> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/mlfoundations/open_clip/llms.txt
> Use this file to discover all available pages before exploring further.

# image_transform_v2

> Create image preprocessing transforms with configuration objects

## Overview

Creates torchvision transform pipelines for preprocessing images before passing them to CLIP vision encoders. Accepts a `PreprocessCfg` configuration object for clean, declarative transform configuration.

## Function Signature

```python theme={null}
def image_transform_v2(
    cfg: PreprocessCfg,
    is_train: bool,
    aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = None,
) -> Compose
```

## Parameters

<ParamField path="cfg" type="PreprocessCfg" required>
  Preprocessing configuration object containing:

  * `size`: Target image size (int or tuple)
  * `mean`: Normalization mean values
  * `std`: Normalization std values
  * `interpolation`: Resize interpolation method
  * `resize_mode`: How to resize images ('shortest', 'longest', 'squash')
  * `fill_color`: Padding fill color

  See [`PreprocessCfg`](/api/preprocess-cfg) for details.
</ParamField>

<ParamField path="is_train" type="bool" required>
  Whether to create training (with augmentation) or inference (deterministic) transforms.
</ParamField>

<ParamField path="aug_cfg" type="Union[Dict, AugmentationCfg]" default="None">
  Augmentation configuration for training. Only used when `is_train=True`.

  See [`AugmentationCfg`](/api/augmentation-cfg) for options.
</ParamField>

## Returns

<ResponseField name="transform" type="torchvision.transforms.Compose">
  Composed transform pipeline that can be applied to PIL Images. Includes:

  * **Training**: Random crops, color jitter, normalization
  * **Inference**: Resize, center crop, normalization
</ResponseField>

## Examples

### Create inference transform

```python theme={null}
import open_clip
from PIL import Image

# Create preprocessing config
preprocess_cfg = open_clip.PreprocessCfg(
    size=224,
    mean=(0.48145466, 0.4578275, 0.40821073),
    std=(0.26862954, 0.26130258, 0.27577711),
    interpolation='bicubic',
    resize_mode='shortest'
)

# Create inference transform
transform = open_clip.image_transform_v2(
    cfg=preprocess_cfg,
    is_train=False
)

# Apply to image
image = Image.open('cat.jpg')
image_tensor = transform(image)
print(image_tensor.shape)  # torch.Size([3, 224, 224])
```

### Create training transform with augmentation

```python theme={null}
import open_clip

# Preprocessing config
preprocess_cfg = open_clip.PreprocessCfg(
    size=224,
    interpolation='bicubic'
)

# Augmentation config
aug_cfg = open_clip.AugmentationCfg(
    scale=(0.08, 1.0),
    ratio=(0.75, 1.33),
    color_jitter=(0.4, 0.4, 0.4, 0.1),
    color_jitter_prob=0.8,
    gray_scale_prob=0.2
)

# Create training transform
train_transform = open_clip.image_transform_v2(
    cfg=preprocess_cfg,
    is_train=True,
    aug_cfg=aug_cfg
)
```

### Use with model preprocess config

```python theme={null}
import open_clip
import torch
from PIL import Image

# Load model and get its preprocessing config
model, preprocess_cfg, _ = open_clip.create_model_and_transforms(
    'ViT-B-32',
    pretrained='laion2b_s34b_b79k',
    precision='fp16',
    device='cuda'
)

# Create transform from model's config
transform = open_clip.image_transform_v2(
    cfg=preprocess_cfg,
    is_train=False
)

# Process image
image = Image.open('photo.jpg')
image_tensor = transform(image).unsqueeze(0).to('cuda')

# Encode
with torch.no_grad():
    image_features = model.encode_image(image_tensor)
```

### Different resize modes

```python theme={null}
import open_clip

# Shortest edge resize (default)
cfg_shortest = open_clip.PreprocessCfg(size=224, resize_mode='shortest')
transform_shortest = open_clip.image_transform_v2(cfg_shortest, is_train=False)

# Longest edge resize with padding
cfg_longest = open_clip.PreprocessCfg(size=224, resize_mode='longest', fill_color=0)
transform_longest = open_clip.image_transform_v2(cfg_longest, is_train=False)

# Squash to exact size (may distort aspect ratio)
cfg_squash = open_clip.PreprocessCfg(size=(224, 224), resize_mode='squash')
transform_squash = open_clip.image_transform_v2(cfg_squash, is_train=False)
```

### Training with timm augmentations

```python theme={null}
import open_clip

# Use timm's RandAugment and other advanced augmentations
preprocess_cfg = open_clip.PreprocessCfg(size=224)
aug_cfg = open_clip.AugmentationCfg(
    scale=(0.08, 1.0),
    color_jitter=0.4,
    re_prob=0.25,  # Random erasing probability
    re_count=1,
    use_timm=True  # Enable timm augmentations
)

train_transform = open_clip.image_transform_v2(
    cfg=preprocess_cfg,
    is_train=True,
    aug_cfg=aug_cfg
)
```

### Non-square images

```python theme={null}
import open_clip

# Rectangular image preprocessing
preprocess_cfg = open_clip.PreprocessCfg(
    size=(384, 224),  # height, width
    resize_mode='shortest'
)

transform = open_clip.image_transform_v2(cfg=preprocess_cfg, is_train=False)
```

## Transform Pipeline

### Inference Mode (`is_train=False`)

1. **Resize** based on `resize_mode`:
   * `shortest`: Resize shortest edge to target size
   * `longest`: Resize longest edge to target size
   * `squash`: Resize to exact dimensions
2. **Center Crop** (or pad if needed)
3. **Convert to RGB**
4. **To Tensor**
5. **Normalize** with mean/std

### Training Mode (`is_train=True`)

1. **Random Resized Crop** with scale and ratio
2. **Convert to RGB**
3. **Color Jitter** (optional, based on aug\_cfg)
4. **Grayscale** (optional, based on aug\_cfg)
5. **To Tensor**
6. **Normalize** with mean/std
7. **Random Erasing** (optional, if using timm)

## Notes

* For most use cases, use the transforms returned by [`create_model_and_transforms()`](/api/create-model-and-transforms)
* `PreprocessCfg` provides type-safe configuration compared to passing individual parameters
* Training transforms include random augmentation for better generalization
* Inference transforms are deterministic and optimized for consistent preprocessing
* Images are automatically converted to RGB mode
* Normalization uses ImageNet statistics by default

## See Also

* [`PreprocessCfg`](/api/preprocess-cfg) - Preprocessing configuration dataclass
* [`AugmentationCfg`](/api/augmentation-cfg) - Augmentation configuration dataclass
* [`create_model_and_transforms()`](/api/create-model-and-transforms) - Get model with transforms