Model for "GeoLangBind", is still in development, not the final version
Model card for GeoLB-ViT-B-16-SigLIP
GeoLangBind: Unifying Earth Observation Modalities with Vision-Language Foundation Models
Details are coming soon.
Model Details
- Model Type: Contrastive Image-Text, Zero-Shot Image Classification.
- Dataset: Earth observation image-text datasets
- Papers:
- GeoLangBind: Unifying Earth Observation Modalities with Vision-Language Foundation Models
Model Usage
Install the geolb_open_clip package first: https://github.com/ShadowXZT/geolb_open_clip.git
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 # works on open-clip-torch>=2.23.0, timm>=0.9.8
model, preprocess = create_model_from_pretrained('hf-hub:XShadow/GeoLB-ViT-B-16-SigLIP')
tokenizer = get_tokenizer('hf-hub:XShadow/GeoLB-ViT-B-16-SigLIP')
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)
With timm
(for image embeddings)
from urllib.request import urlopen
from PIL import Image
import timm
image = Image.open(urlopen(
'https://huggingface.co./datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'dofa_vit_base_patch16_siglip_224',
pretrained=True,
num_classes=0,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(image).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
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