> ## Documentation Index
> Fetch the complete documentation index at: https://docs.exla.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# CLIP

> Multimodal image-text matching model optimized for any device

# CLIP Model

The CLIP (Contrastive Language-Image Pretraining) model is a powerful multimodal model that connects text and images. With InferX, you can run CLIP on any device using the same API - whether it's a Jetson, GPU server, or CPU-only system.

## Features

* **Universal API**: Same code works on Jetson, GPU, or CPU
* **Hardware-Optimized**: Automatically detects your hardware and uses the appropriate implementation
* **Real-time Processing**: Optimized for fast inference across all platforms
* **Zero Configuration**: No setup required - just import and run

## Installation

CLIP is included with InferX. No separate installation required.

```bash theme={null}
pip install git+https://github.com/exla-ai/InferX.git
```

## Basic Usage

```python theme={null}
from inferx.models.clip import clip
import json

# Initialize the model (automatically detects your hardware)
model = clip()

# Run inference
results = model.inference(
    image_paths=["path/to/image1.jpg", "path/to/image2.jpg"],
    text_queries=["a photo of a dog", "a photo of a cat", "a photo of a bird"]
)

# Print results
print(json.dumps(results, indent=2))
```

## Advanced Usage

### Processing Multiple Images

```python theme={null}
from inferx.models.clip import clip

# Process a list of images
images = [
    "path/to/image1.jpg",
    "path/to/image2.jpg",
    "path/to/image3.jpg"
]

# Or load images from a text file (one path per line)
images = "path/to/image_list.txt"

model = clip()
results = model.inference(
    image_paths=images,
    text_queries=["query1", "query2", "query3"]
)
```

### Batch Processing

```python theme={null}
from inferx.models.clip import clip
import os

# Initialize model
model = clip()

# Process directory of images
image_directory = "path/to/images/"
image_paths = [
    os.path.join(image_directory, f) 
    for f in os.listdir(image_directory) 
    if f.endswith(('.jpg', '.png', '.jpeg'))
]

text_queries = [
    "a photo of a dog",
    "a photo of a cat", 
    "a landscape photo",
    "a person walking"
]

results = model.inference(
    image_paths=image_paths,
    text_queries=text_queries
)
```

## Performance

InferX automatically optimizes CLIP for your hardware:

| Hardware        | Typical Inference Time | Memory Usage |
| --------------- | ---------------------- | ------------ |
| Jetson AGX Orin | \~50ms                 | \~2GB        |
| RTX 4090        | \~20ms                 | \~3GB        |
| Intel i7 CPU    | \~200ms                | \~1GB        |

## Response Format

```json theme={null}
[
  {
    "a photo of a dog": [
      {
        "image_path": "data/dog.png",
        "score": "23.1011"
      },
      {
        "image_path": "data/cat.png",
        "score": "17.1396"
      }
    ]
  }
]
```

## Hardware Detection

InferX automatically detects and optimizes for your hardware:

```
✨ InferX - CLIP Model ✨
🔍 Device Detected: AGX_ORIN
⠏ [0.5s] Initializing InferX Optimized CLIP model for AGX_ORIN [GPU Mode]
✓ [0.6s] Ready for inference
```

## Error Handling

```python theme={null}
from inferx.models.clip import clip

try:
    model = clip()
    results = model.inference(
        image_paths=["nonexistent.jpg"],
        text_queries=["test query"]
    )
except FileNotFoundError:
    print("Image file not found")
except Exception as e:
    print(f"Error: {e}")
```

## Next Steps

* Explore other [InferX models](/models)
* Check out [practical examples](https://github.com/exla-ai/InferX-examples/tree/main/clip)
* Learn about [custom model optimization](/models/custom-models/overview)