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Add the following snippet to your HTML:. Read up about this project on. I always wanted to build my own training data set and use it to train the deep learning model. A fairly accurate model with a latency of seconds runs on the Google AIY Vision box and does not require access to Internet or the Cloud.
It can be used to control your mobile robot, replace your TV remote control, or for many other applications. Two important qualities of a successful deep learning model used for real-time applications are robustness and low latency. Model robustness can be improved by having a high degree of control over the image background. This would also help to reduce the model training time and the size of the required training data set.
Model latency can be improved if we reduce the possible search region and would only look for the hand command in a specific part of the image where this command is likely displayed instead of scanning the entire image with sliding windows of different sizes.
This Aiy model preview how the Hand Command Recognizer works. First, the recognizer tries to detect and locate a human face on the image and make sure it is stable and does not move around. Given the size and location of the detected face box, the recognizer estimates the size and location of the chest box where the hand commands will likely be displayed.
This eliminates the need for searching for the hand command over the entire image and therefore greatly reduces the latency during model inference. Because we can decide what T-short of jacket we put on, we have a high degree of control over the background of the classified image which increases the model robustness, eliminate the need to collects the large training data set and reduces the model's training time. A diversity of possible backgrounds is also limited by the of available T-shirts and jackets in our wardrobe.
Every time you run the script you should provide the values for two arguments:. In order to make image collection process easier, each recording session is broken down in two stages. Practical suggestions during the collection of training data:. Follow Steps 1 to 4 of these instructions. The only differences are. Below is a screenshot with the command to train your model the script retrain. The available parameters are well explained in retrain.
Feel free to skip the rest steps of TensorFlowfor Poets tutorial. Make sure you don't run it on Google Vision Kit! You can find more information on Google AIY projects. Log in up. Dmitri Villevald. Collect Training Images Step 3. Train Your Model Step 4. Compile Model on Linux Machine Step 5. Intermediate Full instructions provided 10 hours 5, Things used in this project.
Motivation I always wanted to build my own training data Aiy model preview and use it to train the deep learning model. Use this dataset and transfer learning to build the Hand Command Classifier by retraining the last layer of MobileNet model. Classifier De Two important qualities of a successful deep learning model used for real-time applications are robustness and low latency.
Use it to retrain the last layer of MobileNet network with your own training data on your PC. Copyright The TensorFlow Authors. All Rights Reserved. d under the ApacheVersion 2. See the for the specific language governing permissions and limitations under the. With support for TensorBoard. This example shows how to take a Inception v3 or Mobilenet model trained on ImageNet images, and train a new top layer that can recognize other classes of images.
The top layer receives as input a dimensional vector dimensional for Mobilenet for each image. We train a softmax layer on top of this representation. Here's an example, which assumes you have a folder containing class-named subfolders, each full of images for each label. The label for each image is taken from the name of the subfolder it's in. By default this script will use the high accuracy, but comparatively large and slow Inception v3 model architecture.
The first can be '1. These include things like tensor names and their sizes. If you want to adapt this script to work with another model, you will need to update these to reflect the values in the network you're using. Analyzes the sub folders in the image directory, splits them into stable training, testing, and validation sets, and returns a data structure describing the lists of images for each label and their paths. Returns: A dictionary containing an entry for each label subfolder, with images split into training, testing, and validation sets within each label. Some images will ' 'never be selected.
For example this is used in the plant disease data set to group multiple pictures of the same leaf. To do that, we need a stable way of deciding based on just the file name itself, so we do a hash of that and then use that to generate a probability value that we use to as it. This will be moduloed by the available of images for the label, so it can be arbitrarily large. Returns: File system path string to an image that meets the requested parameters.
Returns: Graph holding the trained Inception network, and various tensors we'll be manipulating. ParseFromString f. Args: sess: Current active TensorFlow Session. Returns: Numpy array of bottleneck values. If the pretrained model we're using doesn't already exist, this function downlo it from the TensorFlow.
If a cached version of the bottleneck data exists on-disk, return that, otherwise calculate the data and save it to disk for future use. Args: sess: The current active TensorFlow Session. This will be modulo-ed by the available of images for the label, so it can be arbitrarily large. Returns: Numpy array of values produced by the bottleneck layer for the image. Because we're likely to read the same image multiple times if there are no distortions applied during training it can speed things Aiy model preview a lot if we calculate the bottleneck layer values once for each image during preprocessing, and then just read those cached values repeatedly during training.
Here we go through all the images we've Aiy model preview, calculate those values, and save them off. Returns: Nothing. If no distortions are being Aiy model preview, this function can retrieve the cached bottleneck values directly from disk for images.
It picks a random set of images from the specified category. Args: sess: Current TensorFlow Session. If negative, all bottlenecks will be retrieved. Returns: List of bottleneck arrays, their corresponding ground truths, and the relevant filenames. If we're training with distortions like crops, scales, or flips, we have to recalculate the full model for every image, and so we can't use cached bottleneck values.
Instead we find random images for the requested category, run them through the distortion graph, and then the full graph to get the bottleneck for each. Returns: List of bottleneck arrays and their corresponding ground truths.
This involves 2 memory copies and might be optimized in other implementations. Returns: Boolean value indicating whether any distortions should be applied. During training it can help to improve the if we run the images through simple distortions like crops, scales, and flips. These reflect the kind of variations we expect in the real world, and so can help train the model Aiy model preview cope with natural data more effectively.
Here we take the supplied parameters and construct a network of operations to apply them to an image. The cropping parameter controls the size of that box relative to the input image. If it's zero, then the box is the same size as the input and no cropping is performed. For example if the scale percentage is zero, then the bounding box is the same size as the input and no scaling is applied. Returns: The jpeg input layer and the distorted result tensor.
We need to retrain the top layer to identify our new classes, so this function adds the right operations to the graph, along with some variables to hold the weights, and then sets up all the gradients for the backward pass. Returns: The tensors for the training and cross entropyand tensors for the bottleneck input and ground truth input. Variable tf. Returns: Tuple of evaluation step, prediction. There are different base image recognition pretrained models that can be retrained using transfer learning, and this function translates from the name of a model to the attributes that are needed to download and train with it.
Args: architecture: Name of a model architecture. Returns: Dictionary of information about the model, or None if the name isn't recognized Raises: ValueError: If architecture name is unknown. This file has been truncatedplease download it to see its full contents. How it works: The session has two stages. Stage 1. Raw image collection During this stage you would display in front of a camera the hand gesture for a specific label which name you specify in the command-line argument --label.
Suggestions: 1 Select a reasonable of images to capture within the session so you don't get tired. For example, you may record images for each label wearing a red T-short in a bright room environment 1then record another images for each label wearing a blue sweater in another room environment 2etc.
ArgumentParser parser. If different from sensor mode implied resolution, inference must be adjusted accordingly. This is true in particular when camera. Hand Gesture Classifier github Repo with scripts mentioned in the projects.
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