From 66617140f0305487936b2f258547361b889026b7 Mon Sep 17 00:00:00 2001 From: Keshav Arora <119474193+CoderOMaster@users.noreply.github.com> Date: Tue, 23 Jan 2024 20:11:42 +0530 Subject: [PATCH 01/11] Add files via upload --- .../Dataset/LInk.txt | 3 + .../Images/Example.png | Bin 0 -> 300523 bytes .../Images/Plot.png | Bin 0 -> 19483 bytes .../Model/CNN.ipynb | 294 +++++++++++ .../Model/Misc_Models.ipynb | 483 ++++++++++++++++++ .../Model/UnderSampled.ipynb | 276 ++++++++++ .../requirements.txt | 6 + 7 files changed, 1062 insertions(+) create mode 100644 Cassava Leaf Disease Classification/Dataset/LInk.txt create mode 100644 Cassava Leaf Disease Classification/Images/Example.png create mode 100644 Cassava Leaf Disease Classification/Images/Plot.png create mode 100644 Cassava Leaf Disease Classification/Model/CNN.ipynb create mode 100644 Cassava Leaf Disease Classification/Model/Misc_Models.ipynb create mode 100644 Cassava Leaf Disease Classification/Model/UnderSampled.ipynb create 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z`{R$Bpmbi>4IH-{BbG`cmgwVobod!Cfbqff0@%c!qeh1WID78gvw#3#Y5bX=5SXUE zx3@2Q_ZNz3$J7G;BMGNo4oa`CNcwWu0k@A#6~A(2A7VidJa?d&3lmx`L_|VR8(PCI zBXjNAVYG0KJm(;Yp`ctZCF!oAgYWx0L^7!tM{fZ-R7NYXL8iDMCH4Eu+}vRF77k8M z!@2}U)L}jF92;6st*(%dLVIDh?=}RZ(Z`AuLzsj|Q^!{A`|qb5e(=RJ?BRbWEB}K( zQgJ}!k~+D}>5~k7vs=j(j~*aPjn7IhU^mHUe4a>ZeD>k-;-)ILkL>Z=Oq*dwprf3V LIh%0$^6mcw%a5no literal 0 HcmV?d00001 diff --git a/Cassava Leaf Disease Classification/Model/CNN.ipynb b/Cassava Leaf Disease Classification/Model/CNN.ipynb new file mode 100644 index 000000000..bddf2ab0b --- /dev/null +++ b/Cassava Leaf Disease Classification/Model/CNN.ipynb @@ -0,0 +1,294 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "TPU" + }, + "cells": [ + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "ZgKNAgwylDkM", + "outputId": "a33dd5ae-ad9a-4939-e18a-506f5215b336" + }, + "execution_count": 1, + "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" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import tensorflow as tf\n", + "import re\n", + "from tensorflow.keras.preprocessing.text import one_hot\n", + "import matplotlib.pyplot as py\n", + "from tensorflow.keras.models import Sequential,load_model\n", + "from sklearn.model_selection import train_test_split\n" + ], + "metadata": { + "id": "zq1juT-2nfgA" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "\n", + "main_directory = '/content/drive/MyDrive/data'\n", + "# Define image dimensions and batch size\n", + "img_height, img_width = 224, 224\n", + "batch_size = 32\n", + "\n", + "# Use ImageDataGenerator for data augmentation and normalization\n", + "datagen = ImageDataGenerator(\n", + " rescale=1./255,\n", + " shear_range=0.2,\n", + " zoom_range=0.2,\n", + " horizontal_flip=True,\n", + " validation_split=0.2 # Set the validation split\n", + ")\n", + "\n", + "# Create training data generator\n", + "train_generator = datagen.flow_from_directory(\n", + " main_directory,\n", + " target_size=(img_height, img_width),\n", + " batch_size=batch_size,\n", + " class_mode='categorical',\n", + " subset='training' # Specify 'training' for training data\n", + ")\n", + "\n", + "# Create validation data generator\n", + "validation_generator = datagen.flow_from_directory(\n", + " main_directory,\n", + " target_size=(img_height, img_width),\n", + " batch_size=batch_size,\n", + " class_mode='categorical',\n", + " subset='validation' # Specify 'validation' for validation data\n", + ")\n", + "\n", + "# Define your CNN model using TensorFlow's Keras API\n", + "model = tf.keras.models.Sequential([\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(img_height, img_width, 3)),\n", + " tf.keras.layers.MaxPooling2D(2, 2),\n", + " tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n", + " tf.keras.layers.MaxPooling2D(2, 2),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(128, activation='relu'),\n", + " tf.keras.layers.Dense(5, activation='softmax') # 4 classes for diseases\n", + "])\n", + "\n", + "# Compile the model\n", + "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Train the model\n", + "model.fit(\n", + " train_generator,\n", + " steps_per_epoch=train_generator.samples // batch_size,\n", + " validation_data=validation_generator,\n", + " validation_steps=validation_generator.samples // batch_size,\n", + " epochs=10 # Set the number of epochs\n", + ")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ohjGWycknjcT", + "outputId": "26181de4-4136-45e4-b697-0a5b423cf7d2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found 17120 images belonging to 5 classes.\n", + "Found 4277 images belonging to 5 classes.\n", + "Epoch 1/10\n", + "535/535 [==============================] - 445s 826ms/step - loss: 1.1809 - accuracy: 0.6193 - val_loss: 1.0219 - val_accuracy: 0.6262\n", + "Epoch 2/10\n", + "535/535 [==============================] - 481s 899ms/step - loss: 0.9930 - accuracy: 0.6361 - val_loss: 0.9264 - val_accuracy: 0.6527\n", + "Epoch 3/10\n", + "535/535 [==============================] - 425s 795ms/step - loss: 0.9094 - accuracy: 0.6527 - val_loss: 0.8733 - val_accuracy: 0.6624\n", + "Epoch 4/10\n", + "535/535 [==============================] - 423s 791ms/step - loss: 0.8665 - accuracy: 0.6751 - val_loss: 0.8820 - val_accuracy: 0.6703\n", + "Epoch 5/10\n", + "535/535 [==============================] - 419s 784ms/step - loss: 0.8286 - accuracy: 0.6883 - val_loss: 0.8381 - val_accuracy: 0.6805\n", + "Epoch 6/10\n", + "535/535 [==============================] - 477s 892ms/step - loss: 0.8065 - accuracy: 0.6976 - val_loss: 0.8142 - val_accuracy: 0.6823\n", + "Epoch 7/10\n", + "535/535 [==============================] - 408s 763ms/step - loss: 0.7731 - accuracy: 0.7116 - val_loss: 0.8000 - val_accuracy: 0.6969\n", + "Epoch 8/10\n", + "535/535 [==============================] - 411s 769ms/step - loss: 0.7556 - accuracy: 0.7154 - val_loss: 0.8063 - val_accuracy: 0.6969\n", + "Epoch 9/10\n", + "535/535 [==============================] - 403s 753ms/step - loss: 0.7326 - accuracy: 0.7245 - val_loss: 0.8010 - val_accuracy: 0.6924\n", + "Epoch 10/10\n", + "535/535 [==============================] - 406s 759ms/step - loss: 0.7127 - accuracy: 0.7324 - val_loss: 0.7961 - val_accuracy: 0.7023\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 17 + } + ] + }, + { + "cell_type": "code", + "source": [ + "model.save(\"/content/drive/MyDrive\")\n" + ], + "metadata": { + "id": "Tv3WJTGC9Dt1" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "evaluation_result = model.evaluate(validation_generator, steps=validation_generator.samples // batch_size)\n", + "print(\"Validation Loss:\", evaluation_result[0])\n", + "print(\"Validation Accuracy:\", evaluation_result[1])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tGE3OAL4_YWp", + "outputId": "66ee96e8-5664-49bf-b9b8-71ec6b9f62b9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "133/133 [==============================] - 84s 630ms/step - loss: 0.8085 - accuracy: 0.7002\n", + "Validation Loss: 0.8085159063339233\n", + "Validation Accuracy: 0.7001879811286926\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow.keras.applications import VGG16\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, Flatten, Dropout\n", + "\n", + "\n", + "# Load pre-trained VGG16 model\n", + "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(img_height, img_width, 3))\n", + "\n", + "# Freeze the layers of the pre-trained model\n", + "for layer in base_model.layers:\n", + " layer.trainable = False\n", + "\n", + "# Create a new model on top of the pre-trained model\n", + "model = Sequential()\n", + "model.add(base_model)\n", + "model.add(Flatten())\n", + "model.add(Dense(128, activation='relu'))\n", + "model.add(Dropout(0.5))\n", + "model.add(Dense(5, activation='softmax')) # 5 classes for your task\n", + "\n", + "# Compile the model\n", + "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Train the model\n", + "model.fit(\n", + " train_generator,\n", + " steps_per_epoch=train_generator.samples // batch_size,\n", + " validation_data=validation_generator,\n", + " validation_steps=validation_generator.samples // batch_size,\n", + " epochs=10 # Set the number of epochs\n", + ")\n", + "\n", + "# Evaluate the model on the validation set\n", + "evaluation_result = model.evaluate(validation_generator, steps=validation_generator.samples // batch_size)\n", + "print(\"Validation Loss:\", evaluation_result[0])\n", + "print(\"Validation Accuracy:\", evaluation_result[1])\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LUKlFJfZ-66C", + "outputId": "74a256ac-9a0a-411e-f58c-8158927c5d72" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "58889256/58889256 [==============================] - 2s 0us/step\n", + "Epoch 1/10\n", + "535/535 [==============================] - 442s 809ms/step - loss: 1.1297 - accuracy: 0.6065 - val_loss: 0.9465 - val_accuracy: 0.6147\n", + "Epoch 2/10\n", + "535/535 [==============================] - 488s 913ms/step - loss: 1.0326 - accuracy: 0.6147 - val_loss: 0.9351 - val_accuracy: 0.6163\n", + "Epoch 3/10\n", + "535/535 [==============================] - 434s 812ms/step - loss: 1.0177 - accuracy: 0.6149 - val_loss: 0.9210 - val_accuracy: 0.6161\n", + "Epoch 4/10\n", + "535/535 [==============================] - 432s 807ms/step - loss: 1.0118 - accuracy: 0.6149 - val_loss: 0.9234 - val_accuracy: 0.6147\n", + "Epoch 5/10\n", + "535/535 [==============================] - 480s 898ms/step - loss: 1.0028 - accuracy: 0.6149 - val_loss: 0.9272 - val_accuracy: 0.6151\n", + "Epoch 6/10\n", + "535/535 [==============================] - 415s 776ms/step - loss: 0.9988 - accuracy: 0.6149 - val_loss: 0.9163 - val_accuracy: 0.6158\n", + "Epoch 7/10\n", + "535/535 [==============================] - 479s 896ms/step - loss: 0.9945 - accuracy: 0.6149 - val_loss: 0.9155 - val_accuracy: 0.6147\n", + "Epoch 8/10\n", + "535/535 [==============================] - 420s 784ms/step - loss: 0.9990 - accuracy: 0.6149 - val_loss: 0.9119 - val_accuracy: 0.6163\n", + "Epoch 9/10\n", + "535/535 [==============================] - 472s 883ms/step - loss: 0.9939 - accuracy: 0.6149 - val_loss: 0.9141 - val_accuracy: 0.6156\n", + "Epoch 10/10\n", + "535/535 [==============================] - 415s 776ms/step - loss: 0.9950 - accuracy: 0.6147 - val_loss: 0.9141 - val_accuracy: 0.6140\n", + "133/133 [==============================] - 84s 629ms/step - loss: 0.9111 - accuracy: 0.6151\n", + "Validation Loss: 0.9110695123672485\n", + "Validation Accuracy: 0.6151315569877625\n" + ] + } + ] + } + ] +} \ No newline at end of file diff --git a/Cassava Leaf Disease Classification/Model/Misc_Models.ipynb b/Cassava Leaf Disease Classification/Model/Misc_Models.ipynb new file mode 100644 index 000000000..b927b25fc --- /dev/null +++ b/Cassava Leaf Disease Classification/Model/Misc_Models.ipynb @@ -0,0 +1,483 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SDF-Gkx4muGN", + "outputId": "7222c2d3-3d3f-40c0-fcc5-1923865d96d3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found 17120 images belonging to 5 classes.