Function can be used to generate feature service that contains polygons on detected objects found in the imagery data using the designated deep learning model. Optional dictionary. Name-value pairs of arguments and their values that can be customized by the clients. If not provided, a Feature layer is created by the method and used as the output.

Alternatively, you can pass in the name of the output Feature Service that should be created by this method to be used as the output for the tool. A RuntimeError is raised if a service by that name already exists. Optional string. The field in the feature class that contains the confidence scores as output by the object detection method. The class value field in the input feature class.

If not specified, the function will use the standard class value fields Classvalue and Value. If these fields do not exist, all features will be treated as the same object class. Optional integer. The maximum overlap ratio for two overlapping features. Defined as the ratio of intersection area over union area. Context contains additional settings that affect task execution.

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Dictionary can contain value for following keys:. Setting context parameter will override the values set using arcgis. False : all raster items in the image service will be mosaicked together and processed. This is the default.

fastai—A Layered API for Deep Learning

Keyword only parameter. Optional boolean. If True, the result will be a GPJob object and results will be returned asynchronously. Function can be used to output feature service with assigned class label for each feature based on information from overlapped imagery data using the designated deep learning model. Optional feature layer. The point, line, or polygon input feature layer that identifies the location of each object to be classified and labelled. Each row in the input feature layer represents a single object.

If no input feature layer is specified, the function assumes that each input image contains a single object to be classified. If the input image or images use a spatial reference, the output from the function is a feature layer, where the extent of each image is used as the bounding geometry for each labelled feature layer.

If the input image or images are not spatially referenced, the output from the function is a table containing the image ID values and the class labels for each image. Optional str. The name of the field that will contain the classification label in the output feature layer.

If no field name is specified, a new field called ClassLabel will be generated in the output feature layer.Abstract: fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches.

It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library.

We have used this library to successfully create a complete deep learning course, which we were able to write more quickly than using previous approaches, and the code was more clear. The library is already in wide use in research, industry, and teaching. Other libraries have tended to force a choice between conciseness and speed of development, or flexibility and expressivity, but not both.

This goal of getting the best of both worlds has motivated the design of a layered architecture.

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A high-level API powers ready-to-use functions to train models in various applications, offering customizable models with sensible defaults. It is built on top of a hierarchy of lower level APIs which provide composable building blocks. The high-level of the API is most likely to be useful to beginners and to practitioners who are mainly in interested in applying pre-existing deep learning methods.

It offers concise APIs over four main application areas: vision, text, tabular and time-series analysis, and collaborative filtering. These APIs choose intelligent default values and behaviors based on all available information.

For instance, fastai provides a single Learner class which brings together architecture, optimizer, and data, and automatically chooses an appropriate loss function where possible. Integrating these concerns into a single class enables fastai to curate appropriate default choices. To give another example, generally a training set should be shuffled, and a validation does not need to be.

So fastai provides a single DataLoaders class which automatically constructs validation and training data loaders with these details already handled. In addition, because the training set and validation set are integrated into a single class, fastai is able, by default, always to display metrics during training using the validation set.

This use of intelligent defaults—based on our own experience or best practices—extends to incorporating state-of-the-art research wherever possible. For instance, transfer learning is critically important for training models quickly, accurately, and cheaply, but the details matter a great deal.

As a result, every line of user code tends to be more likely to be meaningful, and easier to read. The mid-level API provides the core deep learning and data-processing methods for each of these applications, and low-level APIs provide a library of optimized primitives and functional and object-oriented foundations, which allows the mid-level to be developed and customised.

In order to achieve its goal of hackability, the library does not aim to supplant or hide these lower levels or these foundation. Within a fastai model, one can interact directly with the underlying PyTorch primitives; and within a PyTorch model, one can incrementally adopt components from the fastai library as conveniences rather than as an integrated package.

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We believe fastai meets its design goals. A user can create and train a state-of-the-art vision model using transfer learning with four understandable lines of code. Perhaps more tellingly, we have been able to implement recent deep learning research papers with just a couple of hours work, whilst matching the performance shown in the papers.

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The following sections describe the main functionality of the various API levels in more detail and review prior related work. We chose to include a lot of code to illustrate the concepts we are presenting. While that code made change slightly as the library or its dependencies evolve it is running against fastai v2. The next section reviews the high-level APIs "out-of-the-box" applications for some of the most used deep learning domains. The applications provided are vision, text, tabular, and collaborative filtering.

This is not an excerpt; these are all of the lines of code necessary for this task. Each line of code does one important task, allowing the user to focus on what they need to do, rather than minor details. This first line imports all the necessary pieces from the library. The library is carefully designed to ensure that importing in this way only imports the symbols that are actually likely to be useful to the user and avoids cluttering the namespace or shadowing important symbols.What other bet365 offers are there.

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fastai—A Layered API for Deep Learning

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