hermes-agent/website/docs/user-guide/skills/optional/mlops/mlops-clip.md

title	sidebar_label	description
Clip — OpenAI's model connecting vision and language	Clip	OpenAI's model connecting vision and language

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Clip

OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.

Skill metadata

Source	Optional — install with hermes skills install official/mlops/clip
Path	optional-skills/mlops/clip
Version	1.0.0
Author	Orchestra Research
License	MIT
Dependencies	transformers, torch, pillow
Tags	Multimodal, CLIP, Vision-Language, Zero-Shot, Image Classification, OpenAI, Image Search, Cross-Modal Retrieval, Content Moderation

Reference: full SKILL.md

:::info The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active. :::

CLIP - Contrastive Language-Image Pre-Training

OpenAI's model that understands images from natural language.

When to use CLIP

Use when:

• Zero-shot image classification (no training data needed)
• Image-text similarity/matching
• Semantic image search
• Content moderation (detect NSFW, violence)
• Visual question answering
• Cross-modal retrieval (image→text, text→image)

Metrics:

• 25,300+ GitHub stars
• Trained on 400M image-text pairs
• Matches ResNet-50 on ImageNet (zero-shot)
• MIT License

Use alternatives instead:

• BLIP-2: Better captioning
• LLaVA: Vision-language chat
• Segment Anything: Image segmentation

Quick start

Installation

pip install git+https://github.com/openai/CLIP.git
pip install torch torchvision ftfy regex tqdm

Zero-shot classification

import torch
import clip
from PIL import Image

# Load model
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-B/32", device=device)

# Load image
image = preprocess(Image.open("photo.jpg")).unsqueeze(0).to(device)

# Define possible labels
text = clip.tokenize(["a dog", "a cat", "a bird", "a car"]).to(device)

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

    # Cosine similarity
    logits_per_image, logits_per_text = model(image, text)
    probs = logits_per_image.softmax(dim=-1).cpu().numpy()

# Print results
labels = ["a dog", "a cat", "a bird", "a car"]
for label, prob in zip(labels, probs[0]):
    print(f"{label}: {prob:.2%}")

Available models

# Models (sorted by size)
models = [
    "RN50",           # ResNet-50
    "RN101",          # ResNet-101
    "ViT-B/32",       # Vision Transformer (recommended)
    "ViT-B/16",       # Better quality, slower
    "ViT-L/14",       # Best quality, slowest
]

model, preprocess = clip.load("ViT-B/32")

Model	Parameters	Speed	Quality
RN50	102M	Fast	Good
ViT-B/32	151M	Medium	Better
ViT-L/14	428M	Slow	Best

Image-text similarity

# Compute embeddings
image_features = model.encode_image(image)
text_features = model.encode_text(text)

# Normalize
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)

# Cosine similarity
similarity = (image_features @ text_features.T).item()
print(f"Similarity: {similarity:.4f}")

Semantic image search

# Index images
image_paths = ["img1.jpg", "img2.jpg", "img3.jpg"]
image_embeddings = []

for img_path in image_paths:
    image = preprocess(Image.open(img_path)).unsqueeze(0).to(device)
    with torch.no_grad():
        embedding = model.encode_image(image)
        embedding /= embedding.norm(dim=-1, keepdim=True)
    image_embeddings.append(embedding)

image_embeddings = torch.cat(image_embeddings)

# Search with text query
query = "a sunset over the ocean"
text_input = clip.tokenize([query]).to(device)
with torch.no_grad():
    text_embedding = model.encode_text(text_input)
    text_embedding /= text_embedding.norm(dim=-1, keepdim=True)

# Find most similar images
similarities = (text_embedding @ image_embeddings.T).squeeze(0)
top_k = similarities.topk(3)

for idx, score in zip(top_k.indices, top_k.values):
    print(f"{image_paths[idx]}: {score:.3f}")

Content moderation

# Define categories
categories = [
    "safe for work",
    "not safe for work",
    "violent content",
    "graphic content"
]

text = clip.tokenize(categories).to(device)

# Check image
with torch.no_grad():
    logits_per_image, _ = model(image, text)
    probs = logits_per_image.softmax(dim=-1)

# Get classification
max_idx = probs.argmax().item()
max_prob = probs[0, max_idx].item()

print(f"Category: {categories[max_idx]} ({max_prob:.2%})")

Batch processing

# Process multiple images
images = [preprocess(Image.open(f"img{i}.jpg")) for i in range(10)]
images = torch.stack(images).to(device)

with torch.no_grad():
    image_features = model.encode_image(images)
    image_features /= image_features.norm(dim=-1, keepdim=True)

# Batch text
texts = ["a dog", "a cat", "a bird"]
text_tokens = clip.tokenize(texts).to(device)

with torch.no_grad():
    text_features = model.encode_text(text_tokens)
    text_features /= text_features.norm(dim=-1, keepdim=True)

# Similarity matrix (10 images × 3 texts)
similarities = image_features @ text_features.T
print(similarities.shape)  # (10, 3)

Integration with vector databases

# Store CLIP embeddings in Chroma/FAISS
import chromadb

client = chromadb.Client()
collection = client.create_collection("image_embeddings")

# Add image embeddings
for img_path, embedding in zip(image_paths, image_embeddings):
    collection.add(
        embeddings=[embedding.cpu().numpy().tolist()],
        metadatas=[{"path": img_path}],
        ids=[img_path]
    )

# Query with text
query = "a sunset"
text_embedding = model.encode_text(clip.tokenize([query]))
results = collection.query(
    query_embeddings=[text_embedding.cpu().numpy().tolist()],
    n_results=5
)

Best practices

1. Use ViT-B/32 for most cases - Good balance
2. Normalize embeddings - Required for cosine similarity
3. Batch processing - More efficient
4. Cache embeddings - Expensive to recompute
5. Use descriptive labels - Better zero-shot performance
6. GPU recommended - 10-50× faster
7. Preprocess images - Use provided preprocess function

Performance

Operation	CPU	GPU (V100)
Image encoding	~200ms	~20ms
Text encoding	~50ms	~5ms
Similarity compute	<1ms	<1ms

Limitations

1. Not for fine-grained tasks - Best for broad categories
2. Requires descriptive text - Vague labels perform poorly
3. Biased on web data - May have dataset biases
4. No bounding boxes - Whole image only
5. Limited spatial understanding - Position/counting weak

Resources

• GitHub: https://github.com/openai/CLIP ⭐ 25,300+
• Paper: https://arxiv.org/abs/2103.00020
• Colab: https://colab.research.google.com/github/openai/clip/
• License: MIT