\n", + "Found 4277 images belonging to 5 classes.\n", + "Epoch 1/10\n", + "535/535 [==============================] - 4185s 8s/step - loss: 1.2015 - accuracy: 0.6150 - val_loss: 1.1057 - val_accuracy: 0.6194\n", + "Epoch 2/10\n", + "535/535 [==============================] - 394s 737ms/step - loss: 1.0425 - accuracy: 0.6276 - val_loss: 1.0089 - val_accuracy: 0.6358\n", + "Epoch 3/10\n", + "535/535 [==============================] - 398s 744ms/step - loss: 0.9996 - accuracy: 0.6363 - val_loss: 0.9880 - val_accuracy: 0.6335\n", + "Epoch 4/10\n", + "535/535 [==============================] - 417s 780ms/step - loss: 0.9680 - accuracy: 0.6430 - val_loss: 0.9447 - val_accuracy: 0.6433\n", + "Epoch 5/10\n", + "535/535 [==============================] - 403s 754ms/step - loss: 0.9379 - accuracy: 0.6496 - val_loss: 0.9299 - val_accuracy: 0.6438\n", + "Epoch 6/10\n", + "535/535 [==============================] - 397s 742ms/step - loss: 0.9138 - accuracy: 0.6576 - val_loss: 0.9144 - val_accuracy: 0.6478\n", + "Epoch 7/10\n", + "535/535 [==============================] - 404s 756ms/step - loss: 0.8905 - accuracy: 0.6614 - val_loss: 0.8914 - val_accuracy: 0.6574\n", + "Epoch 8/10\n", + "535/535 [==============================] - 405s 758ms/step - loss: 0.8711 - accuracy: 0.6692 - val_loss: 0.8799 - val_accuracy: 0.6609\n", + "Epoch 9/10\n", + "535/535 [==============================] - 426s 796ms/step - loss: 0.8639 - accuracy: 0.6714 - val_loss: 0.9107 - val_accuracy: 0.6506\n", + "Epoch 10/10\n", + "535/535 [==============================] - 429s 801ms/step - loss: 0.8523 - accuracy: 0.6759 - val_loss: 0.8589 - val_accuracy: 0.6673\n", + "133/133 [==============================] - 82s 617ms/step - loss: 0.8650 - accuracy: 0.6617\n", + "Validation Loss: 0.8649784326553345\n", + "Validation Accuracy: 0.6616541147232056\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "\n", + "main_directory = '/content/drive/MyDrive/data'\n", + "img_height, img_width = 224, 224\n", + "batch_size = 32\n", + "\n", + "# Use ImageDataGenerator for data augmentation and normalization\n", + "datagen = ImageDataGenerator(\n", + " rescale=1./255,\n", + " shear_range=0.2,\n", + " zoom_range=0.2,\n", + " horizontal_flip=True,\n", + " validation_split=0.2\n", + ")\n", + "\n", + "# Create training data generator with data augmentation\n", + "train_generator = datagen.flow_from_directory(\n", + " main_directory,\n", + " target_size=(img_height, img_width),\n", + " batch_size=batch_size,\n", + " class_mode='categorical',\n", + " subset='training'\n", + ")\n", + "\n", + "# Create validation data generator\n", + "validation_generator = datagen.flow_from_directory(\n", + " main_directory,\n", + " target_size=(img_height, img_width),\n", + " batch_size=batch_size,\n", + " class_mode='categorical',\n", + " subset='validation'\n", + ")\n", + "\n", + "# Data augmentation layers\n", + "data_augmentation = keras.Sequential(\n", + " [\n", + " layers.experimental.preprocessing.RandomFlip(\"horizontal\", input_shape=(img_height, img_width, 3)),\n", + " layers.experimental.preprocessing.RandomRotation(0.1),\n", + " layers.experimental.preprocessing.RandomZoom(0.1),\n", + " ]\n", + ")\n", + "\n", + "# Main model\n", + "model = tf.keras.models.Sequential([\n", + " data_augmentation,\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(img_height, img_width, 3)),\n", + " tf.keras.layers.MaxPooling2D(2, 2),\n", + " tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n", + " tf.keras.layers.MaxPooling2D(2, 2),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(128, activation='relu'),\n", + " tf.keras.layers.Dense(5, activation='softmax')\n", + "])\n", + "\n", + "# Compile the model\n", + "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Train the model\n", + "model.fit(\n", + " train_generator,\n", + " steps_per_epoch=train_generator.samples // batch_size,\n", + " validation_data=validation_generator,\n", + " validation_steps=validation_generator.samples // batch_size,\n", + " epochs=10\n", + ")\n", + "\n", + "# Evaluate the model on the validation set\n", + "evaluation_result = model.evaluate(validation_generator, steps=validation_generator.samples // batch_size)\n", + "print(\"Validation Loss:\", evaluation_result[0])\n", + "print(\"Validation Accuracy:\", evaluation_result[1])\n" + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Load an image from the test set (adjust the index as needed)\n", + "test_image, true_label = validation_generator[110]\n", + "\n", + "# Predict the class probabilities for the loaded image\n", + "predictions = model.predict(test_image)\n", + "\n", + "# Get the predicted class index\n", + "predicted_class_index = np.argmax(predictions[0])\n", + "\n", + "# Map class index to class label\n", + "class_labels = list(train_generator.class_indices.keys())\n", + "predicted_class_label = class_labels[predicted_class_index]\n", + "\n", + "# Display the image\n", + "plt.imshow(test_image[0])\n", + "plt.title(f'True Label: {class_labels[np.argmax(true_label)]}\\nPredicted Label: {predicted_class_label}')\n", + "plt.show()\n", + "\n", + "\n", + "#example of an incorrect result for demonstration purpose" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 490 + }, + "id": "pd8tviE6ZyFo", + "outputId": "bb4caa37-c3b6-4fa7-8060-c80100cfb592" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1/1 [==============================] - 0s 36ms/step\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "

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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "from tensorflow.keras import layers, models\n", + "from tensorflow.keras.optimizers import Adam\n", + "from tensorflow.keras.callbacks import ModelCheckpoint\n", + "from tensorflow.keras.applications import ResNet50\n", + "\n", + "# Define image dimensions and batch size\n", + "\n", + "\n", + "# Load the pre-trained ResNet50 model without the top layer\n", + "base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(img_height, img_width, 3))\n", + "\n", + "# Freeze the layers in the base model\n", + "for layer in base_model.layers:\n", + " layer.trainable = False\n", + "\n", + "# Add custom top layers for classification\n", + "x = base_model.output\n", + "x = layers.GlobalAveragePooling2D()(x)\n", + "x = layers.Dense(256, activation='relu')(x)\n", + "x = layers.Dropout(0.5)(x)\n", + "predictions = layers.Dense(train_generator.num_classes, activation='softmax')(x)\n", + "\n", + "# Create the final model\n", + "model = models.Model(inputs=base_model.input, outputs=predictions)\n", + "\n", + "# Compile the model\n", + "model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Define callbacks, e.g., ModelCheckpoint for saving the best model during training\n", + "checkpoint = ModelCheckpoint('resnet_model.h5', save_best_only=True)\n", + "\n", + "c" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7pK5lcQd8ihA", + "outputId": "68e34871-579d-49d3-ed16-86b86746331e" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/10\n", + "535/535 [==============================] - ETA: 0s - loss: 1.1992 - accuracy: 0.6110" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n", + " saving_api.save_model(\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r535/535 [==============================] - 502s 917ms/step - loss: 1.1992 - accuracy: 0.6110 - val_loss: 1.1432 - val_accuracy: 0.6151\n", + "Epoch 2/10\n", + "535/535 [==============================] - 421s 786ms/step - loss: 1.1711 - accuracy: 0.6149 - val_loss: 1.1287 - val_accuracy: 0.6156\n", + "Epoch 3/10\n", + "535/535 [==============================] - 415s 777ms/step - loss: 1.1568 - accuracy: 0.6149 - val_loss: 1.1260 - val_accuracy: 0.6168\n", + "Epoch 4/10\n", + "535/535 [==============================] - 413s 771ms/step - loss: 1.1478 - accuracy: 0.6149 - val_loss: 1.1147 - val_accuracy: 0.6147\n", + "Epoch 5/10\n", + "535/535 [==============================] - 416s 776ms/step - loss: 1.1384 - accuracy: 0.6149 - val_loss: 1.1185 - val_accuracy: 0.6151\n", + "Epoch 6/10\n", + "535/535 [==============================] - 415s 776ms/step - loss: 1.1333 - accuracy: 0.6149 - val_loss: 1.1082 - val_accuracy: 0.6151\n", + "Epoch 7/10\n", + "535/535 [==============================] - 409s 764ms/step - loss: 1.1330 - accuracy: 0.6149 - val_loss: 1.1103 - val_accuracy: 0.6156\n", + "Epoch 8/10\n", + "535/535 [==============================] - 414s 774ms/step - loss: 1.1307 - accuracy: 0.6149 - val_loss: 1.1088 - val_accuracy: 0.6151\n", + "Epoch 9/10\n", + "535/535 [==============================] - 412s 771ms/step - loss: 1.1268 - accuracy: 0.6149 - val_loss: 1.1054 - val_accuracy: 0.6154\n", + "Epoch 10/10\n", + "535/535 [==============================] - 414s 773ms/step - loss: 1.1244 - accuracy: 0.6149 - val_loss: 1.1003 - val_accuracy: 0.6161\n", + "133/133 [==============================] - 82s 612ms/step - loss: 1.0996 - accuracy: 0.6161\n", + "Validation Loss: 1.099609375\n", + "Validation Accuracy: 0.6160714030265808\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from tensorflow.keras.applications import InceptionV3\n", + "\n", + "# ... (previous code)\n", + "\n", + "# Load the pre-trained InceptionV3 model without the top layer\n", + "base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(img_height, img_width, 3))\n", + "\n", + "# Freeze the layers in the base model\n", + "for layer in base_model.layers:\n", + " layer.trainable = False\n", + "\n", + "# Add custom top layers for classification\n", + "x = base_model.output\n", + "x = layers.GlobalAveragePooling2D()(x)\n", + "x = layers.Dense(256, activation='relu')(x)\n", + "x = layers.Dropout(0.5)(x)\n", + "predictions = layers.Dense(train_generator.num_classes, activation='softmax')(x)\n", + "\n", + "# Create the final model\n", + "model = models.Model(inputs=base_model.input, outputs=predictions)\n", + "\n", + "# Compile the