Pinned Repositories
diffusion_segmentation
Use denoising diffusion model to segment the objects on the image step by step.
dml_segmentation
eth_price_prediction_with_sinewaves
few_shot_sam
FEWSAM Few-shot Segmentation tool based on Segment Anything
filter_distinguisher
A CNN Model that creates filters bu unsupervised learning. The model tries to distinguish the output of each convolution operation and tries to create filters that generate most various output layers.
self_supervised_learning_linear_projection
A Self Supervised Learning Method using Linear Projection
semantic_segmentation
solo-learn
solo-learn: a library of self-supervised methods for visual representation learning powered by Pytorch Lightning
Spelling-Error-Correction-Censor-Bad-Words-Using-Prime-Numbers
vectorsum_vectordifference
A Self-supervised learning algorithm that uses summation and difference of embedding vectors that are generated from partial and full image.
rootvisionai's Repositories
rootvisionai/few_shot_sam
FEWSAM Few-shot Segmentation tool based on Segment Anything
rootvisionai/filter_distinguisher
A CNN Model that creates filters bu unsupervised learning. The model tries to distinguish the output of each convolution operation and tries to create filters that generate most various output layers.
rootvisionai/semantic_segmentation
rootvisionai/vectorsum_vectordifference
A Self-supervised learning algorithm that uses summation and difference of embedding vectors that are generated from partial and full image.
rootvisionai/diffusion_segmentation
Use denoising diffusion model to segment the objects on the image step by step.
rootvisionai/dml_segmentation
rootvisionai/eth_price_prediction_with_sinewaves
rootvisionai/self_supervised_learning_linear_projection
A Self Supervised Learning Method using Linear Projection
rootvisionai/solo-learn
solo-learn: a library of self-supervised methods for visual representation learning powered by Pytorch Lightning
rootvisionai/Spelling-Error-Correction-Censor-Bad-Words-Using-Prime-Numbers
rootvisionai/clip_for_dml
CLIP Feature Extractor and Linear Projection Loss for Deep Metric Learning
rootvisionai/Currency-Prediction-by-Probability-Patterns
rootvisionai/dinov2
PyTorch code and models for the DINOv2 self-supervised learning method.
rootvisionai/guided_segmentation
One way Few-shot Segmentation
rootvisionai/image_to_image_syntetic_data_generator
A synthetic data generator using stable diffusion, it works with image inputs instead of text input
rootvisionai/mpnn_on_imagenet
Multi-Perspective Neural Networks(MPNN) applied on ImageNet dataset. MPNN is a unsupervised learning algorithm.
rootvisionai/ObjectDetectionSilverBullet
Clean and basic implementation of retinanet object detection.
rootvisionai/Objectron
Objectron is a dataset of short, object-centric video clips. In addition, the videos also contain AR session metadata including camera poses, sparse point-clouds and planes. In each video, the camera moves around and above the object and captures it from different views. Each object is annotated with a 3D bounding box. The 3D bounding box describes the object’s position, orientation, and dimensions. The dataset contains about 15K annotated video clips and 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes
rootvisionai/qsnn
Entangled state prediction with respect to time
rootvisionai/rgb2spherical
rootvisionai/rootvision
rootvisionai/segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
rootvisionai/sinusoidal_neural_networks
Feed-forward neural network model using w_oxsin(w_ixpi) instead of using w*x+b as basis function.
rootvisionai/sinusoidal_neural_networks_experiments_4_1
Experiments which are conducted in Section 4.1 in master thesis "Development of Deep Neural Networks that learns faster"
rootvisionai/sinusoidal_neural_networks_experiments_5_4
Experiments which are conducted in Section 5.4 in master thesis "Development of Deep Neural Networks that learns faster"