Ssdlite mobilenet v2

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Ssdlite mobilenet v2

Efficient networks optimized for speed and memory, with residual blocks. All pre-trained models expect input images normalized in the same way, i. The MobileNet v2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input. MobileNet v2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer.

Additionally, non-linearities in the narrow layers were removed in order to maintain representational power. To analyze traffic and optimize your experience, we serve cookies on this site.


By clicking or navigating, you agree to allow our usage of cookies. Learn more, including about available controls: Cookies Policy. MobileNet v2 By Pytorch Team. Efficient networks optimized for speed and memory, with residual blocks View on Github Open on Google Colab. Compose [ transforms. Resizetransforms. CenterCroptransforms.

ssdlite mobilenet v2

ToTensortransforms. To get probabilities, you can run a softmax on it. Tutorials Get in-depth tutorials for beginners and advanced developers View Tutorials. Resources Find development resources and get your questions answered View Resources.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Does this model learn anything at all? Learn more. Ask Question. Asked 1 year, 9 months ago. Active 1 year, 9 months ago. Viewed times. Farshad Farshad 11 1 1 silver badge 6 6 bronze badges. Active Oldest Votes. I determined the number of classes and path of tfrecords as well.

ssdlite mobilenet v2

Also i reduced the batch size to 6 because of low memory. I think i should select a bigger batch size in ssd model in order that ssd reduce the loss properly. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown.

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The Overflow Blog. The Overflow How many jobs can be done at home?A little less than a year ago I wrote about MobileNetsa neural network architecture that runs very efficiently on mobile devices. Recently researchers at Google announced MobileNet version 2. This is mostly a refinement of V1 that makes it even more efficient and powerful.

Naturally, I made an implementation using Metal Performance Shaders and I can confirm it lives up to the promise. The big idea behind MobileNet V1 is that convolutional layers, which are essential to computer vision tasks but are quite expensive to compute, can be replaced by so-called depthwise separable convolutions.

It does approximately the same thing as traditional convolution but is much faster. There are no pooling layers in between these depthwise separable blocks. Instead, some of the depthwise layers have a stride of 2 to reduce the spatial dimensions of the data.

When that happens, the corresponding pointwise layer also doubles the number of output channels. As is common in modern architectures, the convolution layers are followed by batch normalization. This is like the well-known ReLU but it prevents activations from becoming too big:. There is actually more than one MobileNet.

It was designed to be a family of neural network architectures. There are several hyperparameters that let you play with different architecture trade-offs. This changes how many channels are in each layer. Using a depth multiplier of 0. It is therefore much faster than the full model but also less accurate. Thanks to the innovation of depthwise separable convolutions, MobileNet has to do about 9 times less work than comparable neural nets with the same accuracy.

For a more in-depth look, check out my previous blog post or the original paper. MobileNet V2 still uses depthwise separable convolutions, but its main building block now looks like this:. This time there are three convolutional layers in the block. In V1 the pointwise convolution either kept the number of channels the same or doubled them. In V2 it does the opposite: it makes the number of channels smaller. This is why this layer is now known as the projection layer — it projects data with a high number of dimensions channels into a tensor with a much lower number of dimensions.

For example, the depthwise layer may work on a tensor with channels, which the projection layer will then shrink down to only 24 channels. This kind of layer is also called a bottleneck layer because it reduces the amount of data that flows through the network. The first layer is the new kid in the block.

Its purpose is to expand the number of channels in the data before it goes into the depthwise convolution. Hence, this expansion layer always has more output channels than input channels — it pretty much does the opposite of the projection layer.


Exactly by how much the data gets expanded is given by the expansion factor. This is one of those hyperparameters for experimenting with different architecture tradeoffs. The default expansion factor is 6. Next, the depthwise convolution applies its filters to that -channel tensor.

And finally, the projection layer projects the filtered channels back to a smaller number, say 24 again. So the input and the output of the block are low-dimensional tensors, while the filtering step that happens inside block is done on a high-dimensional tensor.

