QLIP-I: Image Embedding Model
QLIP-I is a vision transformer-based model that generates normalized 512-dimensional embeddings for images. The model uses a patch-based approach to encode visual information into a compact representation suitable for similarity search, retrieval, and downstream tasks.
Install dependencies
pip install torch torchvision pillow numpy
Usage
Single Image Encoding
from PIL import Image
import QLIPI
# Load and encode an image
image = Image.open("path/to/image.jpg")
embedding = QLIPI.encode(image)
# The embedding is a 512-dimensional normalized tensor
print(embedding.shape) # torch.Size([512])
Batch Encoding
import QLIPI
from PIL import Image
# Encode multiple images at once
images = [
"image1.jpg",
"image2.jpg",
Image.open("image3.jpg")
]
embeddings = QLIPI.encode_batch(images)
# Returns a batch of embeddings
print(embeddings.shape) # torch.Size([3, 512])
Computing Similarity
import torch
import QLIPI
from PIL import Image
# Encode two images
img1 = Image.open("cat.jpg")
img2 = Image.open("dog.jpg")
emb1 = QLIPI.encode(img1)
emb2 = QLIPI.encode(img2)
# Compute cosine similarity (embeddings are already normalized)
similarity = torch.dot(emb1, emb2)
print(f"Similarity: {similarity.item():.4f}")
Save and Load Embeddings
import torch
import QLIPI
from PIL import Image
# Encode and save
image = Image.open("image.jpg")
embedding = QLIPI.encode(image)
torch.save(embedding, "embedding.pt")
# Load embedding
loaded_embedding = torch.load("embedding.pt")
Model Information
import QLIPI
# Get device being used
print(QLIPI.device()) # cuda or cpu
# Get model path
print(QLIPI.model_path()) # Path to qlip-img.pt
# Get embedding dimensions
print(QLIPI.embedding_dim()) # 512
# Get number of parameters
print(QLIPI.model_parameters()) # Total trainable parameters
Input Specifications
- Format: RGB images (automatically converted from other formats)
- Preprocessing: Images are resized to 224×224 using bicubic interpolation with center cropping
- Normalization: ImageNet statistics
- Mean: [0.485, 0.456, 0.406]
- Std: [0.229, 0.224, 0.225]
The model automatically uses CUDA if available and falls back to CPU otherwise.
Use Cases
- Image Similarity Search: Find similar images using cosine similarity
- Image Retrieval: Build reverse image search systems
- Clustering: Group similar images together
- Classification: Use embeddings as features for downstream classifiers
- Content-Based Recommendation: Recommend visually similar content
- Duplicate Detection: Identify near-duplicate images
Limitations
- Images are resized to 224×224, which may lose detail in high-resolution images
- Model is trained for general visual understanding; domain-specific fine-tuning may improve performance on specialized datasets
- Embeddings are fixed at 512 dimensions
License
MIT Licenses