model\n", + "model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "\n", + "# Train the model\n", + "model.fit(\n", + " train_generator,\n", + " steps_per_epoch=train_generator.samples // batch_size,\n", + " validation_data=validation_generator,\n", + " validation_steps=validation_generator.samples // batch_size,\n", + " epochs=10\n", + ")\n", + "\n", + "# Evaluate the model on the validation set\n", + "evaluation_result = model.evaluate(validation_generator, steps=validation_generator.samples // batch_size)\n", + "print(\"Validation Loss:\", evaluation_result[0])\n", + "print(\"Validation Accuracy:\", evaluation_result[1])\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qbl_PHs3mIXb", + "outputId": "d70ec461-4ca1-46ca-a5be-5feb394bf936" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "87910968/87910968 [==============================] - 0s 0us/step\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/10\n", + "535/535 [==============================] - 418s 765ms/step - loss: 1.0110 - accuracy: 0.6403 - val_loss: 0.8850 - val_accuracy: 0.6715\n", + "Epoch 2/10\n", + "535/535 [==============================] - 411s 768ms/step - loss: 0.9202 - accuracy: 0.6621 - val_loss: 0.8550 - val_accuracy: 0.6793\n", + "Epoch 3/10\n", + "535/535 [==============================] - 410s 766ms/step - loss: 0.9098 - accuracy: 0.6672 - val_loss: 0.8414 - val_accuracy: 0.6915\n", + "Epoch 4/10\n", + "535/535 [==============================] - 410s 768ms/step - loss: 0.8883 - accuracy: 0.6711 - val_loss: 0.8341 - val_accuracy: 0.6884\n", + "Epoch 5/10\n", + "535/535 [==============================] - 409s 765ms/step - loss: 0.8770 - accuracy: 0.6740 - val_loss: 0.8373 - val_accuracy: 0.6910\n", + "Epoch 6/10\n", + "535/535 [==============================] - 405s 757ms/step - loss: 0.8698 - accuracy: 0.6776 - val_loss: 0.8266 - val_accuracy: 0.6936\n", + "Epoch 7/10\n", + "535/535 [==============================] - 407s 762ms/step - loss: 0.8680 - accuracy: 0.6783 - val_loss: 0.8309 - val_accuracy: 0.6891\n", + "Epoch 8/10\n", + "535/535 [==============================] - 408s 762ms/step - loss: 0.8508 - accuracy: 0.6832 - val_loss: 0.8357 - val_accuracy: 0.6927\n", + "Epoch 9/10\n", + "535/535 [==============================] - 410s 767ms/step - loss: 0.8539 - accuracy: 0.6816 - val_loss: 0.8165 - val_accuracy: 0.6974\n", + "Epoch 10/10\n", + "535/535 [==============================] - 420s 785ms/step - loss: 0.8521 - accuracy: 0.6826 - val_loss: 0.8267 - val_accuracy: 0.6927\n", + "133/133 [==============================] - 83s 623ms/step - loss: 0.8330 - accuracy: 0.6943\n", + "Validation Loss: 0.8329557776451111\n", + "Validation Accuracy: 0.6943138837814331\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n", + "\n", + "# ... (previous code)\n", + "\n", + "# Define the AlexNet model with hyperparameters\n", + "model = models.Sequential()\n", + "\n", + "# Layer 1\n", + "model.add(Conv2D(96, (11, 11), strides=(4, 4), activation='relu', input_shape=(img_height, img_width, 3)))\n", + "model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))\n", + "\n", + "# Layer 2\n", + "model.add(Conv2D(256, (5, 5), padding='same', activation='relu'))\n", + "model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))\n", + "\n", + "# Layer 3\n", + "model.add(Conv2D(384, (3, 3), padding='same', activation='relu'))\n", + "\n", + "# Layer 4\n", + "model.add(Conv2D(384, (3, 3), padding='same', activation='relu'))\n", + "\n", + "# Layer 5\n", + "model.add(Conv2D(256, (3, 3), padding='same', activation='relu'))\n", + "model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))\n", + "\n", + "# Flatten the output\n", + "model.add(Flatten())\n", + "\n", + "# Fully connected layers with dropout for regularization\n", + "model.add(Dense(4096, activation='relu'))\n", + "model.add(Dropout(0.5)) # Add dropout for regularization\n", + "model.add(Dense(4096, activation='relu'))\n", + "model.add(Dropout(0.5)) # Add dropout for regularization\n", + "model.add(Dense(train_generator.num_classes, activation='softmax'))\n", + "\n", + "# Compile the model with hyperparameters\n", + "model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# ... (remaining code)\n", + "# Train the model\n", + "model.fit(\n", + " train_generator,\n", + " steps_per_epoch=train_generator.samples // batch_size,\n", + " validation_data=validation_generator,\n", + " validation_steps=validation_generator.samples // batch_size,\n", + " epochs=10\n", + ")\n", + "\n", + "# Evaluate the model on the validation set\n", + "evaluation_result = model.evaluate(validation_generator, steps=validation_generator.samples // batch_size)\n", + "print(\"Validation Loss:\", evaluation_result[0])\n", + "print(\"Validation Accuracy:\", evaluation_result[1])\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0rOjlwu-wITw", + "outputId": "5f704735-7523-41ce-8710-cb7e26d14a63" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/10\n", + "535/535 [==============================] - 400s 739ms/step - loss: 1.2836 - accuracy: 0.6127 - val_loss: 1.1855 - val_accuracy: 0.6144\n", + "Epoch 2/10\n", + "535/535 [==============================] - 394s 736ms/step - loss: 1.1896 - accuracy: 0.6149 - val_loss: 1.1856 - val_accuracy: 0.6140\n", + "Epoch 3/10\n", + "535/535 [==============================] - 400s 748ms/step - loss: 1.1894 - accuracy: 0.6149 - val_loss: 1.1893 - val_accuracy: 0.6144\n", + "Epoch 4/10\n", + "535/535 [==============================] - 399s 745ms/step - loss: 1.1890 - accuracy: 0.6149 - val_loss: 1.1838 - val_accuracy: 0.6149\n", + "Epoch 5/10\n", + "535/535 [==============================] - 394s 736ms/step - loss: 1.1861 - accuracy: 0.6149 - val_loss: 1.1849 - val_accuracy: 0.6154\n", + "Epoch 6/10\n", + "130/535 [======>.......................] - ETA: 4:04 - loss: 1.1903 - accuracy: 0.6132" + ] + } + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/Cassava Leaf Disease Classification/Model/UnderSampled.ipynb b/Cassava Leaf Disease Classification/Model/UnderSampled.ipynb new file mode 100644 index 000000000..0bd7c94db --- /dev/null +++ b/Cassava Leaf Disease Classification/Model/UnderSampled.ipynb @@ -0,0 +1,276 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "zq1juT-2nfgA" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import tensorflow as tf\n", + "import re\n", + "from tensorflow.keras.preprocessing.text import one_hot\n", + "import matplotlib.pyplot as py\n", + "from tensorflow.keras.models import Sequential,load_model\n", + "from sklearn.model_selection import train_test_split\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "82Cxx6driP9i", + "outputId": "21e74b6b-52ea-4def-f218-26941b93de8d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "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", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 245 + }, + "id": "B9MaRashj49_", + "outputId": "f90d9b7e-1cbf-43cc-cbe3-4b6d028b3cd0" + }, + "outputs": [ + { + "output_type": "error", + "ename": "KeyboardInterrupt", + "evalue": "", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0mclass_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmain_directory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_path\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---> 29\u001b[0;31m \u001b[0mnum_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_path\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[0m\u001b[1;32m 30\u001b[0m \u001b[0msamples_per_class\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclass_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnum_samples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0mmin_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin_samples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_samples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "import os\n", + "import shutil\n", + "from tensorflow.keras.optimizers import Adam\n", + "\n", + "def keep_first_n_images(directory, n):\n", + " # Get the list of files in the directory\n", + " files = os.listdir(directory)\n", + "\n", + " # Sort the files to ensure consistent order\n", + " files.sort()\n", + "\n", + " # Keep the first n files and delete the rest\n", + " for file_name in files[n:]:\n", + " file_path = os.path.join(directory, file_name)\n", + " os.remove(file_path)\n", + "\n", + "directory_path = '/content/drive/MyDrive/data'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "lLcMHvFwzgPI", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4c655ca8-2001-4c3e-9a51-c70e07ef50c3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found 8993 images belonging to 5 classes.\n", + "Found 2246 images belonging to 5 classes.\n" + ] + } + ], + "source": [ + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "\n", + "main_directory = '/content/drive/MyDrive/data'\n", + "\n", + "# Define image dimensions and batch size\n", + "img_height, img_width = 224, 224\n", + "batch_size = 32\n", + "\n", + "# Use ImageDataGenerator for data augmentation and normalization\n", + "datagen = ImageDataGenerator(\n", + " rescale=1./255,\n", + " shear_range=0.2,\n", + " zoom_range=0.2,\n", + " horizontal_flip=True,\n", + " validation_split=0.2 # Set the validation split\n", + ")\n", + "\n", + "# Create training data generator\n", + "train_generator = datagen.flow_from_directory(\n", + " main_directory,\n", + " target_size=(img_height, img_width),\n", + " batch_size=batch_size,\n", + " class_mode='categorical',\n", + " subset='training' # Specify 'training' for training data\n", + ")\n", + "\n", + "# Create validation data generator\n", + "validation_generator = datagen.flow_from_directory(\n", + " main_directory,\n", + " target_size=(img_height, img_width),\n", + " batch_size=batch_size,\n", + " class_mode='categorical',\n", + " subset='validation' # Specify 'validation' for validation data\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 472 + }, + "id": "YMZSNc87jFEB", + "outputId": "04064409-32ab-4e5a-8f34-04c44088cb4c" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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5OyRJTqfTujzGZTIAAC5/ZRqIjDFKTEzUV199pUWLFik6OvqM70lJSZEkhYWFSZLi4uK0Zs0a7dmzx2ozf/58uVwu1a1b12qzcOFCt/nMnz9fcXFxpbQmAADgUlamgWjAgAH68MMPNWPGDAUEBCgzM1OZmZk6fPiwJCk9PV3PPvusVq5cqW3btumbb77R/fffr5YtW6p+/fqSpLZt26pu3brq0aOHVq1apblz52rEiBEaMGCAnE6nJOmhhx7Sli1b9MQTT2jDhg1644039Omnn2rw4MFltu4AAKD8KNPb7ovr9T116lT16tVLO3bs0H333ae1a9fq0KFDioiI0O23364RI0a4Xcbavn27+vfvr6SkJPn5+alnz556/vnnizyYcfDgwVq/fr2uvPJKPf3002f9YMacnBwFBgYqOzuby2cAAFwizuXvd7l6DlF5RSACAODScy5/v8tFp2oAAICyRCACAAC2RyACAAC2RyACAAC2RyACAAC2RyACAAC2RyACAAC2RyACAAC2V25+7R4AgMtN1L9ml3UJl4xtz3cs0+VzhggAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANieZ1kXAAC48KL+NbusS7hkbHu+Y1mXgDLAGSIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7ZRqIxo0bp+uuu04BAQGqVq2abrvtNm3cuNGtzZEjRzRgwABVrlxZ/v7+uvPOO5WVleXWJiMjQx07dpSvr6+qVaumoUOH6vjx425tkpKSdO2118rpdKpmzZqaNm3ahV49AABwiSjTQPTDDz9owIAB+umnnzR//nwdO3ZMbdu21aFDh6w2gwcP1v/+9z999tln+uGHH7Rr1y7dcccd1vT8/Hx17NhRR48e1bJly/Tee+9p2rRpeuaZZ6w2W7duVceOHdW6dWulpKRo0KBB6tOnj+bOnXtR1xcAAJRPDmOMKesiCv3xxx+qVq2afvjhB7Vs2VLZ2dmqWrWqZsyYobvuukuStGHDBsXExCg5OVnXX3+9vvvuO3Xq1Em7du1SSEiIJGnKlCkaNmyY/vjjD3l5eWnYsGGaPXu21q5day2ra9euOnDggObMmXPGunJychQYGKjs7Gy5XK4Ls/IAcAFx2/3ZK83b7tnuZ+9CPO7gXP5+l6s+RNnZ2ZKk4OBgSdLKlSt17NgxxcfHW22uvvpqXXXVVUpOTpYkJScnq169elYYkqSEhATl5ORo3bp1Vpu/z6OwTeE8TpaXl6ecnBy3FwAAuHyVm0BUUFCgQYMGqXnz5rrmmmskSZmZmfLy8lJQUJBb25CQEGVmZlpt/h6GCqcXTjtdm5ycHB0+fLhILePGjVNgYKD1ioiIKJV1BAAA5VO5CUQDBgzQ2rVr9fHHH5d1KRo+fLiys7Ot144dO8q6JAAAcAGVi5/uSExM1KxZs7R48WJdeeWV1vjQ0FAdPXpUBw4ccDtLlJWVpdDQUKvNzz//7Da/wrvQ/t7m5DvTsrKy5HK55OPjU6Qep9Mpp9NZKusGAADKvzI9Q2SMUWJior766istWrRI0dHRbtMbN26sihUrauHChda4jRs3KiMjQ3FxcZKkuLg4rVmzRnv27LHazJ8/Xy6XS3Xr1rXa/H0ehW0K5wEAAOytTM8QDRgwQDNmzNDXX3+tgIAAq89PYGCgfHx8FBgYqN69e2vIkCEKDg6Wy+XSwIEDFRcXp+uvv16S1LZtW9WtW1c9evTQiy++qMzMTI0YMUIDBgywzvI89NBDev311/XEE0/on//8pxYtWqRPP/1Us2fT+x8AAJTxGaL//Oc/ys7O1k033aSwsDDr9cknn1htJkyYoE6dOunOO+9Uy5YtFRoaqi+//NKa7uHhoVmzZsnDw0NxcXG67777dP/992vMmDFWm+joaM2ePVvz589XgwYN9Morr+i///2vEhISLur6AgCA8qlcPYeovOI5RAAudTwP5+zxHKKywXOIAAAAyhiBCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2F65+C0zAPbBc1nO3oV4LguAU+MMEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD0CEQAAsD3Psi4AKCtR/5pd1iVcMrY937GsSwCAC4ozRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPbKNBAtXrxYnTt3Vnh4uBwOh2bOnOk2vVevXnI4HG6vdu3aubXZt2+funfvLpfLpaCgIPXu3Vu5ublubVavXq0WLVrI29tbERERevHFFy/0qgEAgEtImQaiQ4cOqUGDBpo8eXKxbdq1a6fdu3dbr48++shtevfu3bVu3TrNnz9fs2bN0uLFi9WvXz9rek5Ojtq2bavIyEitXLlSL730kkaNGqW33nrrgq0XAAC4tHiW5cLbt2+v9u3bn7aN0+lUaGjoKaelpqZqzpw5WrFihZo0aSJJmjRpkjp06KCXX35Z4eHhmj59uo4ePap3331XXl5eio2NVUpKisaPH+8WnAAAgH2V+z5ESUlJqlatmurUqaP+/ftr79691rTk5GQFBQVZYUiS4uPjVaFCBS1fvtxq07JlS3l5eVltEhIStHHjRu3fv//irQgAACi3yvQM0Zm0a9dOd9xxh6Kjo5Wenq4nn3xS7du3V3Jysjw8PJSZmalq1aq5vcfT01PBwcHKzMyUJGVmZio6OtqtTUhIiDWtUqVKRZabl5envLw8azgnJ6e0Vw0AAJQj5ToQde3a1fp3vXr1VL9+fdWoUUNJSUlq06bNBVvuuHHjNHr06As2fwAAUL6U+0tmf1e9enVVqVJFaWlpkqTQ0FDt2bPHrc3x48e1b98+q99RaGiosrKy3NoUDhfXN2n48OHKzs62Xjt27CjtVQEAAOXIJRWIdu7cqb179yosLEySFBcXpwMHDmjlypVWm0WLFqmgoEDNmjWz2ixevFjHjh2z2syfP1916tQ55eUy6URHbpfL5fYCAACXrzINRLm5uUpJSVFKSookaevWrUpJSVFGRoZyc3M1dOhQ/fTTT9q2bZsWLlyoLl26qGbNmkpISJAkxcTEqF27durbt69+/vlnLV26VImJieratavCw8MlSd26dZOXl5d69+6tdevW6ZNPPtFrr72mIUOGlNVqAwCAcqZMA9Evv/yiRo0aqVGjRpKkIUOGqFGjRnrmmWfk4eGh1atX69Zbb1Xt2rXVu3dvNW7cWEuWLJHT6bTmMX36dF199dVq06aNOnTooBtvvNHtGUOBgYGaN2+etm7dqsaNG+uxxx7TM888wy33AADAUqadqm+66SYZY4qdPnfu3DPOIzg4WDNmzDhtm/r162vJkiXnXB8AALCHS6oPEQAAwIVAIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZXokBUvXp17d27t8j4AwcOqHr16uddFAAAwMVUokC0bds25efnFxmfl5en33///byLAgAAuJjO6dfuv/nmG+vfc+fOVWBgoDWcn5+vhQsXKioqqtSKAwAAuBjOKRDddtttkiSHw6GePXu6TatYsaKioqL0yiuvlFpxAAAAF8M5BaKCggJJUnR0tFasWKEqVapckKIAAAAupnMKRIW2bt1a2nUAAACUmRIFIklauHChFi5cqD179lhnjgq9++67510YAADAxVKiQDR69GiNGTNGTZo0UVhYmBwOR2nXBQAAcNGUKBBNmTJF06ZNU48ePUq7HgAAgIuuRM8hOnr0qG644YbSrgUAAKBMlCgQ9enTRzNmzCjtWgAAAMpEiS6ZHTlyRG+99ZYWLFig+vXrq2LFim7Tx48fXyrFAQAAXAwlCkSrV69Ww4YNJUlr1651m0YHawAAcKkpUSD6/vvvS7sOAACAMlOiPkQAAACXkxKdIWrduvVpL40tWrSoxAUBAABcbCUKRIX9hwodO3ZMKSkpWrt2bZEffQUAACjvShSIJkyYcMrxo0aNUm5u7nkVBAAAcLGVah+i++67j98xAwAAl5xSDUTJycny9vYuzVkCAABccCW6ZHbHHXe4DRtjtHv3bv3yyy96+umnS6UwAACAi6VEgSgwMNBtuEKFCqpTp47GjBmjtm3blkphAAAAF0uJAtHUqVNLuw4AAIAyU6JAVGjlypVKTU2VJMXGxqpRo0alUhQAAMDFVKJAtGfPHnXt2lVJSUkKCgqSJB04cECtW7fWxx9/rKpVq5ZmjQAAABdUie4yGzhwoA4ePKh169Zp37592rdvn9auXaucnBw98sgjpV0jAADABVWiM0Rz5szRggULFBMTY42rW7euJk+eTKdqAABwySnRGaKCggJVrFixyPiKFSuqoKDgvIsCAAC4mEoUiG6++WY9+uij2rVrlzXu999/1+DBg9WmTZtSKw4AAOBiKFEgev3115WTk6OoqCjVqFFDNWrUUHR0tHJycjRp0qTSrhEAAOCCKlEfooiICP36669asGCBNmzYIEmKiYlRfHx8qRYHAABwMZzTGaJFixapbt26ysnJkcPh0C233KKBAwdq4MCBuu666xQbG6slS5ZcqFoBAAAuiHMKRK+++qr69u0rl8tVZFpgYKAefPBBjR8/vtSKAwAAuBjOKRCtWrVK7dq1K3Z627ZttXLlyvMuCgAA4GI6p0CUlZV1ytvtC3l6euqPP/4476IAAAAupnMKRFdccYXWrl1b7PTVq1crLCzsvIsCAAC4mM4pEHXo0EFPP/20jhw5UmTa4cOHNXLkSHXq1KnUigMAALgYzum2+xEjRujLL79U7dq1lZiYqDp16kiSNmzYoMmTJys/P19PPfXUBSkUAADgQjmnQBQSEqJly5apf//+Gj58uIwxkiSHw6GEhARNnjxZISEhF6RQAACAC+WcH8wYGRmpb7/9Vvv371daWpqMMapVq5YqVap0IeoDAAC44Er0pGpJqlSpkq677rrSrAUAAKBMlOi3zAAAAC4nBCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7BCIAAGB7ZRqIFi9erM6dOys8PFwOh0MzZ850m26M0TPPPKOwsDD5+PgoPj5emzdvdmuzb98+de/eXS6XS0FBQerdu7dyc3Pd2qxevVotWrSQt7e3IiIi9OKLL17oVQMAAJeQMg1Ehw4dUoMGDTR58uRTTn/xxRc1ceJETZkyRcuXL5efn58SEhJ05MgRq0337t21bt06zZ8/X7NmzdLixYvVr18/a3pOTo7atm2ryMhIrVy5Ui+99JJGjRqlt95664KvHwAAuDSU+NfuS0P79u3Vvn37U04zxujVV1/ViBEj1KVLF0nS+++/r5CQEM2cOVNdu3ZVamqq5syZoxUrVqhJkyaSpEmTJqlDhw56+eWXFR4erunTp+vo0aN699135eXlpdjYWKWkpGj8+PFuwQkAANhXue1DtHXrVmVmZio+Pt4aFxgYqGbNmik5OVmSlJycrKCgICsMSVJ8fLwqVKig5cuXW21atmwpLy8vq01CQoI2btyo/fv3n3LZeXl5ysnJcXsBAIDLV7kNRJmZmZKkkJAQt/EhISHWtMzMTFWrVs1tuqenp4KDg93anGoef1/GycaNG6fAwEDrFRERcf4rBAAAyq1yG4jK0vDhw5WdnW29duzYUdYlAQCAC6jcBqLQ0FBJUlZWltv4rKwsa1poaKj27NnjNv348ePat2+fW5tTzePvyziZ0+mUy+VyewEAgMtXuQ1E0dHRCg0N1cKFC61xOTk5Wr58ueLi4iRJcXFxOnDggFauXGm1WbRokQoKCtSsWTOrzeLFi3Xs2DGrzfz581WnTh1VqlTpIq0NAAAoz8o0EOXm5iolJUUpKSmSTnSkTklJUUZGhhwOhwYNGqR///vf+uabb7RmzRrdf//9Cg8P12233SZJiomJUbt27dS3b1/9/PPPWrp0qRITE9W1a1eFh4dLkrp16yYvLy/17t1b69at0yeffKLXXntNQ4YMKaO1BgAA5U2Z3nb/yy+/qHXr1tZwYUjp2bOnpk2bpieeeEKHDh1Sv379dODAAd14442aM2eOvL29rfdMnz5diYmJatOmjSpUqKA777xTEydOtKYHBgZq3rx5GjBggBo3bqwqVaromWee4ZZ7AABgKdNAdNNNN8kYU+x0h8OhMWPGaMyYMcW2CQ4O1owZM067nPr162vJkiUlrhMAAFzeym0fIgAAgIuFQAQAAGyPQAQAAGyPQAQAAGyPQAQAAGyPQAQAAGyPQAQAAGyPQAQAAGyPQAQAAGyPQAQAAGyPQAQAAGyPQAQAAGyvTH/cFSdE/Wt2WZdwydj2fMeyLgEAcBniDBEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALA9AhEAALC9ch2IRo0aJYfD4fa6+uqrrelHjhzRgAEDVLlyZfn7++vOO+9UVlaW2zwyMjLUsWNH+fr6qlq1aho6dKiOHz9+sVcFAACUY55lXcCZxMbGasGCBdawp+f/lTx48GDNnj1bn332mQIDA5WYmKg77rhDS5culSTl5+erY8eOCg0N1bJly7R7927df//9qlixop577rmLvi4AAKB8KveByNPTU6GhoUXGZ2dn65133tGMGTN08803S5KmTp2qmJgY/fTTT7r++us1b948rV+/XgsWLFBISIgaNmyoZ599VsOGDdOoUaPk5eV1sVcHAACUQ+X6kpkkbd68WeHh4apevbq6d++ujIwMSdLKlSt17NgxxcfHW22vvvpqXXXVVUpOTpYkJScnq169egoJCbHaJCQkKCcnR+vWrSt2mXl5ecrJyXF7AQCAy1e5DkTNmjXTtGnTNGfOHP3nP//R1q1b1aJFCx08eFCZmZny8vJSUFCQ23tCQkKUmZkpScrMzHQLQ4XTC6cVZ9y4cQoMDLReERERpbtiAACgXCnXl8zat29v/bt+/fpq1qyZIiMj9emnn8rHx+eCLXf48OEaMmSINZyTk0MoAgDgMlauzxCdLCgoSLVr11ZaWppCQ0N19OhRHThwwK1NVlaW1ecoNDS0yF1nhcOn6pdUyOl0yuVyub0AAMDl65IKRLm5uUpPT1dYWJgaN26sihUrauHChdb0jRs3KiMjQ3FxcZKkuLg4rVmzRnv27LHazJ8/Xy6XS3Xr1r3o9QMAgPKpXF8ye/zxx9W5c2dFRkZq165dGjlypDw8PHTvvfcqMDBQvXv31pAhQxQcHCyXy6WBAwcqLi5O119/vSSpbdu2qlu3rnr06KEXX3xRmZmZGjFihAYMGCCn01nGawcAAMqLch2Idu7cqXvvvVd79+5V1apVdeONN+qnn35S1apVJUkTJkxQhQoVdOeddyovL08JCQl64403rPd7eHho1qxZ6t+/v+Li4uTn56eePXtqzJgxZbVKAACgHCrXgejjjz8+7XRvb29NnjxZkydPLrZNZGSkvv3229IuDQAAXEYuqT5EAAAAFwKBCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2J6tAtHkyZMVFRUlb29vNWvWTD///HNZlwQAAMoB2wSiTz75REOGDNHIkSP166+/qkGDBkpISNCePXvKujQAAFDGbBOIxo8fr759++qBBx5Q3bp1NWXKFPn6+urdd98t69IAAEAZs0UgOnr0qFauXKn4+HhrXIUKFRQfH6/k5OQyrAwAAJQHnmVdwMXw559/Kj8/XyEhIW7jQ0JCtGHDhiLt8/LylJeXZw1nZ2dLknJyci5IfQV5f12Q+V6OSvMzYLufPbZ72WC7lw22e9m4EH9jC+dpjDljW1sEonM1btw4jR49usj4iIiIMqgGfxf4allXYE9s97LBdi8bbPeycSG3+8GDBxUYGHjaNrYIRFWqVJGHh4eysrLcxmdlZSk0NLRI++HDh2vIkCHWcEFBgfbt26fKlSvL4XBc8HrLg5ycHEVERGjHjh1yuVxlXY4tsM3LBtu9bLDdy4bdtrsxRgcPHlR4ePgZ29oiEHl5ealx48ZauHChbrvtNkknQs7ChQuVmJhYpL3T6ZTT6XQbFxQUdBEqLX9cLpctvjTlCdu8bLDdywbbvWzYabuf6cxQIVsEIkkaMmSIevbsqSZNmqhp06Z69dVXdejQIT3wwANlXRoAAChjtglE99xzj/744w8988wzyszMVMOGDTVnzpwiHa0BAID92CYQSVJiYuIpL5GhKKfTqZEjRxa5dIgLh21eNtjuZYPtXjbY7sVzmLO5Fw0AAOAyZosHMwIAAJwOgQgAANgegQgAANgegaiU3HTTTRo0aFBZl3HWoqKi9Oqrr551+2nTpp31s5hGjRqlhg0bWsOn2ja9evWynglVXJszcTgcmjlz5jm953Jw8vY9Xyd/tqU9f5ybk78bZeViHNPO5jjE/ljUhTz2nby9y8v+eDHY6i6zS1mvXr104MCBUvsSrFixQn5+fqUyr9Lw5ZdfqmLFiqU6z6SkJLVu3Vr79++/oA/WLO3Ppqw9/vjjGjhwYFmXcVk51T6ybds2RUdH67fffuMP/v/ncDj01Vdf2eYPcEnt3r1blSpVuijLeu21187qd8AuBwQimzl69Ki8vLxUtWrVsi7FTXBwsPXvwhovN8eOHSv10Hch+Pv7y9/f/6Iu83L9zIEL4VQ/OXWhnO1Tni8HXDIrRcePH1diYqICAwNVpUoVPf3001ay/uCDD9SkSRMFBAQoNDRU3bp10549e9zev27dOnXq1Ekul0sBAQFq0aKF0tPTNWrUKL333nv6+uuv5XA45HA4lJSUJEnasWOH7r77bgUFBSk4OFhdunTRtm3brHkWnu4cO3aswsPDVadOHUlFT1WPHz9e9erVk5+fnyIiIvTwww8rNzf3vLbHm2++qYiICC1ZskSzZs1Sv379rG3z22+/WdsmKipK0dHRiomJkcvlUr9+/bR7925de+21qlChghwOh6pWrarg4GCr5tdff12S9Oeff+r222+X0+mUw+FQ//79JZ3433fr1q0lSZUqVZLD4VBwcLA++OADRUVFKTAwUF27dtXBgwfPal0+//xz1atXTz4+PqpcubLi4+N16NChYj+bbdu2yeFw6JNPPlGrVq3k7e2t6dOnS5L++9//KiYmRt7e3rr66qv1xhtvuC1r2LBhql27tnx9fVW9enU9/fTTOnbsWLG1paenq3r16kpMTDyr/8lNmzZNV111lXx9fXX77bdr7969btNPPmWelJSkpk2bys/PT0FBQWrevLm2b99uTf/666917bXXytvbW9WrV9fo0aO1f/9+de/eXX5+fnK5XAoLC5OHh4cCAgL08MMPKzIyUs8++6zuv/9+BQQEqEaNGqpUqZK8vb3l6+srp9OpiIgIPfLII/r555/Vvn17+fv7q1q1aqpbt67CwsLk5+enZs2aWd+FwnULCgrS3LlzFRMTI39/f7Vr1067d+8+43aR/u/78txzzykkJERBQUEaM2aMjh8/rqFDhyo4OFhXXnmlpk6d6va+NWvW6Oabb7b2j379+lnfn+L2kejoaElSo0aN5HA4dNNNN52ypoKCAo0bN07R0dHy8fFRgwYN9Pnnn5/V+pyvgoICPfHEEwoODlZoaKhGjRplTTtw4ID69OmjqlWryuVy6eabb9aqVaus6enp6erSpYtCQkLk7++v6667TgsWLCh2WVFRUZKk22+/XQ6HwxouVNx39/3331flypWVl5fn1v62225Tjx49zm8D6MSlw4EDB2rQoEGqVKmSQkJC9Pbbb1u/dBAQEKCaNWvqu+++s97zww8/qGnTpnI6nQoLC9O//vUvHT9+3Jpe3PFEOnH2/pZbblGVKlUUGBioVq1a6ddff3Wr6eRLZjt37tS9996r4OBg+fn5qUmTJlq+fPlZrd/zzz+vkJAQBQQEqHfv3jpy5Ijb9JMvmZ2udun8j2+rVq1S69atFRAQIJfLpcaNG+uXX36xpv/4449q0aKFfHx8rGPE35d/XgxKRatWrYy/v7959NFHzYYNG8yHH35ofH19zVtvvWWMMeadd94x3377rUlPTzfJyckmLi7OtG/f3nr/zp07TXBwsLnjjjvMihUrzMaNG827775rNmzYYA4ePGjuvvtu065dO7N7926ze/duk5eXZ44ePWpiYmLMP//5T7N69Wqzfv16061bN1OnTh2Tl5dnjDGmZ8+ext/f3/To0cOsXbvWrF271hhjTGRkpJkwYYK1/AkTJphFixaZrVu3moULF5o6deqY/v37W9OnTp1qAgMDz2pbjBw50vj5+Zmbb77Z/Pbbb6Zhw4bG4XCYOnXqWNvG09PTNGjQwKrFw8PDtGjRwqSlpZm0tDTTtGlTI8n069fPfPnll6ZOnTpGkrn33nuNMcasXr3aSDJhYWFmxowZplevXsbb29t4enqavXv3msOHDxun02kkmY0bN5rHHnvM+Pv7mzvuuMOsWbPGLF682ISGhponn3zyjOuza9cu4+npacaPH2+2bt1qVq9ebSZPnmwOHjxY7GezdetWI8lERUWZL774wmzZssXs2rXLfPjhhyYsLMwa98UXX5jg4GAzbdo0a3nPPvusWbp0qdm6dav55ptvTEhIiHnhhRfctm/htlu1apUJDQ01Tz311Fl9Nj/99JOpUKGCeeGFF8zGjRvNa6+9ZoKCgtw+27/P/9ixYyYwMNA8/vjjJi0tzaxfv95MmzbNbN++3RhjzOLFi43L5TLTpk0z6enpZt68eSYqKspce+21JjIy0ixYsMA88cQT5sYbb7S2f506dUxAQIBxuVzm5ZdfNjfddJNp3ry5mT17tvH19TV9+vQx06dPN0uXLjX169c3TqfTDB8+3KSmpprbb7/dBAYGmkaNGpm0tDTz0ksvGafTaTZt2mSMObGfVqxY0cTHx5sVK1aYlStXmpiYGNOtW7ez2j49e/Y0AQEBZsCAAWbDhg3mnXfeMZJMQkKCGTt2rNm0aZN59tlnTcWKFc2OHTuMMcbk5uaasLAwa99auHChiY6ONj179jTGmGL3kZ9//tlIMgsWLDC7d+82e/futWro0qWLVdO///1vc/XVV5s5c+aY9PR0M3XqVON0Ok1SUtJZrVNJtWrVyrhcLjNq1CizadMm89577xmHw2HmzZtnjDEmPj7edO7c2axYscJs2rTJPPbYY6Zy5crWeqSkpJgpU6aYNWvWmE2bNpkRI0YYb29va98xxv04tGfPHiPJTJ061ezevdvs2bPHGHNifzzdd/evv/4ygYGB5tNPP7Xmm5WVZTw9Pc2iRYtKZTsEBASYZ5991vr8PTw8TPv27c1bb71lNm3aZPr3728qV65sDh06ZHbu3Gl8fX3Nww8/bFJTU81XX31lqlSpYkaOHGmMOf3xxBhjFi5caD744AOTmppq1q9fb3r37m1CQkJMTk6OVZMk89VXXxljTuxf1atXNy1atDBLliwxmzdvNp988olZtmzZGdftk08+MU6n0/z3v/81GzZsME899ZQJCAiwvv/GuO+PZ6q9NI5vsbGx5r777jOpqalm06ZN5tNPPzUpKSnGGGPS0tKMn5+fmTBhgtm0aZNZunSpadSokenVq9c5f66nQiAqJa1atTIxMTGmoKDAGjds2DATExNzyvYrVqwwkqwdafjw4SY6OtocPXr0lO1PPkgaY8wHH3xg6tSp47bMvLw84+PjY+bOnWu9LyQkxApIhU4ORCf77LPPTOXKla3hcw1EHh4eZufOncaYE9smIiLCOBwOs3v3bmOMMddcc43x9/e3aqlcubJ59NFHjTHGpKamGkmmWbNm1jw3b95sJJmQkBBjjDEFBQVGkrnzzjuNMcY0bNjQjB492kgy3333nfnxxx+Np6enkWT2799vRo4caXx9fd0OKkOHDnVbRnFWrlxpJJlt27adcvqpPpvCQPTqq6+6ja9Ro4aZMWOG27hnn33WxMXFFbv8l156yTRu3NgaLgwsS5cuNZUqVTIvv/zyGdeh0L333ms6dOjgNu6ee+4pNhDt3bvXSCr2j2+bNm3Mc8895zburbfeMpLMZ599Zo07cOCA8fX1NY8++qj57LPPTIUKFcxtt91mjDGmXr16ZtSoUaZ3796mX79+bvPq06ePkWQOHz5stm/fbjw8PKzvzsaNG60ahg8fbow5sZ9KMmlpadY8Jk+ebO03Z9KzZ08TGRlp8vPzrXF16tQxLVq0sIaPHz9u/Pz8zEcffWStb6VKlUxubq7VZvbs2aZChQomMzPTmm9x+8hvv/1WpIbCtkeOHDG+vr5F/rj17t3b+s/BhdKqVStz4403uo277rrrzLBhw8ySJUuMy+UyR44ccZteo0YN8+abbxY7z9jYWDNp0iRr+OTj0N//0Bc6m+9u//793f6D+corr5jq1au7HRtL6uTtUPj59+jRwxq3e/duI8kkJyebJ598sshxefLkycbf39/k5+ef8Xhysvz8fBMQEGD+97//WeP+vp3efPNNExAQYAXRcxEXF2cefvhht3HNmjUrNhCdqfbSOL4FBAS4Bai/O9UxYsmSJaZChQrm8OHDxS7jbNGHqBRdf/31cjgc1nBcXJxeeeUV5efnKyUlRaNGjdKqVau0f/9+FRQUSJIyMjJUt25dpaSkqEWLFufUx2TVqlVKS0tTQECA2/gjR44oPT3dGq5Xr94Z+2csWLBA48aN04YNG5STk6Pjx4/ryJEj+uuvv+Tr63vWNRW66qqrdMUVV1jDLVu21PTp07Vx40aFhoaqatWqWrdunfLz8yXJbR02btwoSWrXrp01rmbNmvLz89Off/6p/Px8eXh4SJJyc3N14MABrV+/Xj/88INGjhyplJQUFRQUqE6dOlq3bp01j6ioKLflhIWFFblseSoNGjRQmzZtVK9ePSUkJKht27a66667zqpTY5MmTax/Hzp0SOnp6erdu7f69u1rjT9+/LjbdfpPPvlEEydOVHp6unJzc3X8+PEiv0qdkZGhW265RWPHjj2nO4FSU1N1++23u42Li4vTnDlzTtk+ODhYvXr1UkJCgm655RbFx8fr7rvvVlhYmKQT++DSpUs1duxY6z2Fp7/r1asn6f/2rWPHjumNN97Qm2++qYKCAjVo0ECS9Mgjj6h///5yOp06cuSIPvjgA1WocOJqfuHp++DgYBUUFCg/P1/XXXedpBOfi4eHh/Ly8lS5cmVr+b6+vqpRo4Y1fLafc6HY2Fhr+ZIUEhKia665xhr28PBQ5cqVrXmmpqaqQYMGbjcpNG/eXAUFBdq4ceN5/V5iWlqa/vrrL91yyy1u448ePapGjRqVeL5nq379+m7Dhdty1apVys3NddvuknT48GHr2JObm6tRo0Zp9uzZ2r17t44fP67Dhw8rIyPjnOs403e3b9++uu666/T777/riiuu0LRp09SrVy+34/H5+Pt2KPz8C/dvSdZnvGfPHqWmpiouLs5t2c2bN1dubq527tx5xuNJVlaWRowYoaSkJO3Zs0f5+fn666+/it1uKSkpatSokVs/zLOVmpqqhx56yG1cXFycvv/++1O2P13tpXV8GzJkiPr06aMPPvhA8fHx+sc//mF9n1etWqXVq1db3Q8kyRijgoICbd26VTExMee8Df6OPkQXwZEjR5SQkCCXy6Xp06drxYoV+uqrrySdOLBJko+PzznPNzc3V40bN1ZKSorba9OmTerWrZvV7kx3k23btk2dOnVS/fr19cUXX2jlypWaPHmyW30XWmHAOVfr16/XkiVL1KhRI7lcLnl6eio1NVU//PCD9Qe30Mlh0+FwWMH0TLXNnz9f3333nerWratJkyapTp062rp16xnf+/dtX9in5O2333b7vNauXauffvpJkpScnKzu3burQ4cOmjVrln777Tc99dRTRT6HqlWrqmnTpvroo4+Uk5NzxjrOx9SpU5WcnKwbbrhBn3zyiWrXrm3Vm5ubq9GjR7utT2H/FqfT6bZvRUdH695777X2rcLfUurTp4+2bNmigIAARUZG6tixYxoyZIhSUlJ0ww03qG3btvrll1/0wgsvyMPDQ3PnztWCBQu0fPlypaSkKDU1Va+99ppV76k+Z3MOd8mc6v0l3XfOV+E+M3v2bLdtvH79+ovSj6i49c7NzVVYWFiRY8/GjRs1dOhQSSfuVvzqq6/03HPPacmSJUpJSVG9evVKdEw50/Zv1KiRGjRooPfff18rV67UunXr1KtXr3Nf4XNY/t/HFYaf0jie9OzZUykpKXrttde0bNkypaSkqHLlysVut5L87Sip09VeWse3UaNGad26derYsaMWLVqkunXrWn8vc3Nz9eCDD7rNf9WqVdq8ebPbf4JKikBUik7uxPbTTz+pVq1a2rBhg/bu3avnn39eLVq00NVXX13kf6z169fXkiVLiu086+XlZZ1NKXTttddq8+bNqlatmmrWrOn2Opc7A1auXKmCggK98soruv7661W7dm3t2rXrrN9/KhkZGW7zWLJkiSpUqGB16v7jjz/k5+d3yiBU2ObvZy3S0tJ06NAhVa1a1e09O3fu1GeffWZ1SPX09NT69eu1dOlS6+zMydutJBwOh5o3b67Ro0frt99+k5eXl/UlPdVncyohISEKDw/Xli1binxehR1sly1bpsjISD311FNq0qSJatWq5daBuZCPj49mzZolb29vJSQknHXn8JiYmFPup2fSqFEjDR8+XMuWLdM111yjGTNmSDqxD27cuNFtXW666SZVrFhRK1eutPatZ555Rr///rsqVap0yn0rIiJCN998s6Kjo/X4449r5syZqlmzplq0aKHt27erdu3aat++vfLz8+Xj46M2bdqofv361jIv5l03J4uJidGqVavcOnYuXbrUbX8/1T5SeNb2dPtO3bp15XQ6lZGRUWSfiYiIuABrc3auvfZaZWZmytPTs0hdVapUkXRiG/Tq1Uu333676tWrp9DQULcbPk6lYsWKJf6+9unTR9OmTdPUqVMVHx9fZtsnJiZGycnJbiF86dKlCggI0JVXXinp9MeTpUuX6pFHHlGHDh0UGxsrp9OpP//8s9jl1a9fXykpKdq3b1+Jaj3X40FxtZfm8a127doaPHiw5s2bpzvuuMO6ieHaa6/V+vXri8y/Zs2apXKXKoGoFGVkZGjIkCHauHGjPvroI02aNEmPPvqorrrqKnl5eWnSpEnasmWLvvnmGz377LNu701MTFROTo66du2qX375RZs3b9YHH3xgXT6KiorS6tWrtXHjRv355586duyYunfvripVqqhLly5asmSJtm7dqqSkJD3yyCPauXPnWddds2ZNHTt2zKrvgw8+0JQpU85rW3h7e6tnz55atWqVsrOztWPHDtWqVUvZ2dn66KOPtGHDhmIT/dVXX62mTZtq+fLlevDBBzVz5kx17txZkqw7xwr5+flpxowZboHo119/VV5enjp16iSHw6FZs2bp0KFDJf4f/fLly/Xcc8/pl19+UUZGhr788kv98ccf1unZU302xRk9erTGjRuniRMnatOmTVqzZo2mTp2q8ePHS5Jq1aqljIwMffzxx0pPT9fEiROtA+XJ/Pz8NHv2bHl6eqp9+/ZndVfgI488ojlz5ujll1/W5s2b9frrrxd7uUyStm7dquHDhys5OVnbt2/XvHnztHnzZmvdn3nmGb3//vsaPXq01q1bp9TUVM2ePVuxsbEaOnSo9u/fr2PHjql169ZyOBzasGFDkX1r0KBBmjt3rrp3764ff/xRU6dOVWhoqDZv3qyaNWtq+/btuvfee5Wdna1bb71V//jHP3TzzTcrLS1NP//8s8aNG6fZs2efcd0vlO7du1v7+9q1a/X9999r4MCB6tGjh3Up5VT7SLVq1eTj46M5c+YoKytL2dnZReYdEBCgxx9/XIMHD9Z7772n9PR0/frrr5o0aZLee++9i72qlvj4eMXFxem2227TvHnztG3bNi1btkxPPfWUdUdQrVq19OWXX1r/i+/WrdsZv4NRUVFauHChMjMztX///nOqqVu3btq5c6fefvtt/fOf/yzxup2vhx9+WDt27NDAgQO1YcMGff311xo5cqSGDBmiChUqnPF4UqtWLX3wwQdKTU3V8uXL1b1799OeBbr33nsVGhqq2267TUuXLtWWLVv0xRdfKDk5+Yy1Pvroo3r33Xc1depUbdq0SSNHjnTrZnCyM9V+vse3w4cPKzExUUlJSdq+fbuWLl2qFStWWPMfNmyYli1bpsTERKWkpGjz5s36+uuvlZiYeOYP5mycdy8kGGNOdLx7+OGHzUMPPWRcLpepVKmSefLJJ62OdTNmzDBRUVHG6XSauLg488033xTpULlq1SrTtm1b4+vrawICAkyLFi1Menq6MebEHRi33HKL8ff3N5LM999/b4w50Znv/vvvN1WqVDFOp9NUr17d9O3b12RnZxtjTt2Z05iinRnHjx9vwsLCjI+Pj0lISDDvv/++1SHZmHPvVN2gQQPzxhtvmPDwcONwOEyNGjXMAw88YG2bevXqmVtvvdWqpUaNGlanamNO3M3QqFEj43A4jCRTpUoV4+/vb6ZMmWK1kWSaNm1qPD09rc7pLpfL+Pn5meuvv94YY8yYMWNMaGiokWQqVarkVueECRNMZGTkGddn/fr1JiEhwVStWtU4nU5Tu3Ztt46hp/psiuswa4wx06dPNw0bNjReXl6mUqVKpmXLlubLL7+0pg8dOtRUrlzZ+Pv7m3vuucdMmDCh2E7Pxpy4y+SGG24wLVu2dOvYW5x33nnHXHnllcbHx8d07tzZvPzyy8XOPzMz09x2220mLCzMeHl5mcjISPPMM8+4dTqeM2eOueGGG4yPj49xuVymadOmZuLEiaZbt27WvhwQEGAcDoeJjo629q3CztiJiYmmRo0axul0mqCgIBMWFmb8/PyMn5+fqV+/vhk8eLC5/fbbTVBQkPH29jaVK1c2LpfLVKxY0YSFhZnbb7/drF692hhz6v30q6++Mmd7qDvV96VVq1Zu+6YxRb8/q1evNq1btzbe3t4mODjY9O3b19onjSn++/v222+biIgIU6FCBdOqVatT1lBQUGBeffVVU6dOHVOxYkVTtWpVk5CQYH744YezWqeSOtV6d+nSxbp7LicnxwwcONCEh4ebihUrmoiICNO9e3eTkZFhjDnRabx169bGx8fHREREmNdff73IPE/ejt98842pWbOm8fT0tL6bJ+/vxhT/3e3Ro4cJDg4u0tn7fJzN52+Me0fnpKQkc9111xkvLy8TGhpqhg0bZo4dO2aMOfPx5NdffzVNmjQx3t7eplatWuazzz47Y+fzbdu2mTvvvNO4XC7j6+trmjRpYpYvX35W6zd27Fjr+NqzZ0/zxBNPFNup+ky1G3N+x7e8vDzTtWtXExERYby8vEx4eLhJTEx06zD9888/W9+lwmPE2LFjz2pdz8RhjE0eQYlL2s6dOxUREaEFCxaoTZs2ZV0OztGhQ4d0xRVX6JVXXlHv3r3Luhxcptq0aaPY2FhNnDixrEvBJYi7zFAuLVq0SLm5uapXr552796tJ554QlFRUWrZsmVZl4az8Ntvv2nDhg1q2rSpsrOzNWbMGElSly5dyrgyXI7279+vpKQkJSUlFXkQIHC26EOEcxYbG2v9vMPJr7/fDnk+jh07pieffFKxsbG6/fbbVbVqVSUlJV2Qn77IyMgodn38/f1LdJtwWSl8ovOpXs8999xFreXll19WgwYNrCfZLlmyxOpwW1ZO9zkvWbKkTGtDyTVq1Ei9evXSCy+8YHVkx8U5Vl9OuGSGc7Z9+/ZiOw4XPgL+UnL8+PHT3v0SFRUlT89L42Tq77//rsOHD59yWnBwcImeVXI5SUtLK3baFVdccVFvYQYutMvtWH2hEYgAAIDtcckMAADYHoEIAADYHoEIAADYHoEIgC04HA7NnDmzrMsAUE4RiABcFjIzMzVw4EBVr15dTqdTERER6ty5sxYuXFjWpQG4BFwa9xIDwGls27ZNzZs3V1BQkF566SXVq1dPx44d09y5czVgwABt2LChrEsEUM5xhgjAJe/hhx+Ww+HQzz//rDvvvFO1a9dWbGyshgwZUuyvdw8bNky1a9eWr6+vqlevrqefftrtmS2rVq1S69atFRAQIJfLpcaNG1s/XLp9+3Z17txZlSpVkp+fn2JjY/Xtt99elHUFcGFwhgjAJW3fvn2aM2eOxo4dKz8/vyLTg4KCTvm+gIAATZs2TeHh4VqzZo369u2rgIAAPfHEE5JO/Ip9o0aN9J///EceHh5KSUmxnpQ+YMAAHT16VIsXL5afn5/Wr18vf3//C7aOAC48AhGAS1paWpqMMbr66qvP6X0jRoyw/h0VFaXHH39cH3/8sRWIMjIyNHToUGu+tWrVstpnZGTozjvvVL169SRJ1atXP9/VAFDGuGQG4JJW0oftf/LJJ2revLlCQ0Pl7++vESNGuP1u3ZAhQ9SnTx/Fx8fr+eefV3p6ujXtkUce0b///W81b95cI0eO1OrVq897PQCULQIRgEtarVq15HA4zqnjdHJysrp3764OHTpo1qxZ+u233/TUU0/p6NGjVptRo0Zp3bp16tixoxYtWqS6devqq6++kiT16dNHW7ZsUY8ePbRmzRo1adJEkyZNKvV1A3Dx8FtmAC557du315o1a7Rx48Yi/YgOHDigoKAgORwOffXVV7rtttv0yiuv6I033nA769OnTx99/vnnOnDgwCmXce+99+rQoUP65ptvikwbPny4Zs+ezZki4BLGGSIAl7zJkycrPz9fTZs21RdffKHNmzcrNTVVEydOVFxcXJH2tWrVUkZGhj7++GOlp6dr4sSJ1tkfSTp8+LASExOVlJSk7du3a+nSpVqxYoViYmIkSYMGDdLcuXO1detW/frrr/r++++taQAuTXSqBnDJq169un799VeNHTtWjz32mHbv3q2qVauqcePG+s9//lOk/a233qrBgwcrMTFReXl56tixo55++mmNGjVKkuTh4aG9e/fq/vvvV1ZWlqpUqaI77rhDo0ePliTl5+drwIAB2rlzp1wul9q1a6cJEyZczFUGUMq4ZAYAAGyPS2YAAMD2CEQAAMD2CEQAAMD2CEQAAMD2CEQAAMD2CEQAAMD2CEQAAMD2CEQAAMD2CEQAAMD2CEQAAMD2CEQAAMD2CEQAAMD2/h+ihzfjzg/58gAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Get the class indices from the generator\n", + "class_indices = train_generator.class_indices\n", + "\n", + "# Invert the dictionary to map class indices to class labels\n", + "class_labels = {v: k for k, v in class_indices.items()}\n", + "\n", + "# Get the class counts from the generator\n", + "class_counts = train_generator.classes\n", + "\n", + "# Count occurrences of each class\n", + "unique_classes, counts = np.unique(class_counts, return_counts=True)\n", + "\n", + "# Map class indices to class labels for plotting\n", + "class_labels_for_plot = [class_labels[idx] for idx in unique_classes]\n", + "\n", + "# Plot the bar graph\n", + "plt.bar(class_labels_for_plot, counts)\n", + "plt.xlabel('Class')\n", + "plt.ylabel('Count')\n", + "plt.title('Class Distribution')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ohjGWycknjcT", + "outputId": "01f45b1b-1712-40b7-939d-2b35941c5399" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/10\n", + "281/281 [==============================] - 3045s 11s/step - loss: 1.8414 - accuracy: 0.2541 - val_loss: 1.5499 - val_accuracy: 0.2580\n", + "Epoch 2/10\n", + "281/281 [==============================] - 1115s 4s/step - loss: 1.5407 - accuracy: 0.2870 - val_loss: 1.4978 - val_accuracy: 0.3246\n", + "Epoch 3/10\n", + "281/281 [==============================] - 1101s 4s/step - loss: 1.4640 - accuracy: 0.3304 - val_loss: 1.4412 - val_accuracy: 0.3442\n", + "Epoch 4/10\n", + "281/281 [==============================] - 1107s 4s/step - loss: 1.4200 - accuracy: 0.3594 - val_loss: 1.4187 - val_accuracy: 0.3634\n", + "Epoch 5/10\n", + "281/281 [==============================] - 1058s 4s/step - loss: 1.3864 - accuracy: 0.3804 - val_loss: 1.4003 - val_accuracy: 0.3701\n", + "Epoch 6/10\n", + " 91/281 [========>.....................] - ETA: 10:28 - loss: 1.3781 - accuracy: 0.3846" + ] + } + ], + "source": [ + "\n", + "\n", + "\n", + "# Define your CNN model using TensorFlow's Keras API\n", + "model = tf.keras.models.Sequential([\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(img_height, img_width, 3)),\n", + " tf.keras.layers.MaxPooling2D(2, 2),\n", + " tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n", + " tf.keras.layers.MaxPooling2D(2, 2),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(128, activation='relu'),\n", + " tf.keras.layers.Dense(5, activation='softmax') # 4 classes for diseases\n", + "])\n", + "\n", + "# Compile the model\n", + "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Train the model\n", + "model.fit(\n", + " train_generator,\n", + " steps_per_epoch=train_generator.samples // batch_size,\n", + " validation_data=validation_generator,\n", + " validation_steps=validation_generator.samples // batch_size,\n", + " epochs=10 # Set the number of epochs\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "MtGrk9dTpcZP" + } + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/Cassava Leaf Disease Classification/requirements.txt b/Cassava Leaf Disease Classification/requirements.txt new file mode 100644 index 000000000..1ac097dcb --- /dev/null +++ b/Cassava Leaf Disease Classification/requirements.txt @@ -0,0 +1,6 @@ +pandas==1.3.3 +matplotlib==3.4.3 +numpy==1.21.2 +tensorflow==2.6.0 +torch==1.9.1 +transformers==4.10.3 From d2327fe2ed030c45614c4ce2f4bf6a0b5613b447 Mon Sep 17 00:00:00 2001 From: Keshav Arora <119474193+CoderOMaster@users.noreply.github.com> Date: Tue, 23 Jan 2024 20:56:24 +0530 Subject: [PATCH 02/11] Create README.md --- Cassava Leaf Disease Classification/README.md | 52 +++++++++++++++++++ 1 file changed, 52 insertions(+) create mode 100644 Cassava Leaf Disease Classification/README.md diff --git a/Cassava Leaf Disease Classification/README.md b/Cassava Leaf Disease Classification/README.md new file mode 100644 index 000000000..07c318ba9 --- /dev/null +++ b/Cassava Leaf Disease Classification/README.md @@ -0,0 +1,52 @@ +# CASSAVA LEAF DISEASE CLASSIFICATION + +## GOAL +Developing various computer vision models to classify leaf diseases. + +## DATASET +https://www.kaggle.com/datasets/nirmalsankalana/cassava-leaf-disease-classification + +## MODELS USED +- CNN +- VGG16 +- Inception +- ResNet50 +- AlexNet + +## LIBRARIES +- Pandas +- Numpy +- TensorFlow +- OS,Shutil +- Matplotlib +- Scikit-Learn + +## IMPLEMENTATION +1. Load dataset (10,000 entries, 3 columns). +2. Clean text data (handle null values, spaces, capitalization). +3. Use 4 sentiment classes: Positive, Neutral, Negative, Very Negative. +4. Implement tokenization for sequence conversion. +5. Train models with various algorithms. + +## Models and Accuracies + +| Model | Accuracy | +| ----------------- |:----------:| +| Roberta | 0.79 | +| LSTM | 0.72 | +| Neural Network | 0.67 | +| Logistic Regression| 0.71 | + +**VISUALISATION** + +![Alt Text](./Images/Plot.png) + +![Alt Text](./Images/Example.png) + +**CONCLUSION** + +Inception Model has best accuracy out of all models + +**NAME** + +Keshav Arora From 32f1ad2dc539fc127eb9cf173385595b36c662b2 Mon Sep 17 00:00:00 2001 From: Keshav Arora <119474193+CoderOMaster@users.noreply.github.com> Date: Tue, 23 Jan 2024 21:46:49 +0530 Subject: [PATCH 03/11] Update README.md --- Cassava Leaf Disease Classification/README.md | 28 +++++++++++-------- 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/Cassava Leaf Disease Classification/README.md b/Cassava Leaf Disease Classification/README.md index 07c318ba9..34fca1a20 100644 --- a/Cassava Leaf Disease Classification/README.md +++ b/Cassava Leaf Disease Classification/README.md @@ -22,20 +22,24 @@ https://www.kaggle.com/datasets/nirmalsankalana/cassava-leaf-disease-classificat - Scikit-Learn ## IMPLEMENTATION -1. Load dataset (10,000 entries, 3 columns). -2. Clean text data (handle null values, spaces, capitalization). -3. Use 4 sentiment classes: Positive, Neutral, Negative, Very Negative. -4. Implement tokenization for sequence conversion. -5. Train models with various algorithms. +1. Load dataset (21,000 entries and 5 columns) +2. Implemented Deep learning models. +3. Applied data augmentation and undersampling of dataset separately for comparison. +4. Alexnet and undersampled models werent trained fully due to computational and time constraints but their expected accuracy is mapped logically. + ## Models and Accuracies -| Model | Accuracy | -| ----------------- |:----------:| -| Roberta | 0.79 | -| LSTM | 0.72 | -| Neural Network | 0.67 | -| Logistic Regression| 0.71 | +| Model | Accuracy | Validation Loss | +| ----------------- |:----------:|:---------------:| +| CNN | 0.70 | 0.808 | +| VGG16 | 0.61 | 0.911 | +| CNN(Data Aug) | 0.66 | 0.865 | +| ResNet | 0.61 | 1.099 | +| InceptionV3 | 0.69 | 0.833 | +| AlexNet | ~0.62 | ~1.000 | +| CNN(Undersampled) | ~0.5 | ~1.00 | + **VISUALISATION** @@ -45,7 +49,7 @@ https://www.kaggle.com/datasets/nirmalsankalana/cassava-leaf-disease-classificat **CONCLUSION** -Inception Model has best accuracy out of all models +CNN Model is the best out of all models(accuracy and validation loss).In this database undersampling and data augmentation were found to not have any significant impact. **NAME** From 235b38b15ab6fac302ed1dacf565b26be5d00385 Mon Sep 17 00:00:00 2001 From: Keshav Arora <119474193+CoderOMaster@users.noreply.github.com> Date: Tue, 23 Jan 2024 21:48:21 +0530 Subject: [PATCH 04/11] Update README.md --- Cassava Leaf Disease Classification/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/Cassava Leaf Disease Classification/README.md b/Cassava Leaf Disease Classification/README.md index 34fca1a20..97cfcf0cc 100644 --- a/Cassava Leaf Disease Classification/README.md +++ b/Cassava Leaf Disease Classification/README.md @@ -43,8 +43,11 @@ https://www.kaggle.com/datasets/nirmalsankalana/cassava-leaf-disease-classificat **VISUALISATION** + ![Alt Text](./Images/Plot.png) +After Effective Undersampling from 17k to 3k ish in last class + ![Alt Text](./Images/Example.png) **CONCLUSION** From 508428be825fb4e6fd8b69efaf0047c136ceee2a Mon Sep 17 00:00:00 2001 From: Keshav Arora <119474193+CoderOMaster@users.noreply.github.com> Date: Tue, 23 Jan 2024 21:49:32 +0530 Subject: [PATCH 05/11] Update README.md --- Cassava Leaf Disease Classification/README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/Cassava Leaf Disease Classification/README.md b/Cassava Leaf Disease Classification/README.md index 97cfcf0cc..2dd58260c 100644 --- a/Cassava Leaf Disease Classification/README.md +++ b/Cassava Leaf Disease Classification/README.md @@ -46,10 +46,12 @@ https://www.kaggle.com/datasets/nirmalsankalana/cassava-leaf-disease-classificat ![Alt Text](./Images/Plot.png) -After Effective Undersampling from 17k to 3k ish in last class +After Effective Undersampling from 17k to 2k ish in last class of mosaic_disease. ![Alt Text](./Images/Example.png) +Example of Mismatch for demonstration purpose. + **CONCLUSION** CNN Model is the best out of all models(accuracy and validation loss).In this database undersampling and data augmentation were found to not have any significant impact. From 7bed80d51d7803a1a7366cd3a4bc2d8e690ad7e7 Mon Sep 17 00:00:00 2001 From: Keshav Arora <119474193+CoderOMaster@users.noreply.github.com> Date: Wed, 24 Jan 2024 13:07:51 +0530 Subject: [PATCH 06/11] Rename LInk.txt to README.md --- .../Dataset/{LInk.txt => README.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename Cassava Leaf Disease Classification/Dataset/{LInk.txt => README.md} (100%) diff --git a/Cassava Leaf Disease Classification/Dataset/LInk.txt b/Cassava Leaf Disease Classification/Dataset/README.md similarity index 100% rename from Cassava Leaf Disease Classification/Dataset/LInk.txt rename to Cassava Leaf Disease Classification/Dataset/README.md From be5addb3752042b8201feb7113fc8bdf2ca7e23c Mon Sep 17 00:00:00 2001 From: Keshav Arora <119474193+CoderOMaster@users.noreply.github.com> Date: Wed, 24 Jan 2024 13:21:42 +0530 Subject: [PATCH 07/11] Add files via upload --- .../Images/1.png | Bin 0 -> 24300 bytes .../Images/2.png | Bin 0 -> 39937 bytes 2 files changed, 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zU2xa}&H8`;amSu0s1&gy(w0^k{*B+?(~OY|W8T&{{v=faQPKogB7Wz`V(AltI}k*A zv1b9D``f-KTiSH+}YVV0L)lc->rX~AEAu|mP@5kagT_3Q2GEZPOk|D(5vLw zCJ2m-jm3+xa&&ED>P+HvE}LOnfDHi+r-w&*%X7tYewS5-?V&HfBts#~Dt4g|8v;Z& z&aSTBk&%(vG63oXpoZ3b2|=Q^J81rTT+RU0^eTK~wJ&k_V46)4lyJ`I`BeUl4UC^E zt*EXZW>Z_a0LcZ3#o`=`h$4XYusE4aCRlj#86O0qD1Y4l+N;dv8IXk40m4fCbl7S` zY?ycl3tD8SL6>QYL9hxC09afGrKCWp_Xq%auDYQWe|q4rHYYm0nMj}G_sWKbhWuL@ zO;=aiwWEWfZ+qo^gDMCOXsS5(@4Nd3(y{&&tn&@S(4# zXS$qv1~&kDYP1d62(P5%RC02%oY>6U-MzoG|0*FqUVsHt<3KhskBYxh69#pU#)I`Wno2K4#Xfwl8(1T%MkiueGpR@p?UVUqqTe%mM zrGgorP$0F^4`_66h9pKJm4b{dtQ1JpXRsN`t?TS`zO8BO2;Q|euKkGv1YDDVj1K`_ z4i~U_d^(g`+=9n6CXlT#kj-I>5PC<;tAJ=C2mGW!e917U}t*s9En7#sHz%j zSFx)Ema+eQ{)UzV38nl8$lL#QCV_7%!nPj{|5F0`|7-T=CsgvuQnTM4`$WLt5S)YC Ksp@aeU;7swBntxo literal 0 HcmV?d00001 From ac1f5fb82265cabbf8c9ec24876bf3b3738013dd Mon Sep 17 00:00:00 2001 From: Keshav Arora <119474193+CoderOMaster@users.noreply.github.com> Date: Wed, 24 Jan 2024 13:22:35 +0530 Subject: [PATCH 08/11] Delete Cassava Leaf Disease Classification/Model/CNN.ipynb --- .../Model/CNN.ipynb | 294 ------------------ 1 file changed, 294 deletions(-) delete mode 100644 Cassava Leaf Disease Classification/Model/CNN.ipynb diff --git a/Cassava Leaf Disease Classification/Model/CNN.ipynb b/Cassava Leaf Disease Classification/Model/CNN.ipynb deleted file mode 100644 index bddf2ab0b..000000000 --- a/Cassava Leaf Disease Classification/Model/CNN.ipynb +++ /dev/null @@ -1,294 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "TPU" - }, - "cells": [ - { - "cell_type": "code", - "source": [ - "from google.colab import drive\n", - "drive.mount('/content/drive')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "ZgKNAgwylDkM", - "outputId": "a33dd5ae-ad9a-4939-e18a-506f5215b336" - }, - "execution_count": 1, - "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" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import tensorflow as tf\n", - "import re\n", - "from tensorflow.keras.preprocessing.text import one_hot\n", - "import matplotlib.pyplot as py\n", - "from tensorflow.keras.models import Sequential,load_model\n", - "from sklearn.model_selection import train_test_split\n" - ], - "metadata": { - "id": "zq1juT-2nfgA" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "\n", - "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", - "\n", - "main_directory = '/content/drive/MyDrive/data'\n", - "# Define image dimensions and batch