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Update README.md

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@@ -34,29 +34,46 @@ The model uses a CLIP backbone with a ViT-B/16 Transformer architecture as an im
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  ```python
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  import requests
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  from PIL import Image
 
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  import torch
 
 
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- from transformers import Owlv2Processor, Owlv2ForObjectDetection
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-
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- processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16")
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  model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16")
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  url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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  image = Image.open(requests.get(url, stream=True).raw)
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  texts = [["a photo of a cat", "a photo of a dog"]]
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  inputs = processor(text=texts, images=image, return_tensors="pt")
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- outputs = model(**inputs)
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- # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
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- target_sizes = torch.Tensor([image.size[::-1]])
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- # Convert outputs (bounding boxes and class logits) to COCO API
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- results = processor.post_process_object_detection(outputs=outputs, threshold=0.1, target_sizes=target_sizes)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  i = 0 # Retrieve predictions for the first image for the corresponding text queries
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  text = texts[i]
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  boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"]
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- # Print detected objects and rescaled box coordinates
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  for box, score, label in zip(boxes, scores, labels):
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  box = [round(i, 2) for i in box.tolist()]
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  print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")
 
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  ```python
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  import requests
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  from PIL import Image
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+ import numpy as np
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  import torch
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+ from transformers import AutoProcessor, Owlv2ForObjectDetection
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+ from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
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+ processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16")
 
 
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  model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16")
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  url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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  image = Image.open(requests.get(url, stream=True).raw)
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  texts = [["a photo of a cat", "a photo of a dog"]]
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  inputs = processor(text=texts, images=image, return_tensors="pt")
 
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+ # forward pass
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+ with torch.no_grad():
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+ outputs = model(**inputs)
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+
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+ # Note: boxes need to be visualized on the padded, unnormalized image
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+ # hence we'll set the target image sizes (height, width) based on that
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+
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+ def get_preprocessed_image(pixel_values):
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+ pixel_values = pixel_values.squeeze().numpy()
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+ unnormalized_image = (pixel_values * np.array(OPENAI_CLIP_STD)[:, None, None]) + np.array(OPENAI_CLIP_MEAN)[:, None, None]
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+ unnormalized_image = (unnormalized_image * 255).astype(np.uint8)
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+ unnormalized_image = np.moveaxis(unnormalized_image, 0, -1)
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+ unnormalized_image = Image.fromarray(unnormalized_image)
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+ return unnormalized_image
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+
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+ unnormalized_image = get_preprocessed_image(inputs.pixel_values)
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+
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+ target_sizes = torch.Tensor([unnormalized_image.size[::-1]])
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+ # Convert outputs (bounding boxes and class logits) to final bounding boxes and scores
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+ results = processor.post_process_object_detection(
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+ outputs=outputs, threshold=0.2, target_sizes=target_sizes
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+ )
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  i = 0 # Retrieve predictions for the first image for the corresponding text queries
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  text = texts[i]
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  boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"]
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  for box, score, label in zip(boxes, scores, labels):
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  box = [round(i, 2) for i in box.tolist()]
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  print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")