sam2-hiera-large

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--- license: apache-2.

Code Examples

Batched Images with Batched Objects and Multiple Pointspython
>>> # Add groceries image for more complex example
>>> groceries_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/groceries.jpg"
>>> groceries_image = Image.open(requests.get(groceries_url, stream=True).raw).convert("RGB")
>>> raw_images = [raw_images[0], groceries_image]  # Use truck and groceries images

>>> # Complex batching: multiple images, multiple objects, multiple points per object
>>> input_points = [
...     [[[500, 375]], [[650, 750]]],  # Truck image: 2 objects with 1 point each
...     [[[400, 300]], [[630, 300], [550, 300]]]  # Groceries image: obj1 has 1 point, obj2 has 2 points
... ]
>>> input_labels = [
...     [[1], [1]],  # Truck image: positive clicks
...     [[1], [1, 1]]  # Groceries image: positive clicks for refinement
... ]

>>> inputs = processor(images=raw_images, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
Batched Images with Batched Objects and Multiple Pointspython
>>> # Add groceries image for more complex example
>>> groceries_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/groceries.jpg"
>>> groceries_image = Image.open(requests.get(groceries_url, stream=True).raw).convert("RGB")
>>> raw_images = [raw_images[0], groceries_image]  # Use truck and groceries images

>>> # Complex batching: multiple images, multiple objects, multiple points per object
>>> input_points = [
...     [[[500, 375]], [[650, 750]]],  # Truck image: 2 objects with 1 point each
...     [[[400, 300]], [[630, 300], [550, 300]]]  # Groceries image: obj1 has 1 point, obj2 has 2 points
... ]
>>> input_labels = [
...     [[1], [1]],  # Truck image: positive clicks
...     [[1], [1, 1]]  # Groceries image: positive clicks for refinement
... ]

>>> inputs = processor(images=raw_images, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
Batched Images with Batched Objects and Multiple Pointspython
>>> # Add groceries image for more complex example
>>> groceries_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/groceries.jpg"
>>> groceries_image = Image.open(requests.get(groceries_url, stream=True).raw).convert("RGB")
>>> raw_images = [raw_images[0], groceries_image]  # Use truck and groceries images

>>> # Complex batching: multiple images, multiple objects, multiple points per object
>>> input_points = [
...     [[[500, 375]], [[650, 750]]],  # Truck image: 2 objects with 1 point each
...     [[[400, 300]], [[630, 300], [550, 300]]]  # Groceries image: obj1 has 1 point, obj2 has 2 points
... ]
>>> input_labels = [
...     [[1], [1]],  # Truck image: positive clicks
...     [[1], [1, 1]]  # Groceries image: positive clicks for refinement
... ]

>>> inputs = processor(images=raw_images, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
Batched Images with Batched Objects and Multiple Pointspython
>>> # Add groceries image for more complex example
>>> groceries_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/groceries.jpg"
>>> groceries_image = Image.open(requests.get(groceries_url, stream=True).raw).convert("RGB")
>>> raw_images = [raw_images[0], groceries_image]  # Use truck and groceries images

>>> # Complex batching: multiple images, multiple objects, multiple points per object
>>> input_points = [
...     [[[500, 375]], [[650, 750]]],  # Truck image: 2 objects with 1 point each
...     [[[400, 300]], [[630, 300], [550, 300]]]  # Groceries image: obj1 has 1 point, obj2 has 2 points
... ]
>>> input_labels = [
...     [[1], [1]],  # Truck image: positive clicks
...     [[1], [1, 1]]  # Groceries image: positive clicks for refinement
... ]

>>> inputs = processor(images=raw_images, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
Batched Images with Batched Objects and Multiple Pointspython
>>> # Add groceries image for more complex example
>>> groceries_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/groceries.jpg"
>>> groceries_image = Image.open(requests.get(groceries_url, stream=True).raw).convert("RGB")
>>> raw_images = [raw_images[0], groceries_image]  # Use truck and groceries images

