models-moved

🚀 Singular Values-Driven Automated Filter Pruning Release

🎉 We are thrilled to announce the first official release of our project, featuring a collection of baseline and compressed checkpoints to support efficient network compression.

✔️ Available Checkpoints:

• VGG-16-BN/CIFAR-10
• ResNet-56/110/CIFAR-10
• DenseNet-40/CIFAR-10
• GoogleNet/CIFAR-10
• VGG-16-BN/CIFAR-100
• ResNet-20/56/110/CIFAR-100
• ResNet-50/Imagenet
• MobileNetv2/Imagenet
• Faster/Mask/KeypointRCNNResNet50FPN/COCO-2017

📝 Usage:

To get started with these checkpoints, simply refer to the documentation. Each checkpoint must be loaded with its corresponding pruning ratio.

💬 Feedback and Contributions:

We value your feedback and contributions to this project. If you encounter any issues, have suggestions for improvements, or would like to contribute models, please don't hesitate to open an issue or submit a pull request on our GitHub repository.

🔮 Future Updates:

We are dedicated to the continuous improvement and expansion of our model collection. Keep an eye on our repository for future updates, as we plan to add more models and fine-tuned variations based on community feedback and demands.

Once again, thank you for your interest in our project. We hope these checkpoints empower you to accelerate your projects and research in the domain of network compression.

Happy coding and exploring the world of efficient deep learning!

🇫🇷 The SLIMING Team