This is usually done via supervised learning using a large set of labeled images. Although Inception v3 can be trained from many different labeled image sets, ImageNet is a common dataset of choice. ImageNet has over ten million URLs of labeled images. About a million of the images also have bounding boxes specifying a more precise location for the labeled objects.
For this model, the ImageNet dataset is composed of 1,, images which are split into training and evaluation datasets containing 1,, and 50, images, respectively. The training and evaluation datasets are kept separate intentionally. Only images from the training dataset are used to train the model and only images from the evaluation dataset are used to evaluate model accuracy. The model expects images to be stored as TFRecords. Larger slices have multiple hosts.
Other larger configurations interact with multiple hosts. For instance, v communicates with 16 hosts. Hosts retrieve data from the file system or local memory, do whatever data preprocessing is required, and then transfer preprocessed data to the TPU cores.
We consider these three phases of data handling done by the host individually and refer to the phases as: 1 Storage , 2 Preprocessing , 3 Transfer. A high level picture of the diagram is shown in the figure below. To yield good performance, the system should be balanced. Whatever amount of time a host CPU spends retrieving images, decoding them, and doing relevant preprocessing, should ideally be slightly less or about the same as that spent by the TPU doing computations.
If the host CPU takes longer than the TPU to complete the three data handling phases, then execution will be host bound. Note: because TPUs are so fast, this may be unavoidable for some very simple models. Both cases are shown in the diagram below. The current implementation of Inception v3 lives right at the edge of being input-bound. Images have to be retrieved from the file system, decoded, and then preprocessed. Different types of preprocessing stages are available, ranging from moderate to complex.
If we use the most complex of preprocessing stages, the large number of expensive operations executed by the preprocessing stage will push the system over the edge and the training pipeline will be preprocessing bound. However, it is not necessary to resort to that level of complexity to attain greater than This is discussed in more detail in the next section.
The model uses tf. Dataset to handle all input pipeline related needs. See the datasets performance guide for more information on how to optimize input pipelines using the tf. The Estimator API makes it very straightforward to use this class. The main elements of class InputPipeline are shown in the code snippet below, where we have highlighted the three phases with different colors. Dataset and then makes a series of API calls to utilize the built-in prefetch, interleave, and shuffling capabilities of the dataset.
The storage section begins with the creation of a dataset and includes the reading of TFRecords from storage using tf. Special purpose functions repeat and shuffle are used as needed. Function tf. The sloppy argument relaxes the requirement that the outputs be produced in a deterministic order, and allows the implementation to skip over nested datasets whose elements are not readily available when requested. The preprocessing section calls dataset. The details of the preprocessing stage are discussed in the next section.
The transfer section at the end of the function includes the line return images, labels. TPUEstimator takes the returned values and automatically transfers them to the device. TPU compute time, discounting any infeeding stalls, is currently at msecs or so. Host preprocessing , which includes image decoding and a series of image distortion functions is shown below:.
Image preprocessing is a crucial part of the system and can heavily influence the maximum accuracy that the model attains during training. At a minimum, images need to be decoded and resized to fit the model. In the case of Inception, images need to be xx3 pixels. However, simply decoding and resizing will not be enough to get good accuracy.
The ImageNet training dataset contains 1,, images. One pass over the set of training images is referred to as an epoch. During training, the model will require several passes through the training dataset to improve its image recognition capabilities. In the case of Inception v3, the number of epochs needed will be somewhere in the to range depending on the global batchsize.
It is extremely beneficial to continuously alter the images before feeding them to the model and to do so in such a manner that a particular image is slightly different at every epoch. How to best do this preprocessing of images is as much art as it is science.
On the one hand, a well designed preprocessing stage can significantly boost the recognition capabilities of a model. On the other, too simple a preprocessing stage may create an artificial ceiling on the maximum accuracy that the same model can attain during training.
Inception v3 offers different options for the preprocessing stage, ranging from relatively simple and computationally inexpensive to fairly complex and computationally expensive.
This section discusses the preprocessing pipeline. At evaluation time, preprocessing is quite straightforward: crop a central region of the image and then resize it to the default x size.
The snippet code that does this is shown below:. During training, the cropping is randomized: a bounding box is chosen randomly to select a region of the image which is then resized. The resized image is then optionally flipped and its colors are distorted. The snippet of code that does this is shown below:. It offers a fast mode where only brightness and saturation are modified.
The full mode modifies brightness, saturation, and hue, and randomly alters the order in which these get modified. A code snippet of this function is shown below:. Both fast and full modes require these conversions and although fast mode is less computationally expensive, it still pushes the model to the CPU-compute-bound region, when enabled. The function is shown in the code snippet below:. Here's a sample image that has undergone preprocessing. In addition, it yields good results and has been used successfully to train the Inception v3 model to greater than It is used as the default choice for Inception v3.
Stochastic gradient descent SGD is the simplest kind of update: the weights are nudged in the negative gradient direction. Despite its simplicity, good results can still be obtained on some models. The updates dynamics can be written as:. Momentum is a popular optimizer that frequently leads to faster convergence than can be attained by SGD. This optimizer updates weights much like SGD but also adds a component in the direction of the previous update.
The dynamics of the update are given by:. The last term is the component in the direction of the previous update. This is shown graphically in the figure below:. RMSprop is a popular optimizer first proposed by Geoff Hinton in one of his lectures.
The update dynamics are given by:. For Inception v3, tests show RMSProp giving the best results in terms of maximum accuracy and time to attain it, with momentum a close second. Thus RMSprop is set as the default optimizer.
