imagecode
by McGill-NLP
Code and data for ImageCoDe, a contextual vison-and-language benchmark
AI summary
Image retrieval benchmark
Provides code and data for an image retrieval benchmark that tests contextual understanding of text descriptions with images.
- stars
- 39
- forks
- 6
- watching
- 4
Similar projects
Found by comparing what the projects do, not just their names.
Image retrieval library
This project provides tools and techniques for image retrieval based on visual features.
Image dataset quality check
Automatically detects issues in image datasets to improve computer vision models
Image processing examples
A collection of MATLAB code examples for various digital image processing tasks.
Image retrieval framework
This MATLAB toolbox trains and tests convolutional neural networks for image retrieval tasks, including fine-tuning and supervised whitening.
Image processor
An image processing library with low memory needs
Image classifier
An image classification model using the ResNet-50 architecture, trained on the ImageNet dataset.
Image classifier
An image classification model using a third-generation deep residual network.
Multimodal LLM test suite
A benchmark for evaluating large language models' ability to process multimodal input
LM Benchmark
A benchmark for evaluating large language models in multiple languages and formats
ML benchmark
A benchmark for evaluating machine learning algorithms' performance on large datasets
ML image recognition model
An implementation of a multimodal learning approach to improve language models' ability to recognize unseen images and understand novel concepts.
Image processing library
A library of fast computer vision algorithms implemented in C++ for speed, operating over numpy arrays.
Model benchmarking suite
Measures the performance of deep learning models in various deployment scenarios.
Few-shot learner
This repository provides code for a few-shot transfer learning approach to personalized and federated image classification
Visual Model Benchmark
An open-source benchmarking framework for evaluating cross-style visual capability of large multimodal models