dnth | 2 years ago | on: Cluster images at scale using DINOv2 embeddings
We released a free tool (fastdup) where you can use to cluster image using DINOv2 embeddings.
dnth's comments
dnth | 3 years ago | on: Find image duplicates and outliers – A free, scalable, efficient tool
fastdup is a tool that let you gain insights from a large image/video collection.
It lets you identify image duplicates, video duplicates, wrong labels, outliers, corrupted data, and image clusters.
fastdup is -
Unsupervised: fits any visual dataset. Scalable: handles 400M images on a single machine. Efficient: works on CPU (even on Google Colab with only 2 CPU cores!). Low Cost: can process 12M images on a $1 cloud machine budget.
dnth | 3 years ago | on: Dedup-ing LAION (60M duplicates) and ImageNet (1.2M duplicates) with fastdup
The authors at fastdup ran an analysis on LAION 400M and Imagenet21K. Here's what they found.
LAION 400M
> 60M duplicates. > 962K broken images. > Various label discrepancies.
ImageNet21K
> 1.2M duplicate images. > 104K train/val leak.
fastdup GitHub repo - https://github.com/visual-layer/fastdup
dnth | 3 years ago
Even pros have dark, blurry & duplicate shots. But disorganization can make it hard to find those special memories. Let's fix that.
dnth | 3 years ago | on: PyTorch at the Edge: Deploy 964 TIMM Models on Android with TorchScript
Update - After some code optimization, I got the inference time down to below 100ms. The lowest I got on my Pixel 3 XL is 37ms!
https://dicksonneoh.com/portfolio/pytorch_at_the_edge_timm_t...
dnth | 3 years ago | on: PyTorch at the Edge: Deploy 964 TIMM Models on Android with TorchScript
You're welcome! I'm not sure if I'm the right person to advise on this. But this idea is also known as federated learning right?
dnth | 3 years ago | on: PyTorch at the Edge: Deploy 964 TIMM Models on Android with TorchScript
With flutter you can also build web apps.
dnth | 3 years ago | on: PyTorch at the Edge: Deploy 964 TIMM Models on Android with TorchScript
Note yet! But I heard things are going to be a lot faster in 2.0. Have you tried?
dnth | 3 years ago | on: PyTorch at the Edge: Deploy 964 TIMM Models on Android with TorchScript
Thank you for the feedback! Let me know if you have questions :)
dnth | 3 years ago | on: PyTorch at the Edge: Deploy 964 TIMM Models on Android with TorchScript
Thank you!
dnth | 3 years ago | on: PyTorch at the Edge: Deploy 964 TIMM Models on Android with TorchScript
Thank you!!!
dnth | 3 years ago | on: PyTorch at the Edge: Deploy 964 TIMM Models on Android with TorchScript
Thanks a lot there! I was hesitating to write this piece actually, thinking it's not going to be valuable. I'm glad you find value in them!
dnth | 3 years ago | on: PyTorch at the Edge: Deploy 964 TIMM Models on Android with TorchScript
Media pipe looks really cool. I havent tried it. Have you?
dnth | 3 years ago | on: PyTorch at the Edge: Deploy 964 TIMM Models on Android with TorchScript
The pytorch lite package also supports yolov5 models.
I posted on my LinkedIn awhile ago
https://www.linkedin.com/posts/dickson-neoh_deploying-object...
dnth | 3 years ago | on: PyTorch at the Edge: Deploy 964 TIMM Models on Android with TorchScript
Model deployment is painful. Running a model on a mobile phone?
Forget it .
The frustration is real. I remember spending nights exporting models into ONNX and it still failed me. Deploying models on mobile for edge inference used to be complex.
Not anymore.
In this post, I’m going to show you how you can pick from over 900+ SOTA models on TIMM, train them using best practices with Fastai, and deploy them on Android using Flutter.
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