diff --git a/docker/Dockerfile-arm64 b/docker/Dockerfile-arm64
index 19fa238a..d9ec7529 100644
--- a/docker/Dockerfile-arm64
+++ b/docker/Dockerfile-arm64
@@ -34,7 +34,7 @@ RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED
# Install pip packages
# Install tensorstore from .whl because PyPI does not include aarch64 binaries
RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache-dir https://github.com/ultralytics/yolov5/releases/download/v1.0/tensorstore-0.1.59-cp311-cp311-linux_aarch64.whl -e ".[export]"
+RUN pip install --no-cache-dir https://github.com/ultralytics/assets/releases/download/v0.0.0/tensorstore-0.1.59-cp311-cp311-linux_aarch64.whl -e ".[export]"
# Creates a symbolic link to make 'python' point to 'python3'
RUN ln -sf /usr/bin/python3 /usr/bin/python
diff --git a/docker/Dockerfile-jetson-jetpack4 b/docker/Dockerfile-jetson-jetpack4
index 8c646823..12931ad3 100644
--- a/docker/Dockerfile-jetson-jetpack4
+++ b/docker/Dockerfile-jetson-jetpack4
@@ -38,9 +38,9 @@ ADD https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt $A
# Other versions can be seen in https://elinux.org/Jetson_Zoo and https://forums.developer.nvidia.com/t/pytorch-for-jetson/72048
ADD https://nvidia.box.com/shared/static/gjqofg7rkg97z3gc8jeyup6t8n9j8xjw.whl onnxruntime_gpu-1.8.0-cp38-cp38-linux_aarch64.whl
ADD https://forums.developer.nvidia.com/uploads/short-url/hASzFOm9YsJx6VVFrDW1g44CMmv.whl tensorrt-8.2.0.6-cp38-none-linux_aarch64.whl
-ADD https://github.com/ultralytics/yolov5/releases/download/v1.0/torch-1.11.0a0+gitbc2c6ed-cp38-cp38-linux_aarch64.whl \
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/torch-1.11.0a0+gitbc2c6ed-cp38-cp38-linux_aarch64.whl \
torch-1.11.0a0+gitbc2c6ed-cp38-cp38-linux_aarch64.whl
-ADD https://github.com/ultralytics/yolov5/releases/download/v1.0/torchvision-0.12.0a0+9b5a3fe-cp38-cp38-linux_aarch64.whl \
+ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/torchvision-0.12.0a0+9b5a3fe-cp38-cp38-linux_aarch64.whl \
torchvision-0.12.0a0+9b5a3fe-cp38-cp38-linux_aarch64.whl
# Install pip packages
@@ -61,4 +61,4 @@ RUN pip install --no-cache-dir -e ".[export]"
# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker pull $t && sudo docker run -it --ipc=host $t
# Pull and Run with NVIDIA runtime
-# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t
\ No newline at end of file
+# t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t
diff --git a/docs/en/datasets/classify/imagenet10.md b/docs/en/datasets/classify/imagenet10.md
index 35b85b2e..d7bf55e4 100644
--- a/docs/en/datasets/classify/imagenet10.md
+++ b/docs/en/datasets/classify/imagenet10.md
@@ -6,7 +6,7 @@ keywords: ImageNet10, ImageNet, Ultralytics, CI tests, sanity checks, training p
# ImageNet10 Dataset
-The [ImageNet10](https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenet10.zip) dataset is a small-scale subset of the [ImageNet](https://www.image-net.org/) database, developed by [Ultralytics](https://ultralytics.com) and designed for CI tests, sanity checks, and fast testing of training pipelines. This dataset is composed of the first image in the training set and the first image from the validation set of the first 10 classes in ImageNet. Although significantly smaller, it retains the structure and diversity of the original ImageNet dataset.
+The [ImageNet10](https://github.com/ultralytics/assets/releases/download/v0.0.0/imagenet10.zip) dataset is a small-scale subset of the [ImageNet](https://www.image-net.org/) database, developed by [Ultralytics](https://ultralytics.com) and designed for CI tests, sanity checks, and fast testing of training pipelines. This dataset is composed of the first image in the training set and the first image from the validation set of the first 10 classes in ImageNet. Although significantly smaller, it retains the structure and diversity of the original ImageNet dataset.
## Key Features
@@ -80,7 +80,7 @@ We would like to acknowledge the ImageNet team, led by Olga Russakovsky, Jia Den
### What is the ImageNet10 dataset and how is it different from the full ImageNet dataset?
-The [ImageNet10](https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenet10.zip) dataset is a compact subset of the original [ImageNet](https://www.image-net.org/) database, created by Ultralytics for rapid CI tests, sanity checks, and training pipeline evaluations. ImageNet10 comprises only 20 images, representing the first image in the training and validation sets of the first 10 classes in ImageNet. Despite its small size, it maintains the structure and diversity of the full dataset, making it ideal for quick testing but not for benchmarking models.
