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AViLaMa

Zero-Shot Image Classification
Transformers
PyTorch
Safetensors
vision-text-dual-encoder
image generation
visual qa
text-image embedding
image-text embedding
sartify
visual conversional ai
image semantic retrival
african raw resourced languages
Inference Endpoints
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metadata
language:
  - multilingual
  - en
  - sw
  - ha
  - yo
  - ig
  - zu
  - sn
  - ar
  - am
  - fr
  - pt
tags:
  - zero-shot-image-classification
  - image generation
  - visual qa
  - text-image embedding
  - image-text embedding
  - pytorch
  - sartify
  - visual conversional ai
  - image semantic retrival
  - african raw resourced languages
  - safetensors
  - clip
license: apache-2.0
library_name: transformers

AViLaMa : African Vision-Languages Aligment Pre-Training Model.

Learning Visual Concepts Directly From African Languages Supervision. Paper is coming

Model Details

AViLaMa is the large open-source text-vision alignment pre-training model in African languages. It brings a way to learn visual concepts directly from African languages supervision. Inspired from OpenAI CLIP, but with more modalities like video, audio, etc.. and other techniques like agnostic languages encoding, data filtering network. All for more than 12 African languages, trained on the #AViLaDa-2B datasets of filtered image, video, audio-text pairs. We are also working to make it usable in directly vision-vision tasks.

  • Developed by : Sartify LLC (www.sartify.com)
  • Authors : Innocent Charles, Zephania Reuben
  • Funded by : Sartify LLC, Open Source Community, etc..(We always welcome other donors)
  • Model type : multilingual & multimodality transformer
  • Language(s) : en (English), sw (Swahili), ha (Hausa), yo (Yoruba), ig (Igbo), zu (Zulu), sn (Shona), ar (Arabic), am (Amharic), fr (French), pt (Portuguese)
  • License: apache 2.0

Load model from hugging face. 

import torch
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("sartifyllc/AViLaMa")
tokenizer = AutoTokenizer.from_pretrained("sartifyllc/AViLaMa")
model = model.eval()

Model Sources

  • Repository : AViLaMa-Sources
  • Datasets : Coming...
  • Paper : Coming...
  • Demo : Coming...

Direct & Downstream Use:

  1. zero shot semantic image retrieval and ranking tasks.
  2. zero shot semantic audio retrieval and ranking tasks.
  3. zero shot semantic video retrieval and ranking tasks.
  4. zero shot image classification tasks.
  5. Zero shot video classification tasks.
  6. Zero shot audio classification tasks.
  7. visual QA tasks.
  8. visual conversional GenAI tasks.
  9. image and video capturing tasks.
  10. images and art generation guiding and conditioning tasks.
  11. text-images analysis tasks.
  12. content moderation task.

Citation

BibTeX:

AViLaMa paper
@article{sartifyllc2023africanvision,
  title={AViLaMa: Learning Visual Concepts Directly From African Languages Supervision},
  author={Innocent Charles and
          Zephania Reuben},  
  journal={To be inserted},
  year={2023}
}