refactor: reorganize skills into sub-categories
The skills directory was getting disorganized — mlops alone had 40 skills in a flat list, and 12 categories were singletons with just one skill each. Code change: - prompt_builder.py: Support sub-categories in skill scanner. skills/mlops/training/axolotl/SKILL.md now shows as category 'mlops/training' instead of just 'mlops'. Backwards-compatible with existing flat structure. Split mlops (40 skills) into 7 sub-categories: - mlops/training (12): accelerate, axolotl, flash-attention, grpo-rl-training, peft, pytorch-fsdp, pytorch-lightning, simpo, slime, torchtitan, trl-fine-tuning, unsloth - mlops/inference (8): gguf, guidance, instructor, llama-cpp, obliteratus, outlines, tensorrt-llm, vllm - mlops/models (6): audiocraft, clip, llava, segment-anything, stable-diffusion, whisper - mlops/vector-databases (4): chroma, faiss, pinecone, qdrant - mlops/evaluation (5): huggingface-tokenizers, lm-evaluation-harness, nemo-curator, saelens, weights-and-biases - mlops/cloud (2): lambda-labs, modal - mlops/research (1): dspy Merged singleton categories: - gifs → media (gif-search joins youtube-content) - music-creation → media (heartmula, songsee) - diagramming → creative (excalidraw joins ascii-art) - ocr-and-documents → productivity - domain → research (domain-intel) - feeds → research (blogwatcher) - market-data → research (polymarket) Fixed misplaced skills: - mlops/code-review → software-development (not ML-specific) - mlops/ml-paper-writing → research (academic writing) Added DESCRIPTION.md files for all new/updated categories.
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skills/mlops/models/clip/references/applications.md
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skills/mlops/models/clip/references/applications.md
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# CLIP Applications Guide
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Practical applications and use cases for CLIP.
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## Zero-shot image classification
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```python
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import torch
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import clip
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from PIL import Image
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model, preprocess = clip.load("ViT-B/32")
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# Define categories
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categories = [
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"a photo of a dog",
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"a photo of a cat",
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"a photo of a bird",
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"a photo of a car",
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"a photo of a person"
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]
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# Prepare image
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image = preprocess(Image.open("photo.jpg")).unsqueeze(0)
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text = clip.tokenize(categories)
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# Classify
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with torch.no_grad():
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image_features = model.encode_image(image)
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text_features = model.encode_text(text)
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logits_per_image, _ = model(image, text)
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probs = logits_per_image.softmax(dim=-1).cpu().numpy()
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# Print results
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for category, prob in zip(categories, probs[0]):
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print(f"{category}: {prob:.2%}")
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```
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## Semantic image search
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```python
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# Index images
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image_database = []
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image_paths = ["img1.jpg", "img2.jpg", "img3.jpg"]
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for img_path in image_paths:
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image = preprocess(Image.open(img_path)).unsqueeze(0)
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with torch.no_grad():
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features = model.encode_image(image)
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features /= features.norm(dim=-1, keepdim=True)
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image_database.append((img_path, features))
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# Search with text
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query = "a sunset over mountains"
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text_input = clip.tokenize([query])
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with torch.no_grad():
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text_features = model.encode_text(text_input)
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text_features /= text_features.norm(dim=-1, keepdim=True)
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# Find matches
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similarities = []
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for img_path, img_features in image_database:
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similarity = (text_features @ img_features.T).item()
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similarities.append((img_path, similarity))
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# Sort by similarity
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similarities.sort(key=lambda x: x[1], reverse=True)
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for img_path, score in similarities[:3]:
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print(f"{img_path}: {score:.3f}")
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```
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## Content moderation
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```python
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# Define safety categories
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categories = [
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"safe for work content",
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"not safe for work content",
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"violent or graphic content",
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"hate speech or offensive content",
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"spam or misleading content"
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]
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text = clip.tokenize(categories)
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# Check image
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with torch.no_grad():
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logits, _ = model(image, text)
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probs = logits.softmax(dim=-1)
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# Get classification
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max_idx = probs.argmax().item()
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confidence = probs[0, max_idx].item()
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if confidence > 0.7:
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print(f"Classified as: {categories[max_idx]} ({confidence:.2%})")
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else:
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print(f"Uncertain classification (confidence: {confidence:.2%})")
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```
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## Image-to-text retrieval
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```python
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# Text database
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captions = [
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"A beautiful sunset over the ocean",
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"A cute dog playing in the park",
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"A modern city skyline at night",
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"A delicious pizza with toppings"
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]
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# Encode captions
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caption_features = []
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for caption in captions:
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text = clip.tokenize([caption])
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with torch.no_grad():
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features = model.encode_text(text)
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features /= features.norm(dim=-1, keepdim=True)
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caption_features.append(features)
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caption_features = torch.cat(caption_features)
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# Find matching captions for image
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with torch.no_grad():
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image_features = model.encode_image(image)
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image_features /= image_features.norm(dim=-1, keepdim=True)
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similarities = (image_features @ caption_features.T).squeeze(0)
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top_k = similarities.topk(3)
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for idx, score in zip(top_k.indices, top_k.values):
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print(f"{captions[idx]}: {score:.3f}")
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```
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## Visual question answering
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```python
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# Create yes/no questions
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image = preprocess(Image.open("photo.jpg")).unsqueeze(0)
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questions = [
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"a photo showing people",
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"a photo showing animals",
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"a photo taken indoors",
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"a photo taken outdoors",
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"a photo taken during daytime",
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"a photo taken at night"
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]
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text = clip.tokenize(questions)
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with torch.no_grad():
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logits, _ = model(image, text)
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probs = logits.softmax(dim=-1)
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# Answer questions
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for question, prob in zip(questions, probs[0]):
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answer = "Yes" if prob > 0.5 else "No"
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print(f"{question}: {answer} ({prob:.2%})")
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```
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## Image deduplication
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```python
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# Detect duplicate/similar images
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def compute_similarity(img1_path, img2_path):
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img1 = preprocess(Image.open(img1_path)).unsqueeze(0)
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img2 = preprocess(Image.open(img2_path)).unsqueeze(0)
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with torch.no_grad():
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feat1 = model.encode_image(img1)
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feat2 = model.encode_image(img2)
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feat1 /= feat1.norm(dim=-1, keepdim=True)
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feat2 /= feat2.norm(dim=-1, keepdim=True)
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similarity = (feat1 @ feat2.T).item()
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return similarity
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# Check for duplicates
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threshold = 0.95
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image_pairs = [("img1.jpg", "img2.jpg"), ("img1.jpg", "img3.jpg")]
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for img1, img2 in image_pairs:
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sim = compute_similarity(img1, img2)
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if sim > threshold:
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print(f"{img1} and {img2} are duplicates (similarity: {sim:.3f})")
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```
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## Best practices
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1. **Use descriptive labels** - "a photo of X" works better than just "X"
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2. **Normalize embeddings** - Always normalize for cosine similarity
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3. **Batch processing** - Process multiple images/texts together
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4. **Cache embeddings** - Expensive to recompute
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5. **Set appropriate thresholds** - Test on validation data
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6. **Use GPU** - 10-50× faster than CPU
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7. **Consider model size** - ViT-B/32 good default, ViT-L/14 for best quality
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## Resources
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- **Paper**: https://arxiv.org/abs/2103.00020
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- **GitHub**: https://github.com/openai/CLIP
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- **Colab**: https://colab.research.google.com/github/openai/clip/
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