OFA
UPop
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| OFA | UPop | |
|---|---|---|
| 3 | 1 | |
| 2,323 | 83 | |
| 2.4% | - | |
| 2.8 | 8.4 | |
| 4 days ago | 6 months ago | |
| Python | Python | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
OFA
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[R][P] Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework + VQA Hugging Face Spaces Demo
github: https://github.com/OFA-Sys/OFA
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OFA: model that does text-to-image as well as other tasks
From this:
- [R] Paper: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework. Shocking performance in text-to-image synthesis and open-domain tasks.
UPop
What are some alternatives?
ImageNet21K - Official Pytorch Implementation of: "ImageNet-21K Pretraining for the Masses"(NeurIPS, 2021) paper
Torch-Pruning - [CVPR 2023] Towards Any Structural Pruning; LLMs / SAM / Diffusion / Transformers / YOLOv8 / CNNs
GroundingDINO - Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
image-captioning - Image captioning using python and BLIP
ONE-PEACE - A general representation model across vision, audio, language modalities. Paper: ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
MAGIC - Language Models Can See: Plugging Visual Controls in Text Generation
sparseml - Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
neural-compressor - SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime
model-optimization - A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
Coin-CLIP - Coin-CLIP: fine-tuned with a vast collection of coin images from CLIP using contrastive learning. It enhances feature extraction for coins, boosting image search accuracy. This model merges Visual Transformer (ViT) with CLIP's multimodal learning, optimized for numismatic applications.