size\n", - "img_height, img_width = 224, 224\n", - "batch_size = 32\n", - "\n", - "# Use ImageDataGenerator for data augmentation and normalization\n", - "datagen = ImageDataGenerator(\n", - " rescale=1./255,\n", - " shear_range=0.2,\n", - " zoom_range=0.2,\n", - " horizontal_flip=True,\n", - " validation_split=0.2 # Set the validation split\n", - ")\n", - "\n", - "# Create training data generator\n", - "train_generator = datagen.flow_from_directory(\n", - " main_directory,\n", - " target_size=(img_height, img_width),\n", - " batch_size=batch_size,\n", - " class_mode='categorical',\n", - " subset='training' # Specify 'training' for training data\n", - ")\n", - "\n", - "# Create validation data generator\n", - "validation_generator = datagen.flow_from_directory(\n", - " main_directory,\n", - " target_size=(img_height, img_width),\n", - " batch_size=batch_size,\n", - " class_mode='categorical',\n", - " subset='validation' # Specify 'validation' for validation data\n", - ")\n", - "\n", - "# Define your CNN model using TensorFlow's Keras API\n", - "model = tf.keras.models.Sequential([\n", - " tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(img_height, img_width, 3)),\n", - " tf.keras.layers.MaxPooling2D(2, 2),\n", - " tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n", - " tf.keras.layers.MaxPooling2D(2, 2),\n", - " tf.keras.layers.Flatten(),\n", - " tf.keras.layers.Dense(128, activation='relu'),\n", - " tf.keras.layers.Dense(5, activation='softmax') # 4 classes for diseases\n", - "])\n", - "\n", - "# Compile the model\n", - "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - "# Train the model\n", - "model.fit(\n", - " train_generator,\n", - " steps_per_epoch=train_generator.samples // batch_size,\n", - " validation_data=validation_generator,\n", - " validation_steps=validation_generator.samples // batch_size,\n", - " epochs=10 # Set the number of epochs\n", - ")\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ohjGWycknjcT", - "outputId": "26181de4-4136-45e4-b697-0a5b423cf7d2" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Found 17120 images belonging to 5 classes.\n", - "Found 4277 images belonging to 5 classes.\n", - "Epoch 1/10\n", - "535/535 [==============================] - 445s 826ms/step - loss: 1.1809 - accuracy: 0.6193 - val_loss: 1.0219 - val_accuracy: 0.6262\n", - "Epoch 2/10\n", - "535/535 [==============================] - 481s 899ms/step - loss: 0.9930 - accuracy: 0.6361 - val_loss: 0.9264 - val_accuracy: 0.6527\n", - "Epoch 3/10\n", - "535/535 [==============================] - 425s 795ms/step - loss: 0.9094 - accuracy: 0.6527 - val_loss: 0.8733 - val_accuracy: 0.6624\n", - "Epoch 4/10\n", - "535/535 [==============================] - 423s 791ms/step - loss: 0.8665 - accuracy: 0.6751 - val_loss: 0.8820 - val_accuracy: 0.6703\n", - "Epoch 5/10\n", - "535/535 [==============================] - 419s 784ms/step - loss: 0.8286 - accuracy: 0.6883 - val_loss: 0.8381 - val_accuracy: 0.6805\n", - "Epoch 6/10\n", - "535/535 [==============================] - 477s 892ms/step - loss: 0.8065 - accuracy: 0.6976 - val_loss: 0.8142 - val_accuracy: 0.6823\n", - "Epoch 7/10\n", - "535/535 [==============================] - 408s 763ms/step - loss: 0.7731 - accuracy: 0.7116 - val_loss: 0.8000 - val_accuracy: 0.6969\n", - "Epoch 8/10\n", - "535/535 [==============================] - 411s 769ms/step - loss: 0.7556 - accuracy: 0.7154 - val_loss: 0.8063 - val_accuracy: 0.6969\n", - "Epoch 9/10\n", - "535/535 [==============================] - 403s 753ms/step - loss: 0.7326 - accuracy: 0.7245 - val_loss: 0.8010 - val_accuracy: 0.6924\n", - "Epoch 10/10\n", - "535/535 [==============================] - 406s 759ms/step - loss: 0.7127 - accuracy: 0.7324 - val_loss: 0.7961 - val_accuracy: 0.7023\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 17 - } - ] - }, - { - "cell_type": "code", - "source": [ - "model.save(\"/content/drive/MyDrive\")\n" - ], - "metadata": { - "id": "Tv3WJTGC9Dt1" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "evaluation_result = model.evaluate(validation_generator, steps=validation_generator.samples // batch_size)\n", - "print(\"Validation Loss:\", evaluation_result[0])\n", - "print(\"Validation Accuracy:\", evaluation_result[1])" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "tGE3OAL4_YWp", - "outputId": "66ee96e8-5664-49bf-b9b8-71ec6b9f62b9" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "133/133 [==============================] - 84s 630ms/step - loss: 0.8085 - accuracy: 0.7002\n", - "Validation Loss: 0.8085159063339233\n", - "Validation Accuracy: 0.7001879811286926\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import tensorflow as tf\n", - "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", - "from tensorflow.keras.applications import VGG16\n", - "from tensorflow.keras.models import Sequential\n", - "from tensorflow.keras.layers import Dense, Flatten, Dropout\n", - "\n", - "\n", - "# Load pre-trained VGG16 model\n", - "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(img_height, img_width, 3))\n", - "\n", - "# Freeze the layers of the pre-trained model\n", - "for layer in base_model.layers:\n", - " layer.trainable = False\n", - "\n", - "# Create a new model on top of the pre-trained model\n", - "model = Sequential()\n", - "model.add(base_model)\n", - "model.add(Flatten())\n", - "model.add(Dense(128, activation='relu'))\n", - "model.add(Dropout(0.5))\n", - "model.add(Dense(5, activation='softmax')) # 5 classes for your task\n", - "\n", - "# Compile the model\n", - "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", - "\n", - "# Train the model\n", - "model.fit(\n", - " train_generator,\n", - " steps_per_epoch=train_generator.samples // batch_size,\n", - " validation_data=validation_generator,\n", - " validation_steps=validation_generator.samples // batch_size,\n", - " epochs=10 # Set the number of epochs\n", - ")\n", - "\n", - "# Evaluate the model on the validation set\n", - "evaluation_result = model.evaluate(validation_generator, steps=validation_generator.samples // batch_size)\n", - "print(\"Validation Loss:\", evaluation_result[0])\n", - "print(\"Validation Accuracy:\", evaluation_result[1])\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "LUKlFJfZ-66C", - "outputId": "74a256ac-9a0a-411e-f58c-8158927c5d72" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n", - "58889256/58889256 [==============================] - 2s 0us/step\n", - "Epoch 1/10\n", - "535/535 [==============================] - 442s 809ms/step - loss: 1.1297 - accuracy: 0.6065 - val_loss: 0.9465 - val_accuracy: 0.6147\n", - "Epoch 2/10\n", - "535/535 [==============================] - 488s 913ms/step - loss: 1.0326 - accuracy: 0.6147 - val_loss: 0.9351 - val_accuracy: 0.6163\n", - "Epoch 3/10\n", - "535/535 [==============================] - 434s 812ms/step - loss: 1.0177 - accuracy: 0.6149 - val_loss: 0.9210 - val_accuracy: 0.6161\n", - "Epoch 4/10\n", - "535/535 [==============================] - 432s 807ms/step - loss: 1.0118 - accuracy: 0.6149 - val_loss: 0.9234 - val_accuracy: 0.6147\n", - "Epoch 5/10\n", - "535/535 [==============================] - 480s 898ms/step - loss: 1.0028 - accuracy: 0.6149 - val_loss: 0.9272 - val_accuracy: 0.6151\n", - "Epoch 6/10\n", - "535/535 [==============================] - 415s 776ms/step - loss: 0.9988 - accuracy: 0.6149 - val_loss: 0.9163 - val_accuracy: 0.6158\n", - "Epoch 7/10\n", - "535/535 [==============================] - 479s 896ms/step - loss: 0.9945 - accuracy: 0.6149 - val_loss: 0.9155 - val_accuracy: 0.6147\n", - "Epoch 8/10\n", - "535/535 [==============================] - 420s 784ms/step - loss: 0.9990 - accuracy: 0.6149 - val_loss: 0.9119 - val_accuracy: 0.6163\n", - "Epoch 9/10\n", - "535/535 [==============================] - 472s 883ms/step - loss: 0.9939 - accuracy: 0.6149 - val_loss: 0.9141 - val_accuracy: 0.6156\n", - "Epoch 10/10\n", - "535/535 [==============================] - 415s 776ms/step - loss: 0.9950 - accuracy: 0.6147 - val_loss: 0.9141 - val_accuracy: 0.6140\n", - "133/133 [==============================] - 84s 629ms/step - loss: 0.9111 - accuracy: 0.6151\n", - "Validation Loss: 0.9110695123672485\n", - "Validation Accuracy: 0.6151315569877625\n" - ] - } - ] - } - ] -} \ No newline at end of file From 8f2e9236cdf7b692c82895108e2b441ba9c618e3 Mon Sep 17 00:00:00 2001 From: Keshav Arora <119474193+CoderOMaster@users.noreply.github.com> Date: Wed, 24 Jan 2024 13:22:54 +0530 Subject: [PATCH 09/11] Add files via upload --- .../Model/CNN (1).ipynb | 368 ++++++++++++++++++ 1 file changed, 368 insertions(+) create mode 100644 Cassava Leaf Disease Classification/Model/CNN (1).ipynb diff --git a/Cassava Leaf Disease Classification/Model/CNN (1).ipynb b/Cassava Leaf Disease Classification/Model/CNN (1).ipynb new file mode 100644 index 000000000..bfc08457f --- /dev/null +++ b/Cassava Leaf Disease Classification/Model/CNN (1).ipynb @@ -0,0 +1,368 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZgKNAgwylDkM", + "outputId": "2015834e-09b5-424b-dc48-449e18376b9c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ], + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "zq1juT-2nfgA" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import tensorflow as tf\n", + "import re\n", + "from tensorflow.keras.preprocessing.text import one_hot\n", + "import matplotlib.pyplot as py\n", + "from tensorflow.keras.models import Sequential,load_model\n", + "from sklearn.model_selection import train_test_split\n" + ] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from PIL import Image\n", + "\n", + "# Assuming your dataset directory structure is like: dataset/class1/, dataset/class2/, ...\n", + "dataset_directory = '/content/drive/MyDrive/extracted/data'\n", + "class_names = ['bacterial_blight', 'brown_streak', 'green_mottle', 'healthy', 'mosaic']\n", + "\n", + "# Function to count the number of files in a directory\n", + "def count_files(directory):\n", + " return len([filename for filename in os.listdir(directory) if os.path.isfile(os.path.join(directory, filename))])\n", + "\n", + "# Count the number of images in each class\n", + "class_sizes = [count_files(os.path.join(dataset_directory, class_name)) for class_name in class_names]\n", + "\n", + "# Bar Chart\n", + "plt.figure(figsize=(8, 8))\n", + "plt.bar(class_names, class_sizes, color='skyblue')\n", + "plt.title('Bar Chart for 5 Classes')\n", + "plt.xlabel('Class')\n", + "plt.ylabel('Number of Images')\n", + "plt.show()\n", + "\n", + "# Pie Chart\n", + "plt.figure(figsize=(8, 8))\n", + "plt.pie(class_sizes, labels=class_names, autopct='%1.1f%%', startangle=140)\n", + "plt.title('Pie Chart for 5 Classes')\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "pLR11mQJbKha", + "outputId": "86f648d2-a611-4b69-9bf6-88dc1ee4ab09" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ohjGWycknjcT", + "outputId": "26181de4-4136-45e4-b697-0a5b423cf7d2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 17120 images belonging to 5 classes.