| {"guide": {"name": "image-classification-in-tensorflow", "category": "other-tutorials", "pretty_category": "Other Tutorials", "guide_index": null, "absolute_index": 67, "pretty_name": "Image Classification In Tensorflow", "content": "# Image Classification in TensorFlow and Keras\n\n\n\n\n## Introduction\n\nImage classification is a central task in computer vision. Building better classifiers to classify what object is present in a picture is an active area of research, as it has applications stretching from traffic control systems to satellite imaging.\n\nSuch models are perfect to use with Gradio's _image_ input component, so in this tutorial we will build a web demo to classify images using Gradio. We will be able to build the whole web application in Python, and it will look like the demo on the bottom of the page.\n\n\nLet's get started!\n\n### Prerequisites\n\nMake sure you have the `gradio` Python package already [installed](/getting_started). We will be using a pretrained Keras image classification model, so you should also have `tensorflow` installed.\n\n## Step 1 \u2014 Setting up the Image Classification Model\n\nFirst, we will need an image classification model. For this tutorial, we will use a pretrained Mobile Net model, as it is easily downloadable from [Keras](https://keras.io/api/applications/mobilenet/). You can use a different pretrained model or train your own.\n\n```python\nimport tensorflow as tf\n\ninception_net = tf.keras.applications.MobileNetV2()\n```\n\nThis line automatically downloads the MobileNet model and weights using the Keras library.\n\n## Step 2 \u2014 Defining a `predict` function\n\nNext, we will need to define a function that takes in the _user input_, which in this case is an image, and returns the prediction. The prediction should be returned as a dictionary whose keys are class name and values are confidence probabilities. We will load the class names from this [text file](https://git.io/JJkYN).\n\nIn the case of our pretrained model, it will look like this:\n\n```python\nimport requests\n\n# Download human-readable labels for ImageNet.\nresponse = requests.get(\"https://git.io/JJkYN\")\nlabels = response.text.split(\"\\n\")\n\ndef classify_image(inp):\n inp = inp.reshape((-1, 224, 224, 3))\n inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)\n prediction = inception_net.predict(inp).flatten()\n confidences = {labels[i]: float(prediction[i]) for i in range(1000)}\n return confidences\n```\n\nLet's break this down. The function takes one parameter:\n\n- `inp`: the input image as a `numpy` array\n\nThen, the function adds a batch dimension, passes it through the model, and returns:\n\n- `confidences`: the predictions, as a dictionary whose keys are class labels and whose values are confidence probabilities\n\n## Step 3 \u2014 Creating a Gradio Interface\n\nNow that we have our predictive function set up, we can create a Gradio Interface around it.\n\nIn this case, the input component is a drag-and-drop image component. To create this input, we can use the `\"gradio.inputs.Image\"` class, which creates the component and handles the preprocessing to convert that to a numpy array. We will instantiate the class with a parameter that automatically preprocesses the input image to be 224 pixels by 224 pixels, which is the size that MobileNet expects.\n\nThe output component will be a `\"label\"`, which displays the top labels in a nice form. Since we don't want to show all 1,000 class labels, we will customize it to show only the top 3 images.\n\nFinally, we'll add one more parameter, the `examples`, which allows us to prepopulate our interfaces with a few predefined examples. The code for Gradio looks like this:\n\n```python\nimport gradio as gr\n\ngr.Interface(fn=classify_image,\n inputs=gr.Image(width=224, height=224),\n outputs=gr.Label(num_top_classes=3),\n examples=[\"banana.jpg\", \"car.jpg\"]).launch()\n```\n\nThis produces the following interface, which you can try right here in your browser (try uploading your own examples!):\n\n<gradio-app space=\"gradio/keras-image-classifier\">\n\n---\n\nAnd you're done! That's all the code you need to build a web demo for an image classifier. If you'd like to share with others, try setting `share=True` when you `launch()` the Interface!\n", "tags": ["VISION", "MOBILENET", "TENSORFLOW"], "spaces": ["https://huggingface.co/spaces/abidlabs/keras-image-classifier"], "url": "/guides/image-classification-in-tensorflow/", "contributor": null}} |