|
|
| ### Usage |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from PIL import Image |
| |
| |
| |
| # Captioning |
| print("Short caption:") |
| print(model.caption(image, length="short")["caption"]) |
| |
| print("\nNormal caption:") |
| for t in model.caption(image, length="normal", stream=True)["caption"]: |
| # Streaming generation example, supported for caption() and detect() |
| print(t, end="", flush=True) |
| print(model.caption(image, length="normal")) |
| |
| # Visual Querying |
| print("\nVisual query: 'How many people are in the image?'") |
| print(model.query(image, "How many people are in the image?")["answer"]) |
| |
| # Object Detection |
| print("\nObject detection: 'face'") |
| objects = model.detect(image, "face")["objects"] |
| print(f"Found {len(objects)} face(s)") |
| |
| # Pointing |
| print("\nPointing: 'person'") |
| points = model.point(image, "person")["points"] |
| print(f"Found {len(points)} person(s)") |
| ``` |
