Docs Prettier reformat (#13483)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher
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90 changed files with 763 additions and 742 deletions
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@ -65,7 +65,7 @@ To install the required package, run:
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!!! Tip "Installation"
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=== "CLI"
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```bash
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# Install the required package for YOLOv8
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pip install ultralytics
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@ -139,7 +139,7 @@ The arguments provided when using [export](../modes/export.md) for an Ultralytic
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!!! note
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During calibration, twice the `batch` size provided will be used. Using small batches can lead to inaccurate scaling during calibration. This is because the process adjusts based on the data it sees. Small batches might not capture the full range of values, leading to issues with the final calibration, so the `batch` size is doubled automatically. If no batch size is specified `batch=1`, calibration will be run at `batch=1 * 2` to reduce calibration scaling errors.
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During calibration, twice the `batch` size provided will be used. Using small batches can lead to inaccurate scaling during calibration. This is because the process adjusts based on the data it sees. Small batches might not capture the full range of values, leading to issues with the final calibration, so the `batch` size is doubled automatically. If no batch size is specified `batch=1`, calibration will be run at `batch=1 * 2` to reduce calibration scaling errors.
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Experimentation by NVIDIA led them to recommend using at least 500 calibration images that are representative of the data for your model, with INT8 quantization calibration. This is a guideline and not a _hard_ requirement, and <u>**you will need to experiment with what is required to perform well for your dataset**.</u> Since the calibration data is required for INT8 calibration with TensorRT, make certain to use the `data` argument when `int8=True` for TensorRT and use `data="my_dataset.yaml"`, which will use the images from [validation](../modes/val.md) to calibrate with. When no value is passed for `data` with export to TensorRT with INT8 quantization, the default will be to use one of the ["small" example datasets based on the model task](../datasets/index.md) instead of throwing an error.
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@ -166,13 +166,13 @@ Experimentation by NVIDIA led them to recommend using at least 500 calibration i
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# Run inference
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result = model.predict("https://ultralytics.com/images/bus.jpg")
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```
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1. Exports with dynamic axes, this will be enabled by default when exporting with `int8=True` even when not explicitly set. See [export arguments](../modes/export.md#arguments) for additional information.
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2. Sets max batch size of 8 for exported model, which calibrates with `batch = 2 * 8` to avoid scaling errors during calibration.
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3. Allocates 4 GiB of memory instead of allocating the entire device for conversion process.
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4. Uses [COCO dataset](../datasets/detect/coco.md) for calibration, specifically the images used for [validation](../modes/val.md) (5,000 total).
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=== "CLI"
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```bash
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@ -219,7 +219,7 @@ Experimentation by NVIDIA led them to recommend using at least 500 calibration i
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See [Detection Docs](../tasks/detect.md) for usage examples with these models trained on [COCO](../datasets/detect/coco.md), which include 80 pre-trained classes.
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!!! note
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!!! note
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Inference times shown for `mean`, `min` (fastest), and `max` (slowest) for each test using pre-trained weights `yolov8n.engine`
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| Precision | Eval test | mean<br>(ms) | min \| max<br>(ms) | mAP<sup>val<br>50(B) | mAP<sup>val<br>50-95(B) | `batch` | size<br><sup>(pixels) |
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@ -234,8 +234,8 @@ Experimentation by NVIDIA led them to recommend using at least 500 calibration i
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=== "Segmentation (COCO)"
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See [Segmentation Docs](../tasks/segment.md) for usage examples with these models trained on [COCO](../datasets/segment/coco.md), which include 80 pre-trained classes.
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!!! note
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!!! note
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Inference times shown for `mean`, `min` (fastest), and `max` (slowest) for each test using pre-trained weights `yolov8n-seg.engine`
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| Precision | Eval test | mean<br>(ms) | min \| max<br>(ms) | mAP<sup>val<br>50(B) | mAP<sup>val<br>50-95(B) | mAP<sup>val<br>50(M) | mAP<sup>val<br>50-95(M) | `batch` | size<br><sup>(pixels) |
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@ -251,7 +251,7 @@ Experimentation by NVIDIA led them to recommend using at least 500 calibration i
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See [Classification Docs](../tasks/classify.md) for usage examples with these models trained on [ImageNet](../datasets/classify/imagenet.md), which include 1000 pre-trained classes.