>>> # Complex batching: multiple images, multiple objects, multiple points per object
>>> input_points = [
...     [[[500, 375]], [[650, 750]]],  # Truck image: 2 objects with 1 point each
...     [[[400, 300]], [[630, 300], [550, 300]]]  # Groceries image: obj1 has 1 point, obj2 has 2 points
... ]
>>> input_labels = [
...     [[1], [1]],  # Truck image: positive clicks
...     [[1], [1, 1]]  # Groceries image: positive clicks for refinement
... ]

>>> inputs = processor(images=raw_images, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
Batched Images with Batched Objects and Multiple Pointspython
>>> # Add groceries image for more complex example
>>> groceries_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/groceries.jpg"
>>> groceries_image = Image.open(requests.get(groceries_url, stream=True).raw).convert("RGB")
>>> raw_images = [raw_images[0], groceries_image]  # Use truck and groceries images

>>> # Complex batching: multiple images, multiple objects, multiple points per object
>>> input_points = [
...     [[[500, 375]], [[650, 750]]],  # Truck image: 2 objects with 1 point each
...     [[[400, 300]], [[630, 300], [550, 300]]]  # Groceries image: obj1 has 1 point, obj2 has 2 points
... ]
>>> input_labels = [
...     [[1], [1]],  # Truck image: positive clicks
...     [[1], [1, 1]]  # Groceries image: positive clicks for refinement
... ]

>>> inputs = processor(images=raw_images, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
Batched Images with Batched Objects and Multiple Pointspython
>>> # Add groceries image for more complex example
>>> groceries_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/groceries.jpg"
>>> groceries_image = Image.open(requests.get(groceries_url, stream=True).raw).convert("RGB")
>>> raw_images = [raw_images[0], groceries_image]  # Use truck and groceries images

>>> # Complex batching: multiple images, multiple objects, multiple points per object
>>> input_points = [
...     [[[500, 375]], [[650, 750]]],  # Truck image: 2 objects with 1 point each
...     [[[400, 300]], [[630, 300], [550, 300]]]  # Groceries image: obj1 has 1 point, obj2 has 2 points
... ]
>>> input_labels = [
...     [[1], [1]],  # Truck image: positive clicks
...     [[1], [1, 1]]  # Groceries image: positive clicks for refinement
... ]

>>> inputs = processor(images=raw_images, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
Batched Images with Batched Objects and Multiple Pointspython
>>> # Add groceries image for more complex example
>>> groceries_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/groceries.jpg"
>>> groceries_image = Image.open(requests.get(groceries_url, stream=True).raw).convert("RGB")
>>> raw_images = [raw_images[0], groceries_image]  # Use truck and groceries images

>>> # Complex batching: multiple images, multiple objects, multiple points per object
>>> input_points = [
...     [[[500, 375]], [[650, 750]]],  # Truck image: 2 objects with 1 point each
...     [[[400, 300]], [[630, 300], [550, 300]]]  # Groceries image: obj1 has 1 point, obj2 has 2 points
... ]
>>> input_labels = [
...     [[1], [1]],  # Truck image: positive clicks
...     [[1], [1, 1]]  # Groceries image: positive clicks for refinement
... ]

>>> inputs = processor(images=raw_images, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
Batched Images with Batched Objects and Multiple Pointspython
>>> # Add groceries image for more complex example
>>> groceries_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/groceries.jpg"
>>> groceries_image = Image.open(requests.get(groceries_url, stream=True).raw).convert("RGB")
>>> raw_images = [raw_images[0], groceries_image]  # Use truck and groceries images

>>> # Complex batching: multiple images, multiple objects, multiple points per object
>>> input_points = [
...     [[[500, 375]], [[650, 750]]],  # Truck image: 2 objects with 1 point each
...     [[[400, 300]], [[630, 300], [550, 300]]]  # Groceries image: obj1 has 1 point, obj2 has 2 points
... ]
>>> input_labels = [
...     [[1], [1]],  # Truck image: positive clicks
...     [[1], [1, 1]]  # Groceries image: positive clicks for refinement
... ]

