yolov7 VS edgetpu

If you want something off-the-shelf, try YoloV7.

The current dictionary is still in a beta state and has only been trained on 80 words (e.g. 'man', 'dog', 'car', 'keyboard', 'book', etc.; see list of words, see dataset). I used the object detection model Yolov7 (paper, all credits to them).

(Please note, this is a re-post of my original question here, I think this subreddit might be more appropriate for asking this question)At work, we use Unity, we have a project that needs object detection and classification. We decided to use this YOLO7 model (for non-technical reasons, It had to be the exact same model as the company does have pre-trained weights for this exact model). However, Unity only supports ONNX so I exported the model as an ONNX model, using the code provided in the repo:

Github Projects offering pretrained weights and train / run scripts. Example

The only project I found is this one that implements Yolov7

I'm wondering if there's an efficient way to visualize the image augmentations from the Yolov7 hyperparameters list here

yolov7: https://github.com/WongKinYiu/yolov7#performance

I know the tensor power pixelbook was shutdown and I never heard the actual reason just a bunch of speculation about costs/profitability which is probably true.

It's a shame that there isn't more competition and development in the neural asic world to harness the power of llms/generative AI on a low power, cheap hardware platform like the pixelbook line. For someone that invented the TPU they have done a not so great job of ensuring it's commercialization and support. Both on the hardware and software side.

The coral edge tpu seemed to be the right high level idea but without proper execution.

https://github.com/google-coral/edgetpu/issues/668

> even in the 80s I wanted an FPGA accelerators in every machine

Mostly unrelated, but I recently discovered that you can buy TPUs, right now, as a consumer product, from https://coral.ai.

The stock firmware already allows you to run these things so hard they overheat, which is amazing.

But yes, I also want FPGA accelerators.

I'm looking at the coral.ai cards and they have a M.2 A+E card, same form factor as the wifi slot in the m900 tiny. Has anyone tried another card in that slot other than wifi?

Chips optimized to perform the type of calculations used for NN inference at high parallelism. A good example would be the google spinoff https://coral.ai/ (though their usecase is highly limited by sub-par software constraints)

By no means an expert, but I have seen prototypes using a raspberry pi and a dongle from Coral Ai. They have PCIE and USB based modules.

Last news on https://coral.ai/ was on May 5 2022

Activity on the github project seems to have stopped. https://github.com/google-coral

    https://coral.ai/

Your problem statement still needs more clarification. If the above applies, the best way is to evaluate your ML-based pattern matcher on high-level scenarios. One approach to speed up the evaluation is to lift and shift the execution of scenarios into cloud. Another approach is to use an AI accelerator, such as http://coral.ai or other.

You probably could but not with raspis. Maybe the TPUs they sell. https://coral.ai/