This works just like in ResNet and exists to help with the flow of gradients through the network. The residual connection is only used when the number of channels going into the block is the same as the number of channels coming out of it, which is not always the case as every few blocks the output channels are increased.Posted by Mark Sandler and Andrew Howard, Google Research Last year we introduced MobileNetV1a family of general purpose computer vision neural networks designed with mobile devices in mind to support classification, detection and more.

The ability to run deep networks on personal mobile devices improves user experience, offering anytime, anywhere access, with additional benefits for security, privacy, and energy consumption. As new applications emerge allowing users to interact with the real world in real time, so does the need for ever more efficient neural networks. Today, we are pleased to announce the availability of MobileNetV2 to power the next generation of mobile vision applications.

MobileNetV2 is a significant improvement over MobileNetV1 and pushes the state of the art for mobile visual recognition including classification, object detection and semantic segmentation. Alternately, you can download the notebook and explore it locally using Jupyter.

MobileNetV2 is also available as modules on TF-Hub, and pretrained checkpoints can be found on github. MobileNetV2 builds upon the ideas from MobileNetV1 [1], using depthwise separable convolution as efficient building blocks. However, V2 introduces two new features to the architecture: 1 linear bottlenecks between the layers, and 2 shortcut connections between the bottlenecks 1. The basic structure is shown below. Overview of MobileNetV2 Architecture.

Blue blocks represent composite convolutional building blocks as shown above. Finally, as with traditional residual connections, shortcuts enable faster training and better accuracy.

How does it compare to the first generation of MobileNets? Overall, the MobileNetV2 models are faster for the same accuracy across the entire latency spectrum. MobileNetV2 improves speed reduced latency and increased ImageNet Top 1 accuracy MobileNetV2 is a very effective feature extractor for object detection and segmentation. Follow googleai. Give us feedback in our Product Forums. Google Privacy Terms.This will use many of the techniques that were shown throughout the book, such as:.

如何评价mobilenet v2 ?

An object detector can find the locations of several different types of objects in the image. The detections are described by bounding boxes, and for each bounding box the model also predicts a class.

There are many variations of SSD. Another common model architecture is YOLO. Like SSD it was designed to run in real-time.

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There are many architectural differences between them, but in the end both models make predictions on a fixed-size grid. Each cell in this grid is responsible for detecting objects in a particular location in the original input image. What matters is that they take an image as input and produce a tensor, or multi-array as Core ML calls it, of a certain size as output. This tensor contains the bounding box predictions in one form or another.

For an in-depth explanation of how these kinds of models work and how they are trained, see my blog post One-shot object detection. The number of bounding boxes per cell is 3 for the largest grid and 6 for the others, giving a total of boxes. These models always predict the same number of bounding boxes, even if there is no object at a particular location in the image.

To filter out the useless predictions, a post-processing step called non-maximum suppression or NMS is necessary. In order to turn the predictions into true rectangles, they must be decoded first. Until recently, the decoding and NMS post-processing steps had to be performed afterwards in Swift. The model would output an MLMultiArray containing the grid of predictions and you had to loop through the cells and perform these calculations yourself.

But as of iOS 12 and macOS You simply perform a Vision request on the image and the result is an array of VNRecognizedObjectObservation objects that contain the coordinates and class labels for the bounding boxes.

Vision automatically decodes the predictions for you and even performs NMS. How convenient is that!

ssdlite mobilenet v2

You can download it here. Note: The following instructions were tested with coremltools 2. The part of the TensorFlow graph that we keep has one input for the image and two outputs: one for the bounding box coordinate predictions and one for the classes.

TensorFlow models can be quite complicated, so it usually takes a bit of searching to find the nodes you need. Another trick is to simply print out a list of all the operations in the graph and look for ones that seem reasonably named, then run the graph up to that point and see what sort of results you get. Interestingly, they use a different output node. SSD does multi-label classification on the class predictions, which applies a sigmoid activation to the class scores. By including this node it saves us from having to apply the sigmoid ourselves.

