yolo | Ruby interface to Continuous Integration build tools | Continous Integration library

Generally speaking, batch size can be adjusted at any time without creating a problem. Each element of a batch is independent, but they are fed through the network together for efficiency reasons.

Note that batch size does affect training quality, as the gradients from larger batches will average out and have less variance. But that is irrelevant when doing inference (actually using the model).

You also asked what prevents you deploying a huge model on a small GPU, and the answer is simply performance. It is entirely possible to load part of a large model onto the GPU, run that part, load the next part, run it, and so on. You would need to balance the batch size and model part size, because if you only use a batch size of 1, the continual copying of model parameters will probably make it slower than running the whole model on the CPU.

It's an Anonymous function.

The usual way to use them is to either assign it to a variable, which would become the function's name:

I spent a large amount of time making things self-contained. Among the many changes:

1. you should not have the base class serializing the derived (that's classic OOP), instead Boost expects derived classes to serialize their base_object<> (allowing static polymorphism and, incidentally, type registration)

2. of course, the base class should serialize its data members (type)

3. the base class SHOULD have a virtual destructor (otherwise deleting through unique_ptr's destructor will be unspecified

   Up till here:

Wrapping the transform_targets_for_output method with a try-except-raise clause and applying tf.data.experimental.ignore_errors to the dataset seems to actually work:

You can use the imwrite function of cv2 library to store your files in the local directory, i.e.,

In your case, simply do this,

There is no direct way to convert the normalized Yolo format to another format like Pascal VOC, because you need to know the size of the image to do the conversion. (Just like you need to know the size of the image to convert it to the normalized yolo format in the first place.) You will also want to provide some mapping to convert the class numbers to class names.

I am working on a Python package to simplify these kinds of conversions called PyLabel. I have a sample notebook that will convert Yolo v5 annotations to VOC format here https://github.com/pylabel-project/samples/blob/main/yolo2voc.ipynb. You can open it in Colab using this link.

The core code will be something like this:

After our back and forth in the comments I have enough info to answer your question. This is output from the Google Vision API. The normalizedVertices are similar to the YOLO format, because they are "normalized" meaning the coordinates are scaled between 0 and 1 as opposed to being pixels from 1 to n. Still, you need to do some transformation to put into the YOLO format. In the YOLO format, the X and Y values in the 2nd and 3rd columns refer to the center of the bounding box, as opposed to one of the corners.

Here is a code snipped that will sample at https://ghostbin.com/hOoaz/raw into the follow string in YOLO format '0 0.5080664305 0.5624289849999999 0.9786587390000001 0.56914843'

Try this:

It seems it is an image processing issue.

Indeed with your example, the model loaded from torch hub gives a different output than detect.py. Looking at the source code of detect.py I see that there is some good image pre-processing. From the model hub, I really don't know what's happening to the input. From the model hub, the resulting image is this:

With their pre-processing, this is basically the image that you are feeding into the model. Would not expect any detections from this to be honest.

But then I tried doing the pre-processing myself (noted as well in their tutorial)