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!!! note
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!!! note
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Inference times shown for `mean`, `min` (fastest), and `max` (slowest) for each test using pre-trained weights `yolov8n-cls.engine`
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| Precision | Eval test | mean<br>(ms) | min \| max<br>(ms) | top-1 | top-5 | `batch` | size<br><sup>(pixels) |
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@ -267,7 +267,7 @@ Experimentation by NVIDIA led them to recommend using at least 500 calibration i
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See [Pose Estimation Docs](../tasks/pose.md) for usage examples with these models trained on [COCO](../datasets/pose/coco.md), which include 1 pre-trained class, "person".
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!!! note
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!!! note
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Inference times shown for `mean`, `min` (fastest), and `max` (slowest) for each test using pre-trained weights `yolov8n-pose.engine`
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| Precision | Eval test | mean<br>(ms) | min \| max<br>(ms) | mAP<sup>val<br>50(B) | mAP<sup>val<br>50-95(B) | mAP<sup>val<br>50(P) | mAP<sup>val<br>50-95(P) | `batch` | size<br><sup>(pixels) |
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@ -283,7 +283,7 @@ Experimentation by NVIDIA led them to recommend using at least 500 calibration i
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See [Oriented Detection Docs](../tasks/obb.md) for usage examples with these models trained on [DOTAv1](../datasets/obb/dota-v2.md#dota-v10), which include 15 pre-trained classes.
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!!! note
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!!! note
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Inference times shown for `mean`, `min` (fastest), and `max` (slowest) for each test using pre-trained weights `yolov8n-obb.engine`
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| Precision | Eval test | mean<br>(ms) | min \| max<br>(ms) | mAP<sup>val<br>50(B) | mAP<sup>val<br>50-95(B) | `batch` | size<br><sup>(pixels) |
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@ -303,7 +303,7 @@ Experimentation by NVIDIA led them to recommend using at least 500 calibration i
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Tested with Windows 10.0.19045, `python 3.10.9`, `ultralytics==8.2.4`, `tensorrt==10.0.0b6`
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!!! note
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!!! note
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Inference times shown for `mean`, `min` (fastest), and `max` (slowest) for each test using pre-trained weights `yolov8n.engine`
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| Precision | Eval test | mean<br>(ms) | min \| max<br>(ms) | mAP<sup>val<br>50(B) | mAP<sup>val<br>50-95(B) | `batch` | size<br><sup>(pixels) |
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@ -318,8 +318,8 @@ Experimentation by NVIDIA led them to recommend using at least 500 calibration i
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=== "RTX 3060 12 GB"
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Tested with Windows 10.0.22631, `python 3.11.9`, `ultralytics==8.2.4`, `tensorrt==10.0.1`
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!!! note
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!!! note
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Inference times shown for `mean`, `min` (fastest), and `max` (slowest) for each test using pre-trained weights `yolov8n.engine`
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@ -336,7 +336,7 @@ Experimentation by NVIDIA led them to recommend using at least 500 calibration i
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Tested with Pop!_OS 22.04 LTS, `python 3.10.12`, `ultralytics==8.2.4`, `tensorrt==8.6.1.post1`
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!!! note
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!!! note
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Inference times shown for `mean`, `min` (fastest), and `max` (slowest) for each test using pre-trained weights `yolov8n.engine`
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| Precision | Eval test | mean<br>(ms) | min \| max<br>(ms) | mAP<sup>val<br>50(B) | mAP<sup>val<br>50-95(B) | `batch` | size<br><sup>(pixels) |
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@ -356,7 +356,7 @@ Experimentation by NVIDIA led them to recommend using at least 500 calibration i
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Tested with JetPack 6.0 (L4T 36.3) Ubuntu 22.04.4 LTS, `python 3.10.12`, `ultralytics==8.2.16`, `tensorrt==10.0.1`
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!!! note
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!!! note
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Inference times shown for `mean`, `min` (fastest), and `max` (slowest) for each test using pre-trained weights `yolov8n.engine`
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| Precision | Eval test | mean<br>(ms) | min \| max<br>(ms) | mAP<sup>val<br>50(B) | mAP<sup>val<br>50-95(B) | `batch` | size<br><sup>(pixels) |
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