When running on TPUs and using the Estimator API, the optimizer needs to be wrapped in a CrossShardOptimizer function in order to ensure synchronization among the replicas along with any necessary cross communication. The snippet of code where this is done in Inception v3 is shown below:.
The normal course of action while training is for trainable parameters to get updated during backpropagation in accordance to the optimizer update rules. These were discussed in the previous section and repeated here for convenience:. Exponential moving average also known as exponential smoothing is an optional post-processing step that is applied to the updated weights and can sometimes leads to noticeable improvements in performance.
Inception v3 benefits tremendously from having this additional step. TensorFlow provides the function tf. Even though this is an Infinite impulse response IIR filter, the decay factor establishes an effective window where most of the energy or relevant samples reside, as shown in the following diagram:. We first get a collection of trainable variables and then use the apply method to create shadow variables for each trained variable and add corresponding ops to maintain moving averages for these in their shadow copies.
A snippet of the code that does this on Inception v3 is shown below:. We'd like to use the ema variables during evaluation.
The hooks function is passed to evaluate as shown in the snippet below:. Batch normalization is a widely used technique for normalizing input features on models that can lead to substantial reduction in convergence time. It is one of the more popular and useful algorithmic improvements in machine learning of recent years and is used across a wide range of models, including Inception v3.
Activation inputs are first normalized by subtracting batch mean and dividing by the batch standard deviation, but batch normalization does more than that. To keep things balanced in the presence of back propagation, two trainable parameters are introduced in every layer.
The full set of equations is in the paper and is repeated here for convenience:. Normalization happens happily during training, but come evaluation time, we'd like the model to behave in a deterministic fashion: the classification result of an image should depend solely on the input image and not the set of images that are being fed to the model.
To accomplish this the model computes moving averages of the mean and variance over the minibatches:. In the specific case of Inception v3, a sensible decay factor had been obtained via hyperparameter tuning for use in GPUs. We would like to use this value on TPUs as well, but in order to do that, we need to make some adjustments.
In an 8x1 GPU synchronous job each replica reads the current moving mean and updates it. The updates are sequential, in the sense that the new moving variable must first be written by the current replica before the next one can read it. In the current moving moment calculation implementation on TPUs, each shard performs calculations independently and there's no cross-shard communication.
Although each shard goes through the motions and computes the moving moments that is, mean and variance , only the results from shard 0 are communicated back to the host CPU.
Specifically, the set of operations that comprise a set of 8 sequential updates on the GPU should be compared against a single update on the TPU. This is illustrated in the diagram below:. If we make the simplifying assumption that the 8 mini batches normalized across all relevant dimensions each yield similar values within the GPU 8-minibatch sequential update, then we can approximate these equations as follows:.
Therefore, to match the effect of a given decay factor on the GPU, we need to modify the decay factor on the TPU accordingly.
As batch sizes become larger, training becomes more difficult. Different techniques continue to be proposed to allow efficient training for large batch sizes see here , here , and here , for example. One of said techniques: gradual learning rate ramp-up, was used to train the model to greater than The learning rate remains constant at this low value for a specified small number of 'cold epochs', and then begins a linear increase for a specified number of 'warm-up epochs' at the end of which it intersects what would have been the learning rate should a normal exponential decay had been used.
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Health-specific solutions to enhance the patient experience. Fortunately now we just can change CMakeLists.
So I just created a repo here to make the whole thing work. When linemod is training it will show a assimp window, but it do not contain anything in my case, not a serious problem, linemod works anyway with KinectV1, but not with KinectV2, because KinectV2 has a special resolution, causing an OpenCV error in linearMemoryPyramid.
Fortunately again an awesome guy has worked it out , and he also fixed many other issues. This ork repo integrated all of them, just to make it easier to work on ork, maybe for myself in the future.
Nov 26 Mar 16 To use 10, trainaug images on DeeplabV3 code, you just need to do the following steps: 1. First, create output directory for storing TFRecords. As object detection has developed, different file formats to describe object annotations have emerged. This creates frustrating situations where teams dedicate time to converting from one annotation format to another rather than focusing on higher value tasks — like improving deep learning model architecture.
The most common annotation formats have emerged from challenges and amassed datasets. As machine learning researchers leveraged these datasets to build better models , the format of their annotation formats became unofficial standard protocol s. Jump to the bottom of this post to see how. Note a few key things: 1 the image file that is being annotated is mentioned as a relative path 2 the image metadata is included as width , height , and depth 3 bounding box pixels positions are denoted by the top left-hand corner and bottom right-hand corner as xmin , ymin , xmax , ymax.
The dataset "contains photos of 91 objects types that would be easily recognizable by a 4 year old. Given the sheer quantity and quality of data open sourced, COCO has become a standard dataset for testing and proving state of the art performance in new models.
The dataset is available here. Moreover, the COCO dataset supports multiple types of computer vision problems: keypoint detection, object detection , segmentation, and creating captions. Because of this, there are different formats for the task at hand. This post focuses on object detection. Note a few key things here: 1 there is information on dataset itself and its license 2 all labels included are defined as categories 3 bounding boxes are defined as the x , y coordinates of the upper-left hand corner followed by the bounding box's width and height.
To convert from one format to another , you can write or borrow a custom script or use a tool like Roboflow. GitHub user and Kaggle Master yukkyo created a script the Roboflow team has forked and slightly modified the repository for ease of use here:.
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