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What is missing from MATLAB?
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- action recognition: https://github.com/open-mmlab/mmaction
- video understanding, X3D: https://github.com/facebookresearch/SlowFast ,https://github.com/kenshohara/3D-ResNets-PyTorch
- reID: https://github.com/JDAI-CV/fast-reid
- track: https://github.com/ZQPei/deep_sort_pytorch,https://votchallenge.net/challenges.html
- Metric Learning: https://github.com/KevinMusgrave/pytorch-metric-learning
- image Classification: efficient,https://github.com/lukemelas/EfficientNet-PyTorch
- object detection: Centernet, https://github.com/xingyizhou/CenterNet , efficientDet, https://github.com/google/automl/tree/master/efficientdet,detectron2,https://github.com/facebookresearch/detectron2 ,PointRCNN,https://github.com/sshaoshuai/PointRCNN
- face recognition: insightface, https://github.com/deepinsight/insightface, sphereface,https://github.com/wy1iu/sphereface, arcface,https://github.com/ronghuaiyang/arcface-pytorch
- Semantic segmentation, instance segmentation,https://github.com/facebookresearch/maskrcnn-benchmark
- deeplearning preprocess data: https://github.com/NVIDIA/DALI
- StyleGAN: https://github.com/NVlabs/stylegan
- DeepNetwork Architecture:Mnasnet,https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
- deepnetwork inference performance: https://github.com/microsoft/onnxruntime ,https://github.com/zuoqing1988/ZQCNN
- action recognition: https://github.com/open-mmlab/mmaction
- video understanding, X3D: https://github.com/facebookresearch/SlowFast ,https://github.com/kenshohara/3D-ResNets-PyTorch
- reID: https://github.com/JDAI-CV/fast-reid
- track: https://github.com/ZQPei/deep_sort_pytorch,https://votchallenge.net/challenges.html
- Metric Learning: https://github.com/KevinMusgrave/pytorch-metric-learning
- image Classification: efficient,https://github.com/lukemelas/EfficientNet-PyTorch
- object detection: Centernet, https://github.com/xingyizhou/CenterNet , efficientDet, https://github.com/google/automl/tree/master/efficientdet,detectron2,https://github.com/facebookresearch/detectron2 ,PointRCNN,https://github.com/sshaoshuai/PointRCNN
- face recognition: insightface, https://github.com/deepinsight/insightface sphereface,https://github.com/wy1iu/sphereface, arcface,https://github.com/ronghuaiyang/arcface-pytorch
- Semantic segmentation, instance segmentation,https://github.com/facebookresearch/maskrcnn-benchmark
- deeplearning preprocess data: https://github.com/NVIDIA/DALI
- StyleGAN: https://github.com/NVlabs/stylegan
- DeepNetwork Architecture:Mnasnet,https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
- deepnetwork inference performance: https://github.com/microsoft/onnxruntime ,https://github.com/zuoqing1988/ZQCNN
- Native support for custom keyboard shortcuts, i.e. let me point to a function to execute when a key-combo is pressed. Currently I can put them in the "Favorites" and add them to quick-access and order them for Alt+1, Alt+2, etc, which is alright, not great if you want more than 2 or 3. There is also theEditorMacro from File Exchange, which is good, but would be nice if it was baked-in to Matlab so I could more easily share helper functions with colleagues.
- Better git support. VS Code is light-years ahead here, for example.
- Extended syntax highlighting. Built-in keywords are highlighted, let's add a color for variables, functions calls, and class constructors/static methods. The example screenshots here are a nice example of how much more readable code is with functions highlighted.
- Bring the improvements of function parameter help text from live scripts to the regular .m files
- bar graph interface/modifications terribly difficult; labeling the bars in a grouped bar plot one of the most egregious oversights.
- no builtin hatching patterns yet after 30 years...whassup w/ that; had 'em w/ Calcomp plotters in the '60s...
- don't have consistent decimal format labelling by default on axes -- butt ugly; at least the numeric format (finally!!!) makes it a little easier but why have to fixup such trivial stuff manually?
- for countries where Mathworks permits students to use the online store, any product not listed in the online store for Student licenses, is not available for student licenses
- for countries where Mathworks permits academic or commercial clients to use the online store, any product not listed in the online store is expensive enough that it would be more expensive than the typical credit card limit, requiring a different purchase mechanism such as a Purchase Order.
If your data is tabular in nature, store it in a table (which can handle mixed numeric and non-numeric variables) and use the string selection capabilities to select rows and/or variables from the table. Using a subset of the example from the documentation page for table:
>> load patients >> patients = table(LastName,Gender,Age,Height,Weight); >> head(patients)
How many patients have a last name that starts with the letter J?
>> patients(startsWith(patients.LastName, 'J'), :)
Looks like 5 of them. Three of them are under the age of 40.
>> patients(startsWith(patients.LastName, 'J') & patients.Age < 40, :)
- Define the main class and inner classes in one single m-file.
- Ternary operator.
- Matrix-unpacking such that:
- Item one: A function that tests whether a string names a variable in a table: isVariable( mytable, 'height')
- Item two: A plotting routine that handles tables. For example, a function that takes a table and then a "formula" (as with fitlm). For example:
Both heatmap (introduced in release R2017a) and stackedplot (introduced in release R2018b) support visualizing data from table arrays. If there's a different type of plot that you would like to be able to create from a table, please send information about your use case to Technical Support so they can enter it into the enhancement database.
fprintf can accept string inputs in release R2018a.
>> version ans = '9.4.0.857798 (R2018a) Update 2'
>> fprintf("Hello %s\n", "dpb") Hello dpb
In Release R2018b you can "Specify text as string arrays where you previously specified text as character vectors or cell arrays of character vectors."
In release R2018b, the Release Notes has two entries relevant to this question:
- "Building Apps: Faster canvas interactions in App Designer"
- "Running Apps: Faster startup time for apps"
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