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Top 23 Python Imagenet Projects
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pytorch-image-models
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
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Swin-Transformer
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
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Scout Monitoring
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Efficient-AI-Backbones
Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.
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InfluxDB
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MEAL-V2
MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks. In NeurIPS 2020 workshop.
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datumaro
Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage Computer Vision datasets.
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assembled-cnn
Tensorflow implementation of "Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network"
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ModelZoo.pytorch
Hands on Imagenet training. Unofficial ModelZoo project on Pytorch. MobileNetV3 Top1 75.64๐ GhostNet1.3x 75.78๐
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biprop
Identify a binary weight or binary weight and activation subnetwork within a randomly initialized network by only pruning and binarizing the network.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Project mention: Samsung expected to report 80% profit plunge as losses mount at chip business | news.ycombinator.com | 2023-10-10> there is really nothing that "normal" AI requires that is bound to CUDA. pyTorch and Tensorflow are backend agnostic (ideally...).
There are a lot of optimizations that CUDA has that are nowhere near supported in other software or even hardware. Custom cuda kernels also aren't as rare as one might think, they will often just be hidden unless you're looking at libraries. Our more well known example is going to be StyleGAN[0] but it isn't uncommon to see elsewhere, even in research code. Swin even has a cuda kernel[1]. Or find torch here[1] (which github reports that 4% of the code is cuda (and 42% C++ and 2% C)). These things are everywhere. I don't think pytorch and tensorflow could ever be agnostic, there will always be a difference just because you have to spend resources differently (developing kernels is time resource). We can draw evidence by looking at Intel MKL, which is still better than open source libraries and has been so for a long time.
I really do want AMD to compete in this space. I'd even love a third player like Intel. We really do need competition here, but it would be naive to think that there's going to be a quick catchup here. AMD has a lot of work to do and posting a few bounties and starting a company (idk, called "micro grad"?) isn't going to solve the problem anytime soon.
And fwiw, I'm willing to bet that most AI companies would rather run in house servers than from cloud service providers. The truth is that right now just publishing is extremely correlated to compute infrastructure (doesn't need to be but with all the noise we've just said "fuck the poor" because rejecting is easy) and anyone building products has costly infrastructure.
[0] https://github.com/NVlabs/stylegan2-ada-pytorch/blob/d72cc7d...
[1] https://github.com/microsoft/Swin-Transformer/blob/2cb103f2d...
[2] https://github.com/pytorch/pytorch/tree/main/aten/src
Project mention: Instance segmentation of small objects in grainy drone imagery | /r/computervision | 2023-12-09Also, Iโd suggest considering switching to the segmentation-models library - it provides U-Net models with a variety of pretrained backbones of as encoders. The author also put out a PyTorch version. https://github.com/qubvel/segmentation_models.pytorch https://github.com/qubvel/segmentation_models
Examples of lightweight models include MobileNet, a computer vision model designed for mobile and embedded vision applications, EfficientDet, an object detection model, and EfficientNet, a CNN that uses compound scaling to enable better performance. All these are lightweight models from Google.
Project mention: Apple Researchers Introduce ByteFormer: An AI Model That Consumes Only Bytes And Does Not Explicitly Model The Input Modality - MarkTechPost | /r/singularity | 2023-06-16https://github.com/apple/ml-cvnets/tree/main/examples/byteformer - Where the code will be located once uploaded
Project mention: A look at Appleโs new Transformer-powered predictive text model | news.ycombinator.com | 2023-09-16I'm pretty fatigued on constantly providing references and sources in this thread but an example of what they've made availably publicly:
https://github.com/snap-research/EfficientFormer
Project mention: Help Needed: Converting PlantNet-300k Pretrained Model Weights from Tar to h5 Format Help | /r/learnpython | 2023-06-09It's almost certainly a pickled pytorch model so you will first need to load it using pytorch and then write it out to h5 (legacy keras format) with https://github.com/gmalivenko/pytorch2keras.
Python Imagenet related posts
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labelImg: NEW Data - star count:20925.0
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labelImg: NEW Data - star count:20925.0
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labelImg: NEW Data - star count:20925.0
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labelImg: NEW Data - star count:20925.0
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labelImg: NEW Data - star count:20925.0
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labelImg: NEW Data - star count:20925.0
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labelImg: NEW Data - star count:20925.0
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A note from our sponsor - InfluxDB
www.influxdata.com | 3 Jun 2024
Index
What are some of the best open-source Imagenet projects in Python? This list will help you:
Project | Stars | |
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1 | pytorch-image-models | 30,220 |
2 | Swin-Transformer | 13,126 |
3 | segmentation_models.pytorch | 8,951 |
4 | EfficientNet-PyTorch | 7,715 |
5 | TensorLayer | 7,296 |
6 | Efficient-AI-Backbones | 3,862 |
7 | efficientnet | 2,067 |
8 | ml-cvnets | 1,702 |
9 | EfficientFormer | 957 |
10 | pytorch2keras | 846 |
11 | MEAL-V2 | 684 |
12 | natural-adv-examples | 576 |
13 | CvT | 518 |
14 | datumaro | 492 |
15 | GCVit | 415 |
16 | assembled-cnn | 330 |
17 | PyTorch-Model-Compare | 316 |
18 | FQ-ViT | 263 |
19 | dytox | 132 |
20 | CeiT | 95 |
21 | ModelZoo.pytorch | 49 |
22 | biprop | 46 |
23 | BSN | 40 |
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