From 0131de684d03372c7ff2355abc849c1bfa39c24e Mon Sep 17 00:00:00 2001 From: Vaishali Sharma Date: Wed, 3 Jul 2024 21:35:02 +0530 Subject: [PATCH] Image Classifivation with TensorFLow/Keras (#501) * images for webscraping blog Images that are use in Web Scraping blog. * Banner Image for WebScraping This is the banner image that you asked for to use on the blog. So here, I changed it for Web Scraping Introduction Blog. * image for chatbot content * flowchart image to build a chatbot with NLTK * Images of ChatBot with NLTK article * Image Classification with TensorFLow/Keras. * Image_Classification article's image * Image for ImageClassificationwithTensorFLow/Keras. --- .../ImageClassificationWithTensorFLow.html | 307 ++++++++++++++++++ posts/images/Image_Classification.png | Bin 0 -> 279352 bytes 2 files changed, 307 insertions(+) create mode 100644 posts/Image Classification/ImageClassificationWithTensorFLow.html create mode 100644 posts/images/Image_Classification.png diff --git a/posts/Image Classification/ImageClassificationWithTensorFLow.html b/posts/Image Classification/ImageClassificationWithTensorFLow.html new file mode 100644 index 0000000..2a9bc86 --- /dev/null +++ b/posts/Image Classification/ImageClassificationWithTensorFLow.html @@ -0,0 +1,307 @@ + + + + + + + + + Image Classification with TensorFlow/Keras + + + + + + + + + + +
+
+ +
+

+ Image Classification with TensorFlow/Keras +

+ +
+ Image Classification with TensorFlow +
+
+
+

Image classification is a fundamental task in computer vision, where the goal is to assign a label (class) to an input image. In this tutorial, we’ll explore how to build an image classification model using TensorFlow and Keras. You can either use a pre-trained model or create your own custom convolutional neural network (CNN).

+
+

Pre-requisites

+

Before we dive into the implementation, make sure you have the following installed:

+
    +
  • Python (preferably Python 3.6 or later)
  • +
  • TensorFlow (install using pip install tensorflow)
  • +
  • Keras (included with TensorFlow)
  • +
+

Workflow Overview

+
    +
  1. Data Preparation
  2. +
      +
    • Collect a labeled dataset of images. For example, you can use the Flower Photos dataset.
    • +
    • Organize the data into subdirectories, each representing a different class (e.g., roses, tulips, dandelions).
    • +
    +
  3. Load and Preprocess Data:
  4. +
      +
    • Use tf.keras.utils.image_dataset_from_directory to efficiently load images from disk.
    • +
    • Resize images to a consistent size (e.g., 180x180 pixels).
    • +
    • Normalize pixel values to the range [0, 1].
    • +
    +
  5. Model Building:
  6. +
      +
    • Choose between using a pre-trained model (transfer learning) or building your own CNN from scratch.
    • +
    • For transfer learning, load a pre-trained model (e.g., MobileNetV2, ResNet50) and fine-tune it for your specific task.
    • +
    • For custom CNN, design your architecture with convolutional layers, pooling layers, and fully connected layers.
    • +
    +
  7. Compile and Train the Model:
  8. +
      +
    • Compile the model with an appropriate optimizer, loss function, and evaluation metric.
    • +
    • Train the model on your labeled dataset.
    • +
    • Monitor training progress and adjust hyperparameters as needed.
    • +
    +
  9. Evaluate and Improve:
  10. +
      +
    • Evaluate the model’s performance on a validation set.
    • +
    • Address overfitting by using techniques like data augmentation and dropout.
    • +
    • Fine-tune the model based on evaluation results.
    • +
    +
  11. Prediction and Deployment:
  12. +
      +
    • Use the trained model to predict labels for new images.
    • +
    • Convert the model to TensorFlow Lite format for deployment on mobile devices or embedded systems.
    • +
    +
+

Example Code

+

Below is a simplified example of building an image classification model using a custom CNN:

+ Python:
+
+                                
+import tensorflow as tf
+from tensorflow.keras import layers
+
+# Load and preprocess data
+data_dir = "/path/to/flower_photos"
+batch_size = 32
+img_height, img_width = 180, 180
+
+train_ds = tf.keras.utils.image_dataset_from_directory(
+    data_dir,
+    validation_split=0.2,
+    subset="training",
+    seed=42,
+    image_size=(img_height, img_width),
+    batch_size=batch_size,
+)
+
+# Build a simple CNN
+model = tf.keras.Sequential([
+    layers.Conv2D(32, (3, 3), activation="relu", input_shape=(img_height, img_width, 3)),
+    layers.MaxPooling2D(),
+    layers.Flatten(),
+    layers.Dense(128, activation="relu"),
+    layers.Dense(num_classes, activation="softmax"),  # num_classes = number of flower classes
+])
+
+# Compile the model
+model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
+
+# Train the model
+model.fit(train_ds, epochs=10)
+
+# Evaluate the model (on validation set)
+validation_ds = tf.keras.utils.image_dataset_from_directory(
+    data_dir,
+    validation_split=0.2,
+    subset="validation",
+    seed=42,
+    image_size=(img_height, img_width),
+    batch_size=batch_size,
+)
+model.evaluate(validation_ds)
+
+                            
+                            
+ +

Remember to replace /path/to/flower_photos with the actual path to your dataset directory.

+

Conclusion

+

Image classification with TensorFlow and Keras is a powerful technique that can be applied to various domains, from recognizing objects in photos to medical diagnosis. Experiment with different architectures, hyperparameters, and datasets to improve your model’s accuracy!

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