Object-Detection-and-Tracking | Object Detection and Multi-Object Tracking | Computer Vision library
kandi X-RAY | Object-Detection-and-Tracking Summary
kandi X-RAY | Object-Detection-and-Tracking Summary
Object Detection and Multi-Object Tracking
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Top functions reviewed by kandi - BETA
- Load weights .
- Load Keras model or weights .
- Use yolo4 loss .
- Get random image data .
- Parses and parses images .
- Run yolo .
- Create a convolutional network .
- Preprocess true boxes .
- Compute the loss for a set of anchors .
- Compute the non - maximum suppression .
Object-Detection-and-Tracking Key Features
Object-Detection-and-Tracking Examples and Code Snippets
Lines of Code : 58License : Permissive (MIT)
Copy# @Dataset data
# @CommandService cs
# @ModuleService ms
from sc.fiji.maskflow import ObjectDetector
inputs = {"model": None,
"modelName": "Microtubule",
"dataset": data,
"fillROIManager": True}}
module = ms.waitFor(cs 
License : No LicenseCopygit clone https://github.com/ammarchalifah/people-tracker-and-counter.git
cd people-tracker-and-counter
mkdir models
people-tracker-and-counter
└───models
├──efficientdet_d0_coco17_tpu-32
| ├───checkpoint
| └───saved_model
| MOT17_ROOT/
|->train/
| |->MOT17-02/
| |->MOT17-04/
| |->...
|->test/
| |->MOT17-01/
| |->MOT17-03/
| |->...
|->train_annots.csv
Community Discussions
Trending Discussions on Object-Detection-and-Tracking
QUESTION
Neural networks can be trained to recognize an object, then detect occurrences of that object in an image, regardless of their position and apparent size. An example of doing this in PyTorch is at https://towardsdatascience.com/object-detection-and-tracking-in-pytorch-b3cf1a696a98
As the text observes,
Most of the code deals with resizing the image to a 416px square while maintaining its aspect ratio and padding the overflow.
So the idea is that the model always deals with 416px images, both in training and in the actual object detection. Detected objects, being only part of the image, will typically be smaller than 416px, but that's okay because the model has been trained to detect patterns in a scale-invariant way. The only thing fixed is the size in pixels of the input image.
I'm looking at a context in which it is necessary to do the reverse: train to detect patterns of a fixed size, then detect them in a variable sized image. For example, train to detect patterns 10px square, then look for them in an image that could be 500px or 1000px square, without resizing the image, but with the assurance that it is only necessary to look for 10px occurrences of the pattern.
Is there an idiomatic way to do this in PyTorch?
...ANSWER
Answered 2020-Nov-16 at 16:43Even if you trained your detector with a fixed size image, you can use a different sizes at inference time because everything is convolutional in faster rcnn/yolo architectures. On the other hand, if you only care about 10X10 bounding box detections, you can easily define this as your anchors. I would recomend to you to use the detectron2 framework which is implemented in pytorch and is easily configurable/hackable.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install Object-Detection-and-Tracking
You can use Object-Detection-and-Tracking like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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