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Benchmark with custom data.yaml (#3858)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Maia Numerosky 2023-07-20 20:44:46 -03:00 committed by GitHub
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3 changed files with 19 additions and 16 deletions
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9
docs/modes/benchmark.md
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@ -30,22 +30,23 @@ full list of export arguments.
from ultralytics.utils.benchmarks import benchmark
# Benchmark on GPU
benchmark(model='yolov8n.pt', imgsz=640, half=False, device=0)
benchmark(model='yolov8n.pt', data='coco8.yaml', imgsz=640, half=False, device=0)
```
=== "CLI"
```bash
yolo benchmark model=yolov8n.pt imgsz=640 half=False device=0
yolo benchmark model=yolov8n.pt data='coco8.yaml' imgsz=640 half=False device=0
```
## Arguments
Arguments such as `model`, `imgsz`, `half`, `device`, and `hard_fail` provide users with the flexibility to fine-tune
Arguments such as `model`, `data`, `imgsz`, `half`, `device`, and `hard_fail` provide users with the flexibility to fine-tune
the benchmarks to their specific needs and compare the performance of different export formats with ease.
| Key | Value | Description |
|-------------|---------|----------------------------------------------------------------------|
|-------------|---------|----------------------------------------------------------------------------|
| `model` | `None` | path to model file, i.e. yolov8n.pt, yolov8n.yaml |
| `data` | `None` | path to yaml referencing the benchmarking dataset (under `val` label) |
| `imgsz` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) |
| `half` | `False` | FP16 quantization |
| `int8` | `False` | INT8 quantization |

2
docs/usage/python.md
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@ -243,7 +243,7 @@ their specific use case based on their requirements for speed and accuracy.
from ultralytics.utils.benchmarks import benchmark
# Benchmark
benchmark(model='yolov8n.pt', imgsz=640, half=False, device=0)
benchmark(model='yolov8n.pt', data='coco8.yaml', imgsz=640, half=False, device=0)
```
[Benchmark Examples](../modes/benchmark.md){ .md-button .md-button--primary}

6
ultralytics/utils/benchmarks.py
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@ -5,7 +5,7 @@ Benchmark a YOLO model formats for speed and accuracy
Usage:
from ultralytics.utils.benchmarks import ProfileModels, benchmark
ProfileModels(['yolov8n.yaml', 'yolov8s.yaml']).profile()
run_benchmarks(model='yolov8n.pt', imgsz=160)
benchmark(model='yolov8n.pt', imgsz=160)
Format | `format=argument` | Model
--- | --- | ---
@ -44,6 +44,7 @@ from ultralytics.utils.torch_utils import select_device
def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
data=None,
imgsz=160,
half=False,
int8=False,
@ -55,6 +56,7 @@ def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
Args:
model (str | Path | optional): Path to the model file or directory. Default is
Path(SETTINGS['weights_dir']) / 'yolov8n.pt'.
data (str, optional): Dataset to evaluate on, inherited from TASK2DATA if not passed. Default is None.
imgsz (int, optional): Image size for the benchmark. Default is 160.
half (bool, optional): Use half-precision for the model if True. Default is False.
int8 (bool, optional): Use int8-precision for the model if True. Default is False.
@ -106,7 +108,7 @@ def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
export.predict(ROOT / 'assets/bus.jpg', imgsz=imgsz, device=device, half=half)
# Validate
data = TASK2DATA[model.task] # task to dataset, i.e. coco8.yaml for task=detect
data = data or TASK2DATA[model.task] # task to dataset, i.e. coco8.yaml for task=detect
key = TASK2METRIC[model.task] # task to metric, i.e. metrics/mAP50-95(B) for task=detect
results = export.val(data=data,
batch=1,