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@ -36,7 +36,7 @@ MS COCO
Docker environment (recommended)
<details><summary> <b>Expand</b> </summary>
``` bash
``` shell
# create the docker container, you can change the share memory size if you have more.
nvidia-docker run --name yolov7 -it -v your_coco_path/:/coco/ -v your_code_path/:/yolov7 --shm-size=64g nvcr.io/nvidia/pytorch:21.08-py3
@ -57,7 +57,7 @@ cd /yolov7
[`yolov7.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) [`yolov7x.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) [`yolov7-w6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) [`yolov7-e6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) [`yolov7-d6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) [`yolov7-e6e.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt)
``` bash
``` shell
python test.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.65 --device 0 --weights yolov7.pt --name yolov7_640_val
```
@ -84,7 +84,7 @@ To measure accuracy, download [COCO-annotations for Pycocotools](http://images.c
Data preparation
``` bash
``` shell
bash scripts/get_coco.sh
```
@ -92,7 +92,7 @@ bash scripts/get_coco.sh
Single GPU training
``` bash
``` shell
# train p5 models
python train.py --workers 8 --device 0 --batch-size 32 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml
@ -102,7 +102,7 @@ python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/coco.yaml
Multiple GPU training
``` bash
``` shell
# train p5 models
python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.py --workers 8 --device 0,1,2,3 --sync-bn --batch-size 128 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml
@ -116,7 +116,7 @@ python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train_a
Single GPU finetuning for custom dataset
``` bash
``` shell
# finetune p5 models
python train.py --workers 8 --device 0 --batch-size 32 --data data/custom.yaml --img 640 640 --cfg cfg/training/yolov7-custom.yaml --weights 'yolov7_training.pt' --name yolov7-custom --hyp data/hyp.scratch.custom.yaml
@ -137,11 +137,12 @@ See [keypoint.ipynb](https://github.com/WongKinYiu/yolov7/blob/main/tools/keypoi
## Inference
On video:
``` bash
``` shell
python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source yourvideo.mp4
```
On image:
``` bash
``` shell
python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg
```