BLIP

Pros of CLIP

• Broader applicability across various vision-language tasks
• Larger pre-training dataset (400M image-text pairs)
• Zero-shot capabilities for image classification

Cons of CLIP

• Less specialized for image captioning and visual question answering
• May require more computational resources for fine-tuning
• Limited support for text generation tasks

Code Comparison

CLIP usage:

import torch
from PIL import Image
from clip import clip

model, preprocess = clip.load("ViT-B/32", device="cuda")
image = preprocess(Image.open("image.jpg")).unsqueeze(0).to("cuda")
text = clip.tokenize(["a dog", "a cat"]).to("cuda")

with torch.no_grad():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)

BLIP usage:

from PIL import Image
import requests
from transformers import BlipProcessor, BlipForConditionalGeneration

processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")

img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')

inputs = processor(raw_image, return_tensors="pt")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))

Pros of Vision Transformer

• Focused specifically on vision tasks, potentially offering better performance for image-related applications
• Implements the original Vision Transformer (ViT) architecture, providing a foundation for further research and development
• Backed by Google Research, potentially benefiting from extensive resources and expertise

Cons of Vision Transformer

• Limited to vision tasks, whereas BLIP offers multimodal capabilities (vision and language)
• May require more computational resources for training and inference compared to BLIP's efficient design
• Less flexibility in terms of downstream tasks and fine-tuning options

Code Comparison

BLIP example:

from models.blip import blip_decoder
model = blip_decoder(pretrained='model_base', image_size=384, vit='base')
model.eval()
image = load_image(image_path)
caption = model.generate(image, sample=False, num_beams=3, max_length=20, min_length=5)

Vision Transformer example:

import tensorflow as tf
from vit_keras import vit
model = vit.vit_b16(
    image_size=224,
    activation='softmax',
    pretrained=True,
    include_top=True,
    pretrained_top=True
)
prediction = model.predict(image)

Pros of ImageBind

• Supports multimodal learning across six modalities (text, image, audio, video, depth, thermal)
• Enables zero-shot transfer between modalities without fine-tuning
• Offers a more versatile and flexible approach to multimodal tasks

Cons of ImageBind

• More complex architecture and potentially higher computational requirements
• May require more extensive training data to achieve optimal performance
• Less focused on specific vision-language tasks compared to BLIP

Code Comparison

BLIP (image-text matching):

image = transform(image).unsqueeze(0).to(device)
text = ["a dog", "a cat", "a person"]
itm_output = model(image, text, match_head='itm')
itm_scores = itm_output.image_text_similarity

ImageBind (multimodal embedding):

image_data = load_and_transform_vision_data(image_paths, device)
text_data = load_and_transform_text(text_list, device)
embeddings = model({"vision": image_data, "text": text_data})

Both repositories focus on multimodal learning, but ImageBind offers a broader range of modalities and zero-shot transfer capabilities. BLIP is more specialized for vision-language tasks, while ImageBind provides a more flexible framework for various multimodal applications. The code examples illustrate the different approaches: BLIP focuses on image-text matching, while ImageBind generates embeddings for multiple modalities simultaneously.

Pros of open_clip

• Broader range of pre-trained models, including support for various architectures like ViT, ResNet, and EfficientNet
• More flexible and customizable training options, allowing users to fine-tune models on custom datasets
• Active community development with frequent updates and improvements

Cons of open_clip

• Less focus on multimodal tasks compared to BLIP, which excels in image-text understanding
• May require more expertise to use effectively due to its flexibility and extensive options
• Potentially higher computational requirements for training and fine-tuning

Code Comparison

BLIP example:

from PIL import Image
import requests
from torchvision import transforms
from models.blip import blip_decoder

image = Image.open(requests.get(url, stream=True).raw).convert('RGB')
model = blip_decoder(pretrained='model_base', image_size=384, vit='base')
caption = model.generate(image, sample=False, num_beams=3, max_length=20, min_length=5)

open_clip example:

import open_clip
import torch
from PIL import Image

model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='laion2b_s34b_b79k')
image = preprocess(Image.open("CLIP.png")).unsqueeze(0)
text = open_clip.tokenize(["a diagram", "a dog", "a cat"])
with torch.no_grad():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)
    logits_per_image, logits_per_text = model(image, text)