20.092534","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["<matplotlib.legend.Legend at 0x7b00a4f15d90>"]},"execution_count":34,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 1000x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(10,5))\n","plt.title(\"Training and Validation Loss\")\n","plt.plot(valid_losses_list,label=\"val\")\n","plt.plot(train_losses_list,label=\"train\")\n","plt.xlabel(\"iterations\")\n","plt.ylabel(\"Loss\")\n","plt.legend()"]},{"cell_type":"code","execution_count":35,"id":"e4562b15","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:12:20.43867Z","iopub.status.busy":"2024-06-17T21:12:20.438359Z","iopub.status.idle":"2024-06-17T21:12:20.72899Z","shell.execute_reply":"2024-06-17T21:12:20.72796Z"},"papermill":{"duration":0.314918,"end_time":"2024-06-17T21:12:20.731221","exception":false,"start_time":"2024-06-17T21:12:20.416303","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["<matplotlib.legend.Legend at 0x7b00a5d54050>"]},"execution_count":35,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<Figure size 1000x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(10,5))\n","plt.title(\"Training and Validation accuracy\")\n","plt.plot(valid_acc_list,label=\"val\")\n","plt.plot(train_acc_list,label=\"train\")\n","plt.xlabel(\"iterations\")\n","plt.ylabel(\"accuracy\")\n","plt.legend()"]},{"cell_type":"markdown","id":"ca15c34f","metadata":{"papermill":{"duration":0.020897,"end_time":"2024-06-17T21:12:20.77342","exception":false,"start_time":"2024-06-17T21:12:20.752523","status":"completed"},"tags":[]},"source":["Epoch : 029, Training: Loss: 0.3714, Accuracy: 86.1326%, nttValidation : Loss : 0.2845, Accuracy: 89.7683%, Time: 12.2244s\n","\n","We can see that the training accuracy and validation accuracy are quite high."]},{"cell_type":"code","execution_count":36,"id":"080488a2","metadata":{"execution":{"iopub.execute_input":"2024-06-17T21:12:20.817226Z","iopub.status.busy":"2024-06-17T21:12:20.816648Z","iopub.status.idle":"2024-06-17T21:12:22.611839Z","shell.execute_reply":"2024-06-17T21:12:22.610652Z"},"papermill":{"duration":1.819647,"end_time":"2024-06-17T21:12:22.6143","exception":false,"start_time":"2024-06-17T21:12:20.794653","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy of the network on test images: 92 %\n"]}],"source":["correct = 0\n","total = 0\n","test_loss = 0.0\n","# testing the trained model \n","resnet50.eval()\n","with torch.no_grad():\n","    for j, (inputs, labels) in enumerate(dataloaders['test']):\n","        inputs = inputs.to(device)\n","        labels = labels.to(device)\n","        # Forward pass - compute outputs on input data using the model\n","        outputs = resnet50(inputs)\n","\n","        # Compute the total loss for the batch and add it to valid_loss\n","        test_loss += loss.item() * inputs.size(0)\n","\n","        \n","        _, predictions = torch.max(outputs.data, 1)\n","        total += labels.size(0)\n","        correct += (predictions == labels).sum().item()\n","    \n","    \n","print(f'Accuracy of the network on test images: {100 * correct // total} %')\n","    "]}],"metadata":{"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"datasetId":47062,"sourceId":101404,"sourceType":"datasetVersion"}],"dockerImageVersionId":30445,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 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