CoBSAT

ML Model Benchmarker

Provides a benchmarking framework and dataset for evaluating the performance of large language models in text-to-image tasks

Implementation and dataset for paper "Can MLLMs Perform Text-to-Image In-Context Learning?"

GitHub

28 stars
1 watching
1 forks
Language: Jupyter Notebook
last commit: 14 days ago

Related projects:

Repository Description Stars
ys-zong/vl-icl A benchmarking suite for multimodal in-context learning models 28
multimodal-art-projection/omnibench Evaluates and benchmarks multimodal language models' ability to process visual, acoustic, and textual inputs simultaneously. 14
mshukor/evalign-icl Evaluating and improving large multimodal models through in-context learning 20
junyangwang0410/amber An LLM-free benchmark suite for evaluating MLLMs' hallucination capabilities in various tasks and dimensions 93
freedomintelligence/mllm-bench Evaluates and compares the performance of multimodal large language models on various tasks 55
ml-tooling/ml-workspace An all-in-one web-based IDE for machine learning and data science 3,434
ailab-cvc/seed-bench A benchmark for evaluating large language models' ability to process multimodal input 315
marklogic/ml-gradle Automates tasks involving MarkLogic using Gradle 73
trekhleb/machine-learning-experiments An interactive platform for exploring and comparing various machine learning algorithms and techniques using visualizations and example code. 1,654
damo-nlp-sg/m3exam A benchmark for evaluating large language models in multiple languages and formats 92
mmaul/clml A high-performance statistical machine learning library written in Common Lisp 261
mmaul/clml.tutorials Tutorials and resources for learning Common Lisp Machine Learning with CLML. 31
ardanlabs/training-ai Provides training materials and tools for building machine learning applications 72
isekai-portal/link-context-learning An implementation of a multimodal learning approach to improve language models' ability to recognize unseen images and understand novel concepts. 89
kei500/liblinear-ruby Provides an interface to train and predict with machine learning models using LIBLINEAR 83