>>> inputs = processor(images=raw_images, input_points=input_points, input_labels=input_labels, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
Batched Bounding Boxespython
>>> # Multiple bounding boxes per image (using truck and groceries images)
>>> input_boxes = [
...     [[75, 275, 1725, 850], [425, 600, 700, 875], [1375, 550, 1650, 800], [1240, 675, 1400, 750]],  # Truck image: 4 boxes
...     [[450, 170, 520, 350], [350, 190, 450, 350], [500, 170, 580, 350], [580, 170, 640, 350]]  # Groceries image: 4 boxes
... ]

>>> # Update images for this example
>>> raw_images = [raw_images[0], groceries_image]  # truck and groceries

>>> inputs = processor(images=raw_images, input_boxes=input_boxes, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
>>> print(f"Processed {len(input_boxes)} images with {len(input_boxes[0])} and {len(input_boxes[1])} boxes respectively")
Processed 2 images with 4 and 4 boxes respectively
Batched Bounding Boxespython
>>> # Multiple bounding boxes per image (using truck and groceries images)
>>> input_boxes = [
...     [[75, 275, 1725, 850], [425, 600, 700, 875], [1375, 550, 1650, 800], [1240, 675, 1400, 750]],  # Truck image: 4 boxes
...     [[450, 170, 520, 350], [350, 190, 450, 350], [500, 170, 580, 350], [580, 170, 640, 350]]  # Groceries image: 4 boxes
... ]

>>> # Update images for this example
>>> raw_images = [raw_images[0], groceries_image]  # truck and groceries

>>> inputs = processor(images=raw_images, input_boxes=input_boxes, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
>>> print(f"Processed {len(input_boxes)} images with {len(input_boxes[0])} and {len(input_boxes[1])} boxes respectively")
Processed 2 images with 4 and 4 boxes respectively
Batched Bounding Boxespython
>>> # Multiple bounding boxes per image (using truck and groceries images)
>>> input_boxes = [
...     [[75, 275, 1725, 850], [425, 600, 700, 875], [1375, 550, 1650, 800], [1240, 675, 1400, 750]],  # Truck image: 4 boxes
...     [[450, 170, 520, 350], [350, 190, 450, 350], [500, 170, 580, 350], [580, 170, 640, 350]]  # Groceries image: 4 boxes
... ]

>>> # Update images for this example
>>> raw_images = [raw_images[0], groceries_image]  # truck and groceries

>>> inputs = processor(images=raw_images, input_boxes=input_boxes, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
>>> print(f"Processed {len(input_boxes)} images with {len(input_boxes[0])} and {len(input_boxes[1])} boxes respectively")
Processed 2 images with 4 and 4 boxes respectively
Batched Bounding Boxespython
>>> # Multiple bounding boxes per image (using truck and groceries images)
>>> input_boxes = [
...     [[75, 275, 1725, 850], [425, 600, 700, 875], [1375, 550, 1650, 800], [1240, 675, 1400, 750]],  # Truck image: 4 boxes
...     [[450, 170, 520, 350], [350, 190, 450, 350], [500, 170, 580, 350], [580, 170, 640, 350]]  # Groceries image: 4 boxes
... ]

>>> # Update images for this example
>>> raw_images = [raw_images[0], groceries_image]  # truck and groceries

>>> inputs = processor(images=raw_images, input_boxes=input_boxes, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
>>> print(f"Processed {len(input_boxes)} images with {len(input_boxes[0])} and {len(input_boxes[1])} boxes respectively")
Processed 2 images with 4 and 4 boxes respectively
Batched Bounding Boxespython
>>> # Multiple bounding boxes per image (using truck and groceries images)
>>> input_boxes = [
...     [[75, 275, 1725, 850], [425, 600, 700, 875], [1375, 550, 1650, 800], [1240, 675, 1400, 750]],  # Truck image: 4 boxes
...     [[450, 170, 520, 350], [350, 190, 450, 350], [500, 170, 580, 350], [580, 170, 640, 350]]  # Groceries image: 4 boxes
... ]

