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ultralytics 8.0.196 instance-mean Segment loss (#5285)

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Co-authored-by: Andy <39454881+yermandy@users.noreply.github.com>
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Glenn Jocher 2023-10-09 20:08:39 +02:00 • committed by GitHub
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docs/yolov5/quickstart_tutorial.md
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@ -22,8 +22,7 @@ pip install -r requirements.txt # install
## Inference
YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest
YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
```python
import torch
@ -43,8 +42,7 @@ results.print() # or .show(), .save(), .crop(), .pandas(), etc.
## Inference with detect.py
`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from
the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
```bash
python detect.py --weights yolov5s.pt --source 0 # webcam
@ -63,11 +61,7 @@ python detect.py --weights yolov5s.pt --source 0 #
The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
results. [Models](https://github.com/ultralytics/yolov5/tree/master/models)
and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest
YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are
1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) times faster). Use the
largest `--batch-size` possible, or pass `--batch-size -1` for
YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.
and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) times faster). Use the largest `--batch-size` possible, or pass `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.
```bash
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128