diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 8b035e26..78b6ac08 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -32,7 +32,7 @@ jobs: password: ${{ secrets.DOCKERHUB_TOKEN }} - name: Build and push arm64 image - uses: docker/build-push-action@v5 + uses: docker/build-push-action@v6 continue-on-error: true with: context: . @@ -42,7 +42,7 @@ jobs: tags: ultralytics/yolov5:latest-arm64 - name: Build and push CPU image - uses: docker/build-push-action@v5 + uses: docker/build-push-action@v6 continue-on-error: true with: context: . @@ -51,7 +51,7 @@ jobs: tags: ultralytics/yolov5:latest-cpu - name: Build and push GPU image - uses: docker/build-push-action@v5 + uses: docker/build-push-action@v6 continue-on-error: true with: context: . diff --git a/README.md b/README.md index a8712bc0..eb93c205 100644 --- a/README.md +++ b/README.md @@ -196,7 +196,7 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml - -| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW | +| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW | | :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | | Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv5 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet2) lets you save YOLOv5 models, resume training, and interactively visualise and debug predictions | Run YOLOv5 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) | @@ -406,9 +406,7 @@ python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg ``` ```python -model = torch.hub.load( - "ultralytics/yolov5", "custom", "yolov5s-cls.pt" -) # load from PyTorch Hub +model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s-cls.pt") # load from PyTorch Hub ``` ### Export diff --git a/README.zh-CN.md b/README.zh-CN.md index c81feb86..c815148f 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -394,9 +394,7 @@ python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg ``` ```python -model = torch.hub.load( - "ultralytics/yolov5", "custom", "yolov5s-cls.pt" -) # load from PyTorch Hub +model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s-cls.pt") # load from PyTorch Hub ``` ### 模型导出 diff --git a/utils/loggers/clearml/README.md b/utils/loggers/clearml/README.md index bc40919a..473f8147 100644 --- a/utils/loggers/clearml/README.md +++ b/utils/loggers/clearml/README.md @@ -34,15 +34,15 @@ Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-t 1. Install the `clearml` python package: - ```bash - pip install clearml - ``` + ```bash + pip install clearml + ``` 2. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions: - ```bash - clearml-init - ``` + ```bash + clearml-init + ``` That's it! You're done 😎 diff --git a/utils/segment/dataloaders.py b/utils/segment/dataloaders.py index b0b3a742..e6bfaee4 100644 --- a/utils/segment/dataloaders.py +++ b/utils/segment/dataloaders.py @@ -7,7 +7,7 @@ import cv2 import numpy as np import torch -from torch.utils.data import DataLoader, distributed +from torch.utils.data import DataLoader from ..augmentations import augment_hsv, copy_paste, letterbox from ..dataloaders import InfiniteDataLoader, LoadImagesAndLabels, SmartDistributedSampler, seed_worker