\n", + "Found 4277 images belonging to 5 classes.\n", + "Epoch 1/10\n", + "535/535 [==============================] - 445s 826ms/step - loss: 1.1809 - accuracy: 0.6193 - val_loss: 1.0219 - val_accuracy: 0.6262\n", + "Epoch 2/10\n", + "535/535 [==============================] - 481s 899ms/step - loss: 0.9930 - accuracy: 0.6361 - val_loss: 0.9264 - val_accuracy: 0.6527\n", + "Epoch 3/10\n", + "535/535 [==============================] - 425s 795ms/step - loss: 0.9094 - accuracy: 0.6527 - val_loss: 0.8733 - val_accuracy: 0.6624\n", + "Epoch 4/10\n", + "535/535 [==============================] - 423s 791ms/step - loss: 0.8665 - accuracy: 0.6751 - val_loss: 0.8820 - val_accuracy: 0.6703\n", + "Epoch 5/10\n", + "535/535 [==============================] - 419s 784ms/step - loss: 0.8286 - accuracy: 0.6883 - val_loss: 0.8381 - val_accuracy: 0.6805\n", + "Epoch 6/10\n", + "535/535 [==============================] - 477s 892ms/step - loss: 0.8065 - accuracy: 0.6976 - val_loss: 0.8142 - val_accuracy: 0.6823\n", + "Epoch 7/10\n", + "535/535 [==============================] - 408s 763ms/step - loss: 0.7731 - accuracy: 0.7116 - val_loss: 0.8000 - val_accuracy: 0.6969\n", + "Epoch 8/10\n", + "535/535 [==============================] - 411s 769ms/step - loss: 0.7556 - accuracy: 0.7154 - val_loss: 0.8063 - val_accuracy: 0.6969\n", + "Epoch 9/10\n", + "535/535 [==============================] - 403s 753ms/step - loss: 0.7326 - accuracy: 0.7245 - val_loss: 0.8010 - val_accuracy: 0.6924\n", + "Epoch 10/10\n", + "535/535 [==============================] - 406s 759ms/step - loss: 0.7127 - accuracy: 0.7324 - val_loss: 0.7961 - val_accuracy: 0.7023\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "\n", + "main_directory = '/content/drive/MyDrive/data'\n", + "# Define image dimensions and batch size\n", + "img_height, img_width = 224, 224\n", + "batch_size = 32\n", + "\n", + "# Use ImageDataGenerator for data augmentation and normalization\n", + "datagen = ImageDataGenerator(\n", + " rescale=1./255,\n", + " shear_range=0.2,\n", + " zoom_range=0.2,\n", + " horizontal_flip=True,\n", + " validation_split=0.2 # Set the validation split\n", + ")\n", + "\n", + "# Create training data generator\n", + "train_generator = datagen.flow_from_directory(\n", + " main_directory,\n", + " target_size=(img_height, img_width),\n", + " batch_size=batch_size,\n", + " class_mode='categorical',\n", + " subset='training' # Specify 'training' for training data\n", + ")\n", + "\n", + "# Create validation data generator\n", + "validation_generator = datagen.flow_from_directory(\n", + " main_directory,\n", + " target_size=(img_height, img_width),\n", + " batch_size=batch_size,\n", + " class_mode='categorical',\n", + " subset='validation' # Specify 'validation' for validation data\n", + ")\n", + "\n", + "# Define your CNN model using TensorFlow's Keras API\n", + "model = tf.keras.models.Sequential([\n", + " tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(img_height, img_width, 3)),\n", + " tf.keras.layers.MaxPooling2D(2, 2),\n", + " tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n", + " tf.keras.layers.MaxPooling2D(2, 2),\n", + " tf.keras.layers.Flatten(),\n", + " tf.keras.layers.Dense(128, activation='relu'),\n", + " tf.keras.layers.Dense(5, activation='softmax') # 4 classes for diseases\n", + "])\n", + "\n", + "# Compile the model\n", + "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Train the model\n", + "model.fit(\n", + " train_generator,\n", + " steps_per_epoch=train_generator.samples // batch_size,\n", + " validation_data=validation_generator,\n", + " validation_steps=validation_generator.samples // batch_size,\n", + " epochs=10 # Set the number of epochs\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Tv3WJTGC9Dt1" + }, + "outputs": [], + "source": [ + "model.save(\"/content/drive/MyDrive\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tGE3OAL4_YWp", + "outputId": "66ee96e8-5664-49bf-b9b8-71ec6b9f62b9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "133/133 [==============================] - 84s 630ms/step - loss: 0.8085 - accuracy: 0.7002\n", + "Validation Loss: 0.8085159063339233\n", + "Validation Accuracy: 0.7001879811286926\n" + ] + } + ], + "source": [ + "evaluation_result = model.evaluate(validation_generator, steps=validation_generator.samples // batch_size)\n", + "print(\"Validation Loss:\", evaluation_result[0])\n", + "print(\"Validation Accuracy:\", evaluation_result[1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LUKlFJfZ-66C", + "outputId": "74a256ac-9a0a-411e-f58c-8158927c5d72" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n", + "58889256/58889256 [==============================] - 2s 0us/step\n", + "Epoch 1/10\n", + "535/535 [==============================] - 442s 809ms/step - loss: 1.1297 - accuracy: 0.6065 - val_loss: 0.9465 - val_accuracy: 0.6147\n", + "Epoch 2/10\n", + "535/535 [==============================] - 488s 913ms/step - loss: 1.0326 - accuracy: 0.6147 - val_loss: 0.9351 - val_accuracy: 0.6163\n", + "Epoch 3/10\n", + "535/535 [==============================] - 434s 812ms/step - loss: 1.0177 - accuracy: 0.6149 - val_loss: 0.9210 - val_accuracy: 0.6161\n", + "Epoch 4/10\n", + "535/535 [==============================] - 432s 807ms/step - loss: 1.0118 - accuracy: 0.6149 - val_loss: 0.9234 - val_accuracy: 0.6147\n", + "Epoch 5/10\n", + "535/535 [==============================] - 480s 898ms/step - loss: 1.0028 - accuracy: 0.6149 - val_loss: 0.9272 - val_accuracy: 0.6151\n", + "Epoch 6/10\n", + "535/535 [==============================] - 415s 776ms/step - loss: 0.9988 - accuracy: 0.6149 - val_loss: 0.9163 - val_accuracy: 0.6158\n", + "Epoch 7/10\n", + "535/535 [==============================] - 479s 896ms/step - loss: 0.9945 - accuracy: 0.6149 - val_loss: 0.9155 - val_accuracy: 0.6147\n", + "Epoch 8/10\n", + "535/535 [==============================] - 420s 784ms/step - loss: 0.9990 - accuracy: 0.6149 - val_loss: 0.9119 - val_accuracy: 0.6163\n", + "Epoch 9/10\n", + "535/535 [==============================] - 472s 883ms/step - loss: 0.9939 - accuracy: 0.6149 - val_loss: 0.9141 - val_accuracy: 0.6156\n", + "Epoch 10/10\n", + "535/535 [==============================] - 415s 776ms/step - loss: 0.9950 - accuracy: 0.6147 - val_loss: 0.9141 - val_accuracy: 0.6140\n", + "133/133 [==============================] - 84s 629ms/step - loss: 0.9111 - accuracy: 0.6151\n", + "Validation Loss: 0.9110695123672485\n", + "Validation Accuracy: 0.6151315569877625\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow.keras.applications import VGG16\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, Flatten, Dropout\n", + "\n", + "\n", + "# Load pre-trained VGG16 model\n", + "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(img_height, img_width, 3))\n", + "\n", + "# Freeze the layers of the pre-trained model\n", + "for layer in base_model.layers:\n", + " layer.trainable = False\n", + "\n", + "# Create a new model on top of the pre-trained model\n", + "model = Sequential()\n", + "model.add(base_model)\n", + "model.add(Flatten())\n", + "model.add(Dense(128, activation='relu'))\n", + "model.add(Dropout(0.5))\n", + "model.add(Dense(5, activation='softmax')) # 5 classes for your task\n", + "\n", + "# Compile the model\n", + "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n", + "\n", + "# Train the model\n", + "model.fit(\n", + " train_generator,\n", + " steps_per_epoch=train_generator.samples // batch_size,\n", + " validation_data=validation_generator,\n", + " validation_steps=validation_generator.samples // batch_size,\n", + " epochs=10 # Set the number of epochs\n", + ")\n", + "\n", + "# Evaluate the model on the validation set\n", + "evaluation_result = model.evaluate(validation_generator, steps=validation_generator.samples // batch_size)\n", + "print(\"Validation Loss:\", evaluation_result[0])\n", + "print(\"Validation Accuracy:\", evaluation_result[1])\n" + ] + } + ], + "metadata": { + "accelerator": "TPU", + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 559aaa89bde79558cb6ab4307b17f4b61d9be289 Mon Sep 17 00:00:00 2001 From: Keshav Arora <119474193+CoderOMaster@users.noreply.github.com> Date: Wed, 24 Jan 2024 13:25:24 +0530 Subject: [PATCH 10/11] Update README.md --- Cassava Leaf Disease Classification/README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/Cassava Leaf Disease Classification/README.md b/Cassava Leaf Disease Classification/README.md index 2dd58260c..2a8f9dbdf 100644 --- a/Cassava Leaf Disease Classification/README.md +++ b/Cassava Leaf Disease Classification/README.md @@ -43,6 +43,11 @@ https://www.kaggle.com/datasets/nirmalsankalana/cassava-leaf-disease-classificat **VISUALISATION** +![Alt Text](./Images/1.png) +EDA Analysis as Bar Graph(Before Undersampling) + +![Alt Text](./Images/2.png) +EDA Analysis as Pie Chart(Before Undersampling) ![Alt Text](./Images/Plot.png) From 52fbc15be02ac700e5183c3ebe576aef6751001a Mon Sep 17 00:00:00 2001 From: Keshav Arora <119474193+CoderOMaster@users.noreply.github.com> Date: Wed, 24 Jan 2024 13:25:57 +0530 Subject: [PATCH 11/11] Update README.md --- Cassava Leaf Disease Classification/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/Cassava Leaf Disease Classification/README.md b/Cassava Leaf Disease Classification/README.md index 2a8f9dbdf..94ee0f004 100644 --- a/Cassava Leaf Disease Classification/README.md +++ b/Cassava Leaf Disease Classification/README.md @@ -20,6 +20,7 @@ https://www.kaggle.com/datasets/nirmalsankalana/cassava-leaf-disease-classificat - OS,Shutil - Matplotlib - Scikit-Learn +- Seaborn ## IMPLEMENTATION 1. Load dataset (21,000 entries and 5 columns) @@ -44,9 +45,11 @@ https://www.kaggle.com/datasets/nirmalsankalana/cassava-leaf-disease-classificat **VISUALISATION** ![Alt Text](./Images/1.png) + EDA Analysis as Bar Graph(Before Undersampling) ![Alt Text](./Images/2.png) + EDA Analysis as Pie Chart(Before Undersampling) ![Alt Text](./Images/Plot.png)