classification
solo-learn
| classification | solo-learn | |
|---|---|---|
| 1 | 3 | |
| 20 | 1,358 | |
| - | - | |
| 0.0 | 5.9 | |
| over 1 year ago | 5 days ago | |
| Python | Python | |
| MIT License | MIT License |
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classification
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PyTorch Ensemble model
I created a CNN for a classification problem using 2 different techniques: 1) conventional CNN and 2) contrastive learning (SimCLR framework). As a reference code, I attack the starting point of my coding: https://github.com/giakou4/MNIST_classification.
solo-learn
- [D]Use SimCLR as pretraining for image segmentation
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[D] State-of-the-Art for Self-Supervised (Pre-)Training of CNN architectures (e.g. ResNet)?
Hello, I lost a bit of touch to the current SOTA of self-supervised pretraining of CNNs, in particular ResNet. I found this repository https://github.com/vturrisi/solo-learn that has many methods implemented but I'm not really sure where to start. My goal is to pretrain a ResNet backbone on a decently large amount of image data that comes from a certain domain and after that fine-tune it for different downstream tasks (classification, segmentation, object detection) on a subset of the data I have labels for.
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[P] Solo-learn 1.0.3: new methods, support for transformer architectures, better evaluation, improved docs, and additional results.
Hi Reddit, the solo-learn team is back again with interesting news about its SSL library.
What are some alternatives?
disentangling-vae - Experiments for understanding disentanglement in VAE latent representations
dino - PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
convolution-vision-transformers - PyTorch Implementation of CvT: Introducing Convolutions to Vision Transformers
lightning-transformers - Flexible components pairing 🤗 Transformers with :zap: Pytorch Lightning
dnn_from_scratch - A high level deep learning library for Convolutional Neural Networks,GANs and more, made from scratch(numpy/cupy implementation).
contrastive-reconstruction - Tensorflow-keras implementation for Contrastive Reconstruction (ConRec) : a self-supervised learning algorithm that obtains image representations by jointly optimizing a contrastive and a self-reconstruction loss.
pytorch-sentiment-analysis - Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
Revisiting-Contrastive-SSL - Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]
Image-Forgery-Detection-CNN - Image forgery detection using convolutional neural networks. Group 10's final project for TU Delft's course CS4180 Deep Learning 2019.
Unsupervised-Semantic-Segmentation - Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals. [ICCV 2021]
CEBRA - Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA
vectorrvnn - Data Driven method for hierarchical grouping of paths in Vector Graphics.