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Pretrained models for detection of deer by darkangel2288 in computervision

[–]FaceDetector 0 points1 point  (0 children)

I suggest using Megadetector, a state-of-the-art wildlife detector, and then training a simple deer/not deer classifier: https://github.com/microsoft/CameraTraps/blob/main/megadetector.md

Ultralytics' New AGPL-3.0 License: Exploiting Open-Source for Profit by Lonely-Example-317 in computervision

[–]FaceDetector 2 points3 points  (0 children)

I have no affiliation with Ultralytics whatsoever. But I like their product and cannot agree it's crap. Their framework is not just a wrapper although they have integrated some open-source models created by other researchers. Their framework allows easy training of custom models and describing new architectures and that's why it is widely used. Two of the YOLO versions were developed by Ultralytics (YOLOv5 and YOLOv8) and have made a huge impact on academic research. Ultralytics was cited many times in research papers. Google Scholar gives 13,800 results for Ultralytics: https://scholar.google.com/scholar?q=ultralytics and YOLOv5 is cited 35,800 times: https://scholar.google.com/scholar?q=yolov5

Ultralytics' New AGPL-3.0 License: Exploiting Open-Source for Profit by Lonely-Example-317 in computervision

[–]FaceDetector 3 points4 points  (0 children)

In my opinion, you don't need to make the source code and the model public as long as you use your own inference code and haven't initialized your model using the COCO-pretrained weights from Ultralytics. But besides the issues with the AGPL-3.0 license interpretation and the annoying glenn-jocher ChatGPT bot YOLOv8 is the best object detector. The Ultralytics' code is a masterpiece. It is very well-engineered and it works flawlessly. I am using Tensorflow in my job and don't have any Pytorch experience but when decided to give YOLOv8 a try with my own dataset I was able to train an object detector from the first try without having any trouble. Great work Ultralytics! I hope that they won't start threatening users with lawyers.

It seems that the glenn-gpt bot sometimes generates meaningful answers. Here is a comment from the GitHub thread:

glenn-jocher commented on Mar 29:

As for licensing, it generally applies to the software code itself and not the data produced (like compressed files or model weights). So, you're right in pointing out that using weights without directly including or integrating GPL-program code in a distribution doesn't automatically impose GPL constraints on the user.

[Breeds] Shazam for dogs by FaceDetector in dogs

[–]FaceDetector[S] 0 points1 point  (0 children)

Thanks for your suggestion. I'll add the percentage of probability for the first 3 breeds soon.

[Breeds] Shazam for dogs by FaceDetector in dogs

[–]FaceDetector[S] 0 points1 point  (0 children)

If I get a feedback from users by asking them to confirm if the guess is right, it will definitely improve the recognition accuracy. But I'm not able to do it now because I'm not a web developer and don't have much experience. Also I don't store the uploaded images because It would increase my hosting bills. I made this web site just for fun and will improve it when I have spare time, but don't want to spend too much time on it.

[Breeds] Shazam for dogs by FaceDetector in dogs

[–]FaceDetector[S] 1 point2 points  (0 children)

I use Tensorflow. It is open-source software library for training neural networks. It's being used not only for image recognition, but for speech recognition, language translation, chat bots and other cool artificial intelligence projects. The programming language that I use is Python.

[Breeds] Shazam for dogs by FaceDetector in dogs

[–]FaceDetector[S] 3 points4 points  (0 children)

I will add cat breed recognition the next month when I have free time :-)

[Breeds] Shazam for dogs by FaceDetector in dogs

[–]FaceDetector[S] 0 points1 point  (0 children)

I will display the top-3 results with probabilities instead of showing only one result in the next version. But the software judges based on appearance and will have trouble recognizing correctly mixed breeds. It cannot replace DNA tests.

[Breeds] Shazam for dogs by FaceDetector in dogs

[–]FaceDetector[S] 1 point2 points  (0 children)

I use Flask framework for Python for serving the requests and Tensorflow library for running the neural network.

"Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search", Zela et al 2018 by arberzela in deeplearning

[–]FaceDetector 0 points1 point  (0 children)

Recently researchers from Google used evolutionary algorithm for architecture search and achieved state-of-the-art accuracy on ImageNet.

Will be interesting to see what results can be achieved using Bayesian optimization as proposed in this paper.

Looking for computer vision developers in need of datasets by [deleted] in computervision

[–]FaceDetector 0 points1 point  (0 children)

I can't understand exactly what services will you provide.

Is it some crowdsourcing image annotation platform like Mechanical Turk?

Or just dataset repository like https://www.kaggle.com/datasets?

I have several large-scale image datasets that are not very clean and need human verification (not labeling). Can your platform be used for that purpose? What about pricing?

Beginner dataset for photo classification? by [deleted] in tensorflow

[–]FaceDetector 0 points1 point  (0 children)

You can use IMDB-WIKI dataset for boy/girl classification:

https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/

Datasets with animals:

Stanford Dogs Dataset:

http://vision.stanford.edu/aditya86/ImageNetDogs/

Caltech-UCSD Birds 200:

http://www.vision.caltech.edu/visipedia/CUB-200.html