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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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AViLaMa / README.md
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metadata
language:
  - multilingual
  - en
  - sw
  - ha
  - yo
  - ig
  - zu
  - sn
  - ar
  - am
  - fr
  - pt
pipeline_tag: zero-shot-image-classification
tags:
  - openCLIP
  - clip
  - vision transformer
  - pytorch
  - text-image embedding
  - AvilaBSE
  - sartify
license: apache-2.0

A CLIP (Contrastive Language-Image Pre-training) model trained on DFN-5B. Data Filtering Networks (DFNs) are small networks used to automatically filter large pools of uncurated data. This model was trained on 5B images that were filtered from a pool of 43B uncurated image-text pairs (12.8B image-text pairs from CommonPool-12.8B + 30B additional public image-text pairs).

This model has been converted to PyTorch from the original JAX checkpoints from Axlearn (https://github.com/apple/axlearn). These weights are directly usable in OpenCLIP (image + text).

Model Details

  • Model Type: Contrastive Image-Text, Zero-Shot Image Classification.
  • Dataset: DFN-5b
  • Papers:
  • Samples Seen: 39B (224 x 224) + 5B (384 x 384)

Model Metrics

dataset metric
ImageNet 1k 0.84218
Caltech-101 0.954479
CIFAR-10 0.9879
CIFAR-100 0.9041
CLEVR Counts 0.362467
CLEVR Distance 0.206067
Country211 0.37673
Describable Textures 0.71383
EuroSAT 0.608333
FGVC Aircraft 0.719938
Food-101 0.963129
GTSRB 0.679018
ImageNet Sketch 0.73338
ImageNet v2 0.7837
ImageNet-A 0.7992
ImageNet-O 0.3785
ImageNet-R 0.937633
KITTI Vehicle Distance 0.38256
MNIST 0.8372
ObjectNet 1 0.796867
Oxford Flowers-102 0.896834
Oxford-IIIT Pet 0.966841
Pascal VOC 2007 0.826255
PatchCamelyon 0.695953
Rendered SST2 0.566722
RESISC45 0.755079
Stanford Cars 0.959955
STL-10 0.991125
SUN397 0.772799
SVHN 0.671251
Flickr 0.8808
MSCOCO 0.636889
WinoGAViL 0.571813
iWildCam 0.224911
Camelyon17 0.711536
FMoW 0.209024
Dollar Street 0.71729
GeoDE 0.935699
Average 0.709421

[1]: Center-crop pre-processing used for ObjectNet (squashing results in lower accuracy of 0.737)

Model Usage

With OpenCLIP 

import torch
import torch.nn.functional as F
from urllib.request import urlopen
from PIL import Image
from open_clip import create_model_from_pretrained, get_tokenizer 

model, preprocess = create_model_from_pretrained('hf-hub:apple/DFN5B-CLIP-ViT-H-14-384')
tokenizer = get_tokenizer('ViT-H-14')

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)
    image_features = F.normalize(image_features, dim=-1)
    text_features = F.normalize(text_features, dim=-1)

    text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias)

zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

Citation 

@article{fang2023data,
  title={Data Filtering Networks},
  author={Fang, Alex and Jose, Albin Madappally and Jain, Amit and Schmidt, Ludwig and Toshev, Alexander and Shankar, Vaishaal},
  journal={arXiv preprint arXiv:2309.17425},
  year={2023}
}