Burhan
2 months ago
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8 changed files with 31 additions and 30 deletions
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| Argument | Type | Default | Description | |
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| --------------- | -------------- | ---------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | |
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| `source` | `str` | `'ultralytics/assets'` | Specifies the data source for inference. Can be an image path, video file, directory, URL, or device ID for live feeds. Supports a wide range of formats and sources, enabling flexible application across [different types of input](/modes/predict.md/#inference-sources). | |
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| `conf` | `float` | `0.25` | Sets the minimum confidence threshold for detections. Objects detected with confidence below this threshold will be disregarded. Adjusting this value can help reduce false positives. | |
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| `iou` | `float` | `0.7` | [Intersection Over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou) (IoU) threshold for Non-Maximum Suppression (NMS). Lower values result in fewer detections by eliminating overlapping boxes, useful for reducing duplicates. | |
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| `imgsz` | `int or tuple` | `640` | Defines the image size for inference. Can be a single integer `640` for square resizing or a (height, width) tuple. Proper sizing can improve detection [accuracy](https://www.ultralytics.com/glossary/accuracy) and processing speed. | |
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| `half` | `bool` | `False` | Enables half-[precision](https://www.ultralytics.com/glossary/precision) (FP16) inference, which can speed up model inference on supported GPUs with minimal impact on accuracy. | |
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| `device` | `str` | `None` | Specifies the device for inference (e.g., `cpu`, `cuda:0` or `0`). Allows users to select between CPU, a specific GPU, or other compute devices for model execution. | |
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| `max_det` | `int` | `300` | Maximum number of detections allowed per image. Limits the total number of objects the model can detect in a single inference, preventing excessive outputs in dense scenes. | |
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| `vid_stride` | `int` | `1` | Frame stride for video inputs. Allows skipping frames in videos to speed up processing at the cost of temporal resolution. A value of 1 processes every frame, higher values skip frames. | |
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| `stream_buffer` | `bool` | `False` | Determines the frame processing strategy for video streams. If `False` processing only the most recent frame, minimizing latency (optimized for real-time applications). If `True' processes all frames in order, ensuring no frames are skipped. | |
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| `visualize` | `bool` | `False` | Activates visualization of model features during inference, providing insights into what the model is "seeing". Useful for debugging and model interpretation. | |
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| `augment` | `bool` | `False` | Enables test-time augmentation (TTA) for predictions, potentially improving detection robustness at the cost of inference speed. | |
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| `agnostic_nms` | `bool` | `False` | Enables class-agnostic Non-Maximum Suppression (NMS), which merges overlapping boxes of different classes. Useful in multi-class detection scenarios where class overlap is common. | |
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| `classes` | `list[int]` | `None` | Filters predictions to a set of class IDs. Only detections belonging to the specified classes will be returned. Useful for focusing on relevant objects in multi-class detection tasks. | |
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| `retina_masks` | `bool` | `False` | Uses high-resolution segmentation masks if available in the model. This can enhance mask quality for segmentation tasks, providing finer detail. | |
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| `embed` | `list[int]` | `None` | Specifies the layers from which to extract feature vectors or [embeddings](https://www.ultralytics.com/glossary/embeddings). Useful for downstream tasks like clustering or similarity search. | |
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| Argument | Type | Default | Description | |
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| --------------- | -------------- | ---------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | |
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| `source` | `str` | `'ultralytics/assets'` | Specifies the data source for inference. Can be an image path, video file, directory, URL, or device ID for live feeds. Supports a wide range of formats and sources, enabling flexible application across [different types of input](/modes/predict.md/#inference-sources). | |
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| `conf` | `float` | `0.25` | Sets the minimum confidence threshold for detections. Objects detected with confidence below this threshold will be disregarded. Adjusting this value can help reduce false positives. | |
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| `iou` | `float` | `0.7` | [Intersection Over Union](https://www.ultralytics.com/glossary/intersection-over-union-iou) (IoU) threshold for Non-Maximum Suppression (NMS). Lower values result in fewer detections by eliminating overlapping boxes, useful for reducing duplicates. | |
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| `imgsz` | `int or tuple` | `640` | Defines the image size for inference. Can be a single integer `640` for square resizing or a (height, width) tuple. Proper sizing can improve detection [accuracy](https://www.ultralytics.com/glossary/accuracy) and processing speed. | |
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| `half` | `bool` | `False` | Enables half-[precision](https://www.ultralytics.com/glossary/precision) (FP16) inference, which can speed up model inference on supported GPUs with minimal impact on accuracy. | |
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| `device` | `str` | `None` | Specifies the device for inference (e.g., `cpu`, `cuda:0` or `0`). Allows users to select between CPU, a specific GPU, or other compute devices for model execution. | |
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| `max_det` | `int` | `300` | Maximum number of detections allowed per image. Limits the total number of objects the model can detect in a single inference, preventing excessive outputs in dense scenes. | |
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| `vid_stride` | `int` | `1` | Frame stride for video inputs. Allows skipping frames in videos to speed up processing at the cost of temporal resolution. A value of 1 processes every frame, higher values skip frames. | |
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| `stream_buffer` | `bool` | `False` | Determines whether to queue incoming frames for video streams. If `False`, old frames get dropped to accomodate new frames (optimized for real-time applications). If `True', queues new frames in a buffer, ensuring no frames get skipped, but will cause latency if inference FPS is lower than stream FPS. | |
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| `visualize` | `bool` | `False` | Activates visualization of model features during inference, providing insights into what the model is "seeing". Useful for debugging and model interpretation. | |
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| `augment` | `bool` | `False` | Enables test-time augmentation (TTA) for predictions, potentially improving detection robustness at the cost of inference speed. | |
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| `agnostic_nms` | `bool` | `False` | Enables class-agnostic Non-Maximum Suppression (NMS), which merges overlapping boxes of different classes. Useful in multi-class detection scenarios where class overlap is common. | |
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| `classes` | `list[int]` | `None` | Filters predictions to a set of class IDs. Only detections belonging to the specified classes will be returned. Useful for focusing on relevant objects in multi-class detection tasks. | |
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| `retina_masks` | `bool` | `False` | Uses high-resolution segmentation masks if available in the model. This can enhance mask quality for segmentation tasks, providing finer detail. | |
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| `embed` | `list[int]` | `None` | Specifies the layers from which to extract feature vectors or [embeddings](https://www.ultralytics.com/glossary/embeddings). Useful for downstream tasks like clustering or similarity search. | |
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|
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