This is fairly straightforward usage of tfcoreml. We specify the same inputs and output names again but this time they need to have the :0 appended. The image preprocessing options are typical for TensorFlow image models: first divide by Often TensorFlow models already do their own normalization and this one is no exception. After a few brief moments, if all goes well, tfcoreml completes the conversion and saves the mlmodel to a file.

Also remember the model always predicts bounding boxes for any image. Most of these bounding boxes will not have detected an actual object. In theory we could start using the converted model already, but I always like to clean up the converted model first.

How computers learn to recognize objects instantly - Joseph Redmon

Those are pretty meaningless — and ugly!Over the past 18 months or so, a number of new neural network achitectures were proposed specifically for use on mobile and edge devices.

I have previously written about MobileNet v1 and v2and have used these models in many client projects. The story on Android may well be very different! Especially if you want to make predictions often, for example on real-time video. A neural network such as ResNet — which is a typical backbone model used in research papers — will use too much power and is unsuitable for real-time use.

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In general, the larger model is, the better results it gives. But also, the slower it runs and the more energy it eats up. Large models quickly make the phone hot and drain the battery. A smaller model will run faster and use less battery power, but typically gives less accurate results. Your job as mobile ML practitioner is to find a model architecture that is fast enough but also gives good enough results.

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Previously, I would usually recommend MobileNet v1 or v2. Note: If your app only occasionally uses the neural network, you can get away with using a larger model such as ResNet However, keep in mind that a large model also makes the app bundle a lot bigger. Usually these models are trained on ImageNet and we can tell how good they are by their classification accuracy on the ImageNet test set.

This could be classification or another task such as object detection, pose estimation, segmentation, and so on. You take an existing model that was pre-trained on a popular generic dataset such as ImageNet or COCO, and use that as the feature extractor. Then you add some new layers on top and fine-tune these new layers on your own data. Note: I am regularly approached by clients who ask me to convert the model from a research paper to run on Core ML.

They typically use a large feature extractor such as ResNet. Converting such a model as a proof-of-concept can be worth doing, but once that works my recommendation is to re-train the model from scratch using a smaller feature extractor.

ssdlite mobilenet v2

Number of trained parameters: This determines the size of the Core ML model file, but also has a direct relationship to the runtime speed and the prediction quality.

In general, more parameters means the model is slower but gives better results — however, this does not always hold. ImageNet classification accuracy: I will use published values from the original papers for this.

You can also find these scores in various benchmarks online. Sometimes the website or paper for a given model will claim it uses such-and-such number of FLOPS, or floating point operations per second, which is a measure of how much computation is performed. You can use that as an indicator of speed, but check out my blog post How fast is my model?

The only way to find out how well a model truly works on a given device, is to run it and measure the results. An untrained model does the same amount of computation as the trained version of that model and therefore runs at the same speed.

Speed is important because no one likes waiting.Looking to easily implement your new object detection model in an iOS app? Start building with a free Fritz AI account. They take an image as input and produce two tensors as output. These tensors multi-arrays, as Apple calls them contain information about anchor positions, anchors to bounding boxes, transformations, and the confidences that the bounding boxes have particular classes within them.

These models are designed to make predictions for particular bounding boxes in an image called anchors. The first tensor give us a prediction for every anchor and class.

So the second tensor gives us information about how we should tune our anchor to receive the perfect bounding box for our object.

Post-processing consists of the following steps:. Personalization, instant expertise, game changing user experiences — these are just a few of the values machine learning can add to mobile apps. Subscribe to the Fritz AI Newsletter to discover more. You can also follow the instructions from my previous tutorial to get up and running. You can rename it as you wish. This will automatically generate Core ML access methods to your model. The last thing to do in testing your model is to change name of the model in ObjectDetectionViewController.

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New mobile neural network architectures

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