yolov7 VS hivemind

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

https://github.com/learning-at-home/hivemind is also relevant

I really hope you'll join me, for the Petals support, at least! A single docker-compose.yml file is all we need, for now. If we are able to find enough people willing to host some smaller models, perhaps we could expand into the Hivemind, and create our own, custom foundation model one day?

Decentralized deep learning: https://github.com/learning-at-home/hivemind

https://github.com/learning-at-home/hivemind - same people behind it, was made before petals I think.

not if we rise to meet that challenge. here's a few tools that facilitate AI research in the face of an advanced persistent threat: Hivemind- a distributed Pytorch framework

yeah, there's Hivemind. and there's research wrt how to chunk out training workload so it can be scaled up. not sure why there's commentary that latency issues would limit this sort of enterprise, the architecture typically isn't designed for liveness. other subfields of distributed training/inference include zero-knowledge machine learning. besides all of that, there's also adversarial computation like SafetyNets and refereed delegation of computation.

We already have the software for it. There are some projects, but the one I'm most familiar with is https://github.com/learning-at-home/hivemind for training and it's sister project https://petals.ml/ for running large models distributed.

I'm not entirely how the approach they're using works [0], but I study federated learning and one of the highly-cited survey papers has several chapters (5 and 6 in particular) addressing potential attacks, failure modes, and bias [1].

0: https://github.com/learning-at-home/hivemind

1: https://arxiv.org/abs/1912.04977

The Hivemind project is just that

https://github.com/learning-at-home/hivemind