Build label_image tensorflow example with Windows10

To build the label_image TensorFlow example on Windows 10, you'll need to follow these steps:

1. Install Dependencies:

   Install the required dependencies using pip:

   bashCopy code
   pip install tensorflow
   
   pip install pillow
   
   

2. Clone the TensorFlow Repository:

   Clone the TensorFlow repository from GitHub:

   bashCopy code
   git clone https://github.com/tensorflow/tensorflow.git
   
   

3. Navigate to the label_image Example:

   Navigate to the label_image example directory:

   bashCopy code
   cd tensorflow/tensorflow/examples/label_image
   
   

4. Download a Pre-trained Model:

   Download a pre-trained model from the TensorFlow Hub or other sources. For example, you can download the Inception v3 model:

   bashCopy code
   curl -O https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz
   
   tar -xzf inception_v3_2016_08_28_frozen.pb.tar.gz
   
   

5. Build and Run the Example:

   Run the label_image.py script with the downloaded model:

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   python label_image.py --graph=inception_v3_2016_08_28_frozen.pb --image=/path/to/your/image.jpg
   
   

   Replace /path/to/your/image.jpg with the path to the image you want to classify.

The label_image.py script should now use the Inception v3 model to classify the provided image and display the results.

Please note that TensorFlow has evolved over time, and the details might have changed depending on the version you're using. Make sure to check the TensorFlow documentation or the repository for any updates specific to the version you have.

Keep in mind that you might need to adjust the paths, dependencies, or other settings based on your environment and specific requirements.