> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/developer/python-sdk/two-stage-and-clip-inference.md).

# Two-Stage and CLIP Inference

`Workspace` exposes three convenience methods that combine multiple models or modalities in one call:

* `two_stage()` - run an object-detection model, then run a second model on each crop.
* `two_stage_ocr()` - run an object-detection model, then OCR each crop.
* `clip_compare()` - embed every image in a directory with [CLIP](https://openai.com/research/clip) and rank them against a target image.

These helpers are useful for prototypes (license plates, badge readers, similar-image search) before you build the equivalent in a [Workflow](/developer/python-sdk/manage-workflows.md).

## Two-stage detection + classification

The first stage detects regions of interest. The second stage classifies (or runs another detector on) each cropped region.

```python
import roboflow

rf = roboflow.Roboflow(api_key="YOUR_API_KEY")
ws = rf.workspace()

results = ws.two_stage(
    image="photo.jpg",
    first_stage_model_name="cars-or-trucks",
    first_stage_model_version=2,
    second_stage_model_name="vehicle-make",
    second_stage_model_version=4,
)

for r in results:
    print(r)
```

Each entry in `results` carries the parent detection (bbox, class, confidence) and the second-stage classification.

## Two-stage detection + OCR

```python
results = ws.two_stage_ocr(
    image="photo.jpg",
    first_stage_model_name="license-plates",
    first_stage_model_version=1,
)

for r in results:
    print(r["bbox"], r["text"])
```

The second stage runs Tesseract-style OCR on each crop and returns the recognized text alongside the bounding box.

## CLIP image comparison

`clip_compare()` embeds every image in a directory and the supplied target, returns the cosine-similarity score for each pair, sorted by similarity.

```python
results = ws.clip_compare(
    dir="./gallery",
    image_ext=".jpg",
    target_image="./query.jpg",
)

for r in results:
    print(r["image"], r["similarity"])
```

### Parameters

* `dir` (str) - directory to scan for candidate images.
* `image_ext` (str, default `".png"`) - file extension to match. Note this includes the leading dot.
* `target_image` (str) - path to the image you're searching for similarity against.

## When to graduate to Workflows

These helpers run on the hosted inference endpoint and serially make one HTTP call per stage / per image. Once you need batching, conditional branching, custom blocks, or a long-running pipeline, build the equivalent as a [Workflow](/developer/python-sdk/manage-workflows.md) - the runtime is purpose-built for chained inference and supports streaming inputs.