Add Florence-2 documentation

docs/foundation/florence2.md

@@ -0,0 +1,64 @@
+<a href="https://blog.roboflow.com/florence-2/" target="_blank">Florence-2</a> is a multimodal model developed by Microsoft Research.
+
+You can use Florence-2 for:
+
+1. Object detection: Identify the location of all objects in an image. (`<OD>`)
+2. Dense region captioning: Generate dense captions for all identified regions in an image. (`<DENSE_REGION_CAPTION>`)
+3. Image captioning: Generate a caption for a whole image. (`<CAPTION>` for a short caption, `<DETAILED_CAPTION>` for a more detailed caption, and `<MORE_DETAILED_CAPTION>` for an even more detailed caption)
+4. Region proposal: Identify regions where there are likely to be objects in an image. (`<REGION_PROPOSAL>`)
+5. Phrase grounding: Identify the location of objects that match a text description. (`<CAPTION_TO_PHRASE_GROUNDING>`)
+6. Referring expression segmentation: Identify a segmentation mask that corresponds with a text input. (`<REFERRING_EXPRESSION_SEGMENTATION>`)
+7. Region to segmentation: Calculate a segmentation mask for an object from a bounding box region. (`<REGION_TO_SEGMENTATION>`)
+8. Open vocabulary detection: Identify the location of objects that match a text prompt. (`<OPEN_VOCABULARY_DETECTION>`)
+9. Region to description: Generate a description for a region in an image. (`<REGION_TO_DESCRIPTION>`)
+10. Optical Character Recognition (OCR): Read the text in an image. (`<OCR>`)
+11. OCR with region: Read the text in a specific region in an image. (`<OCR_WITH_REGION>`)
+
+You can use Inference for all the Florence-2 tasks above.
+
+The text in the parentheses are the task prompts you will need to use each task.
+
+### How to Use Florence-2
+
+??? Note "Install `inference`"
+
+    To install `inference` with Florence 2 support use the following command on CPU machine:
+
+    ```bash
+    pip install inference[transformers]
+    ```
+
+    or the following one for GPU machine:
+
+    ```bash
+    pip install inference-gpu[transformers]
+    ```
+
+Create a new Python file called `app.py` and add the following code:
+
+```python
+from inference import get_model
+
+model = get_model("florence-2-base", api_key="API_KEY")
+
+result = model.infer(
+    "https://media.roboflow.com/inference/seawithdock.jpeg", 
+    prompt="<CAPTION>",
+)
+
+print(result[0].response)
+```
+
+Above, replace `<CAPTION>` with the name of the task you want to use.
+
+Replace `API_KEY` with your Roboflow API key. [Learn how to retrieve your Roboflow API key](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
+
+To use PaliGemma with Inference, you will need a Roboflow API key. If you don't already have a Roboflow account, <a href="https://app.roboflow.com" target="_blank">sign up for a free Roboflow account</a>.
+
+Then, run the Python script you have created:
+
+```
+python app.py
+```
+
+The result from your model will be printed to the console.

docs/index.md

@@ -242,6 +242,11 @@ Below you can find list of extras available for `inference` and `inference-gpu`
   <td><a href="/foundation/yolo_world/">Yolo-World model</a></td>
   <td><code>N/A</code></td>
 </tr>
+<tr>
+  <td><code>transformers</code></td>
+  <td><code>transformers</code> based models, like <a href="/foundation/florence2/">Florence-2</a></td>
+  <td><code>N/A</code></td>
+</tr>
 </table>
 
 ??? note "Installing extras"

mkdocs.yml

@@ -49,6 +49,7 @@ nav:
           - CLIP (Classification, Embeddings): foundation/clip.md
           - CogVLM (Multimodal Language Model): foundation/cogvlm.md
           - DocTR (OCR): foundation/doctr.md
+          - Florence-2: foundation/florence2.md
           - TrOCR (OCR): foundation/trocr.md
           - Grounding DINO (Object Detection): foundation/grounding_dino.md
           - L2CS-Net (Gaze Detection): foundation/gaze.md