Search Results for "input-output model"
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Showing 9 open source projects for "input-output model"
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1
SHAP
A game theoretic approach to explain the output of ml models
...To understand how a single feature effects the output of the model we can plot the SHAP value of that feature vs. the value of the feature for all the examples in a dataset. Since SHAP values represent a feature's responsibility for a change in the model output, the plot below represents the change in predicted house price as RM (the average number of rooms per house in an area) changes. -
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DeepSeed
Deep learning optimization library making distributed training easy
...Sparse attention of DeepSpeed powers an order-of-magnitude longer input sequence and obtains up to 6x faster execution comparing with dense transformers. -
3
tsai
Time series Timeseries Deep Learning Machine Learning Pytorch fastai
...If you require any of the dependencies that is not installed, tsai will ask you to install it when necessary) We've also added a new PredictionDynamics callback that will display the predictions during training. This is the type of output you would get in a classification task. New tutorial notebook on how to train your model with larger-than-memory datasets in less time achieving up to 100% GPU usage! See our new tutorial notebook on how to track your experiments with Weights & Biases -
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Raster Vision
Open source framework for deep learning satellite and aerial imagery
...Raster Vision allows engineers to quickly and repeatably configure pipelines that go through core components of a machine learning workflow: analyzing training data, creating training chips, training models, creating predictions, evaluating models, and bundling the model files and configuration for easy deployment. The input to a Raster Vision pipeline is a set of images and training data, optionally with Areas of Interest (AOIs) that describe where the images are labeled. The output of a Raster Vision pipeline is a model bundle that allows you to easily utilize models in various deployment scenarios. -
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5
Make-A-Video - Pytorch (wip)
Implementation of Make-A-Video, new SOTA text to video generator
Implementation of Make-A-Video, new SOTA text to video generator from Meta AI, in Pytorch. They combine pseudo-3d convolutions (axial convolutions) and temporal attention and show much better temporal fusion. The pseudo-3d convolutions isn't a new concept. It has been explored before in other contexts, say for protein contact prediction as "dimensional hybrid residual networks". The gist of the paper comes down to, take a SOTA text-to-image model (here they use DALL-E2, but the same learning... -
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pytorch-fcn
PyTorch Implementation of Fully Convolutional Networks
...It reproduces the influential FCN approach that converts classification networks into dense, pixel-level predictors. The package includes FCN32s, FCN16s, FCN8s, and FCN8s-at-once variants with progressively finer output reconstruction. Training code and a PASCAL VOC example are provided so users can reproduce baseline experiments. The repository reports mean intersection-over-union results alongside the original implementation for comparison. Pretrained model links and validation visualizations support evaluation and demonstrations. It can be installed from source or through the torchfcn package and is primarily useful for research, education, and legacy segmentation baselines. -
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Consistent Depth
We estimate dense, flicker-free, geometrically consistent depth
...The system builds upon traditional structure-from-motion (SfM) techniques to provide geometric constraints while integrating a convolutional neural network trained for single-image depth estimation. During inference, the model fine-tunes itself to align with the geometric constraints of a specific input video, ensuring stable and realistic depth maps even in less-constrained regions. This approach achieves improved geometric consistency and visual stability compared to prior monocular reconstruction methods. The project can process challenging hand-held video footage, including those with moderate dynamic motion, making it practical for real-world usage. -
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CRP - Chemical Reaction Prediction
Predicting Organic Reactions using Neural Networks.
The intend is to solve the forward-reaction prediction problem, where the reactants are known and the interest is in generating the reaction products using Deep learning. This Graphical User Interface takes simplified molecular-input line-entry system (SMILES) as an input and generates the product SMILE & molecule. Beam search is used in Version 2, to generate top 5 predictions. Maximum input length for the model is 15 (excluding spaces). -
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LearningToCompare_FSL
Learning to Compare: Relation Network for Few-Shot Learning
...It includes model definitions, data loading logic, episodic training loops, and scripts that implement the N-way K-shot evaluation protocol common in few-shot research. Researchers can use this codebase as a starting point to test new ideas, modify relation modules, or transfer the approach to new datasets. -
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