| prompt = f"Question: {question} Answer:" | |
| Now we need to preprocess the image/prompt with the model's processor, pass the processed input through the model, and decode the output: | |
| inputs = processor(image, text=prompt, return_tensors="pt").to(device, torch.float16) | |
| generated_ids = model.generate(**inputs, max_new_tokens=10) | |
| generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() | |
| print(generated_text) | |
| "He is looking at the crowd" | |
| As you can see, the model recognized the crowd, and the direction of the face (looking down), however, it seems to miss | |
| the fact the crowd is behind the skater. |