>>> # Update images for this example
>>> raw_images = [raw_images[0], groceries_image]  # truck and groceries

>>> inputs = processor(images=raw_images, input_boxes=input_boxes, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
>>> print(f"Processed {len(input_boxes)} images with {len(input_boxes[0])} and {len(input_boxes[1])} boxes respectively")
Processed 2 images with 4 and 4 boxes respectively
Batched Bounding Boxespython
>>> # Multiple bounding boxes per image (using truck and groceries images)
>>> input_boxes = [
...     [[75, 275, 1725, 850], [425, 600, 700, 875], [1375, 550, 1650, 800], [1240, 675, 1400, 750]],  # Truck image: 4 boxes
...     [[450, 170, 520, 350], [350, 190, 450, 350], [500, 170, 580, 350], [580, 170, 640, 350]]  # Groceries image: 4 boxes
... ]

>>> # Update images for this example
>>> raw_images = [raw_images[0], groceries_image]  # truck and groceries

>>> inputs = processor(images=raw_images, input_boxes=input_boxes, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
>>> print(f"Processed {len(input_boxes)} images with {len(input_boxes[0])} and {len(input_boxes[1])} boxes respectively")
Processed 2 images with 4 and 4 boxes respectively
Batched Bounding Boxespython
>>> # Multiple bounding boxes per image (using truck and groceries images)
>>> input_boxes = [
...     [[75, 275, 1725, 850], [425, 600, 700, 875], [1375, 550, 1650, 800], [1240, 675, 1400, 750]],  # Truck image: 4 boxes
...     [[450, 170, 520, 350], [350, 190, 450, 350], [500, 170, 580, 350], [580, 170, 640, 350]]  # Groceries image: 4 boxes
... ]

>>> # Update images for this example
>>> raw_images = [raw_images[0], groceries_image]  # truck and groceries

>>> inputs = processor(images=raw_images, input_boxes=input_boxes, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
>>> print(f"Processed {len(input_boxes)} images with {len(input_boxes[0])} and {len(input_boxes[1])} boxes respectively")
Processed 2 images with 4 and 4 boxes respectively
Batched Bounding Boxespython
>>> # Multiple bounding boxes per image (using truck and groceries images)
>>> input_boxes = [
...     [[75, 275, 1725, 850], [425, 600, 700, 875], [1375, 550, 1650, 800], [1240, 675, 1400, 750]],  # Truck image: 4 boxes
...     [[450, 170, 520, 350], [350, 190, 450, 350], [500, 170, 580, 350], [580, 170, 640, 350]]  # Groceries image: 4 boxes
... ]

>>> # Update images for this example
>>> raw_images = [raw_images[0], groceries_image]  # truck and groceries

>>> inputs = processor(images=raw_images, input_boxes=input_boxes, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
>>> print(f"Processed {len(input_boxes)} images with {len(input_boxes[0])} and {len(input_boxes[1])} boxes respectively")
Processed 2 images with 4 and 4 boxes respectively
Batched Bounding Boxespython
>>> # Multiple bounding boxes per image (using truck and groceries images)
>>> input_boxes = [
...     [[75, 275, 1725, 850], [425, 600, 700, 875], [1375, 550, 1650, 800], [1240, 675, 1400, 750]],  # Truck image: 4 boxes
...     [[450, 170, 520, 350], [350, 190, 450, 350], [500, 170, 580, 350], [580, 170, 640, 350]]  # Groceries image: 4 boxes
... ]

>>> # Update images for this example
>>> raw_images = [raw_images[0], groceries_image]  # truck and groceries

>>> inputs = processor(images=raw_images, input_boxes=input_boxes, return_tensors="pt").to(device)

>>> with torch.no_grad():
...     outputs = model(**inputs, multimask_output=False)

>>> all_masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
>>> print(f"Processed {len(input_boxes)} images with {len(input_boxes[0])} and {len(input_boxes[1])} boxes respectively")
Processed 2 images with 4 and 4 boxes respectively

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