# Condensed: Zero-Shot Image Classification with CLIP

Summary: This tutorial demonstrates implementing zero-shot image classification using CLIP via AutoGluon's MultiModalPredictor. It shows how to perform image classification without training data by matching images against text descriptions. Key implementation knowledge includes the basic predictor initialization and prediction pattern, handling image-text pair inputs, and awareness of typographic attack limitations. The tutorial helps with tasks like building zero-shot classifiers, implementing CLIP-based image classification, and utilizing pre-trained models that achieve ResNet50-level performance. Core features covered include zero-shot classification without training data, flexible category definition through text descriptions, and batch prediction support, all implemented through a straightforward Python API.

*This is a condensed version that preserves essential implementation details and context.*

Here's the condensed tutorial focusing on essential implementation details:

# Zero-Shot Image Classification with CLIP

## Key Implementation Details

```python
from autogluon.multimodal import MultiModalPredictor
from autogluon.multimodal import download

# Initialize predictor
predictor = MultiModalPredictor(problem_type="zero_shot_image_classification")

# Basic usage pattern
prob = predictor.predict_proba(
    {"image": [image_path]}, 
    {"text": ['class1', 'class2', 'class3']}
)
```

## Core Functionality

1. **Zero-shot Classification**: CLIP can classify images without training data by providing text descriptions of categories
2. **Input Format**: 
   - Images: Provided as file paths
   - Categories: Provided as text descriptions in list format

## Key Features & Capabilities

- Matches supervised ResNet50 performance on ImageNet (76.2% top-1 accuracy) without training
- Works with arbitrary visual categories by simply providing text descriptions
- Pre-trained on 400M image-text pairs

## Important Limitations

1. **Vulnerability to Typographic Attacks**
   - CLIP's predictions can be significantly influenced by text present in images
   - Example: An apple image labeled as "iPod" can cause CLIP to misclassify it as an iPod

2. **Usage Pattern for Classification**:
```python
# Basic classification
prob = predictor.predict_proba(
    {"image": [image_path]}, 
    {"text": ['category1', 'category2', ...]}
)
```

## Best Practices

1. Use clear, descriptive text labels for categories
2. Be aware of potential text-based vulnerabilities in images
3. Provide multiple relevant category options for better classification

## Technical Notes

- No training required - purely inference-based
- Works with common image formats
- Supports batch prediction
- Based on contrastive learning between image and text pairs

This implementation provides zero-shot image classification capabilities without the need for training data or fine-tuning.