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# Detect Small Objects

This guide shows how to detect small objects
with the [Inference](https://github.com/roboflow/inference),
[Ultralytics](https://github.com/ultralytics/ultralytics) or
[Transformers](https://github.com/huggingface/transformers) packages using
[`InferenceSlicer`](/latest/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer).

<video controls>
    <source src="https://media.roboflow.com/supervision_detect_small_objects_example.mp4" type="video/mp4">
</video>

## Baseline Detection

Small object detection in high-resolution images presents challenges due to the objects'
size relative to the image resolution.

=== "Inference"

    ```python
    import cv2
    import supervision as sv
    from inference import get_model

    model = get_model(model_id="yolov8x-640")
    image = cv2.imread(<SOURCE_IMAGE_PATH>)
    results = model.infer(image)[0]
    detections = sv.Detections.from_inference(results)

    bounding_box_annotator = sv.BoundingBoxAnnotator()
    label_annotator = sv.LabelAnnotator()

    annotated_image = bounding_box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections)
    ```

=== "Ultralytics"

    ```python
    import cv2
    import supervision as sv
    from ultralytics import YOLO

    model = YOLO("yolov8x.pt")
    image = cv2.imread(<SOURCE_IMAGE_PATH>)
    results = model(image)[0]
    detections = sv.Detections.from_ultralytics(results)

    bounding_box_annotator = sv.BoundingBoxAnnotator()
    label_annotator = sv.LabelAnnotator()

    annotated_image = bounding_box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections)
    ```

=== "Transformers"

    ```python
    import torch
    import supervision as sv
    from PIL import Image
    from transformers import DetrImageProcessor, DetrForObjectDetection

    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")

    image = Image.open(<SOURCE_IMAGE_PATH>)
    inputs = processor(images=image, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)

    width, height = image.size
    target_size = torch.tensor([[height, width]])
    results = processor.post_process_object_detection(
        outputs=outputs, target_sizes=target_size)[0]
    detections = sv.Detections.from_transformers(results)

    bounding_box_annotator = sv.BoundingBoxAnnotator()
    label_annotator = sv.LabelAnnotator()

    labels = [
        model.config.id2label[class_id]
        for class_id
        in detections.class_id
    ]

    annotated_image = bounding_box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections, labels=labels)
    ```

![basic-detection](https://media.roboflow.com/supervision_detect_small_objects_example_1.png)

## Input Resolution

Modifying the input resolution of images before detection can enhance small object
identification at the cost of processing speed and increased memory usage. This method
is less effective for ultra-high-resolution images (4K and above).

=== "Inference"

    ```{ .py hl_lines="5" }
    import cv2
    import supervision as sv
    from inference import get_model

    model = get_model(model_id="yolov8x-1280")
    image = cv2.imread(<SOURCE_IMAGE_PATH>)
    results = model.infer(image)[0]
    detections = sv.Detections.from_inference(results)

    bounding_box_annotator = sv.BoundingBoxAnnotator()
    label_annotator = sv.LabelAnnotator()

    annotated_image = bounding_box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections)
    ```

=== "Ultralytics"

    ```{ .py hl_lines="7" }
    import cv2
    import supervision as sv
    from ultralytics import YOLO

    model = YOLO("yolov8x.pt")
    image = cv2.imread(<SOURCE_IMAGE_PATH>)
    results = model(image, imgsz=1280)[0]
    detections = sv.Detections.from_ultralytics(results)

    bounding_box_annotator = sv.BoundingBoxAnnotator()
    label_annotator = sv.LabelAnnotator()

    annotated_image = bounding_box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections)
    ```

![detection-with-high-input-resolution](https://media.roboflow.com/supervision_detect_small_objects_example_2.png)

## Inference Slicer

[`InferenceSlicer`](/latest/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer)
processes high-resolution images by dividing them into smaller segments, detecting
objects within each, and aggregating the results.

<video controls>
    <source src="https://media.roboflow.com/supervision_detect_small_objects_example_2.mp4" type="video/mp4">
</video>

=== "Inference"

    ```{ .py hl_lines="9-14" }
    import cv2
    import numpy as np
    import supervision as sv
    from inference import get_model

    model = get_model(model_id="yolov8x-640")
    image = cv2.imread(<SOURCE_IMAGE_PATH>)

    def callback(image_slice: np.ndarray) -> sv.Detections:
        results = model.infer(image_slice)[0]
        return sv.Detections.from_inference(results)

    slicer = sv.InferenceSlicer(callback = callback)
    detections = slicer(image)

    bounding_box_annotator = sv.BoundingBoxAnnotator()
    label_annotator = sv.LabelAnnotator()

    annotated_image = bounding_box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections)
    ```

=== "Ultralytics"

    ```{ .py hl_lines="9-14" }
    import cv2
    import numpy as np
    import supervision as sv
    from ultralytics import YOLO

    model = YOLO("yolov8x.pt")
    image = cv2.imread(<SOURCE_IMAGE_PATH>)

    def callback(image_slice: np.ndarray) -> sv.Detections:
        result = model(image_slice)[0]
        return sv.Detections.from_ultralytics(result)

    slicer = sv.InferenceSlicer(callback = callback)
    detections = slicer(image)

    bounding_box_annotator = sv.BoundingBoxAnnotator()
    label_annotator = sv.LabelAnnotator()

    annotated_image = bounding_box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections)
    ```

=== "Transformers"

    ```{ .py hl_lines="13-28" }
    import cv2
    import torch
    import numpy as np
    import supervision as sv
    from PIL import Image
    from transformers import DetrImageProcessor, DetrForObjectDetection

    processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
    model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")

    image = cv2.imread(<SOURCE_IMAGE_PATH>)

    def callback(image_slice: np.ndarray) -> sv.Detections:
        image_slice = cv2.cvtColor(image_slice, cv2.COLOR_BGR2RGB)
        image_slice = Image.fromarray(image_slice)
        inputs = processor(images=image_slice, return_tensors="pt")

        with torch.no_grad():
            outputs = model(**inputs)

        width, height = image.size
        target_size = torch.tensor([[height, width]])
        results = processor.post_process_object_detection(
            outputs=outputs, target_sizes=target_size)[0]
        return sv.Detections.from_transformers(results)

    slicer = sv.InferenceSlicer(callback = callback)
    detections = slicer(image)

    bounding_box_annotator = sv.BoundingBoxAnnotator()
    label_annotator = sv.LabelAnnotator()

    labels = [
        model.config.id2label[class_id]
        for class_id
        in detections.class_id
    ]

    annotated_image = bounding_box_annotator.annotate(
        scene=image, detections=detections)
    annotated_image = label_annotator.annotate(
        scene=annotated_image, detections=detections, labels=labels)
    ```

![detection-with-inference-slicer](https://media.roboflow.com/supervision_detect_small_objects_example_3.png)