+The [ImageNet10](https://github.com/ultralytics/assets/releases/download/v0.0.0/imagenet10.zip) dataset is a compact subset of the original [ImageNet](https://www.image-net.org/) database, created by Ultralytics for rapid CI tests, sanity checks, and training pipeline evaluations. ImageNet10 comprises only 20 images, representing the first image in the training and validation sets of the first 10 classes in ImageNet. Despite its small size, it maintains the structure and diversity of the full dataset, making it ideal for quick testing but not for benchmarking models.
### How can I use the ImageNet10 dataset to test my deep learning model?
@@ -124,4 +124,4 @@ The ImageNet10 dataset has several key features:
### Where can I download the ImageNet10 dataset?
-You can download the ImageNet10 dataset from the [Ultralytics GitHub releases page](https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenet10.zip). For more detailed information about its structure and applications, refer to the [ImageNet10 Dataset](imagenet10.md) page.
+You can download the ImageNet10 dataset from the [Ultralytics GitHub releases page](https://github.com/ultralytics/assets/releases/download/v0.0.0/imagenet10.zip). For more detailed information about its structure and applications, refer to the [ImageNet10 Dataset](imagenet10.md) page.
diff --git a/docs/en/integrations/tensorrt.md b/docs/en/integrations/tensorrt.md
index f04fa5a6..e3316748 100644
--- a/docs/en/integrations/tensorrt.md
+++ b/docs/en/integrations/tensorrt.md
@@ -13,7 +13,7 @@ By using the TensorRT export format, you can enhance your [Ultralytics YOLOv8](h
## TensorRT
- 
+ 
[TensorRT](https://developer.nvidia.com/tensorrt), developed by NVIDIA, is an advanced software development kit (SDK) designed for high-speed deep learning inference. It's well-suited for real-time applications like object detection.
diff --git a/docs/en/yolov5/tutorials/multi_gpu_training.md b/docs/en/yolov5/tutorials/multi_gpu_training.md
index f61ffc93..4a51007e 100644
--- a/docs/en/yolov5/tutorials/multi_gpu_training.md
+++ b/docs/en/yolov5/tutorials/multi_gpu_training.md
@@ -24,7 +24,7 @@ pip install -r requirements.txt # install
Select a pretrained model to start training from. Here we select [YOLOv5s](https://github.com/ultralytics/yolov5/blob/master/models/yolov5s.yaml), the smallest and fastest model available. See our README [table](https://github.com/ultralytics/yolov5#pretrained-checkpoints) for a full comparison of all models. We will train this model with Multi-GPU on the [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset.
-
+
### Single GPU
diff --git a/docs/en/yolov5/tutorials/tips_for_best_training_results.md b/docs/en/yolov5/tutorials/tips_for_best_training_results.md
index f9254e72..8f1b27a2 100644
--- a/docs/en/yolov5/tutorials/tips_for_best_training_results.md
+++ b/docs/en/yolov5/tutorials/tips_for_best_training_results.md
@@ -28,7 +28,7 @@ We've put together a full guide for users looking to get the best results on the
Larger models like YOLOv5x and [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/tag/v5.0) will produce better results in nearly all cases, but have more parameters, require more CUDA memory to train, and are slower to run. For **mobile** deployments we recommend YOLOv5s/m, for **cloud** deployments we recommend YOLOv5l/x. See our README [table](https://github.com/ultralytics/yolov5#pretrained-checkpoints) for a full comparison of all models.
-
+
- **Start from Pretrained weights.** Recommended for small to medium-sized datasets (i.e. [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml)). Pass the name of the model to the `--weights` argument. Models download automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases).
diff --git a/docs/en/yolov5/tutorials/train_custom_data.md b/docs/en/yolov5/tutorials/train_custom_data.md
index 1b9e47bc..6042a1e1 100644
--- a/docs/en/yolov5/tutorials/train_custom_data.md
+++ b/docs/en/yolov5/tutorials/train_custom_data.md
@@ -127,7 +127,7 @@ Organize your train and val images and labels according to the example below. YO
Select a pretrained model to start training from. Here we select [YOLOv5s](https://github.com/ultralytics/yolov5/blob/master/models/yolov5s.yaml), the second-smallest and fastest model available. See our README [table](https://github.com/ultralytics/yolov5#pretrained-checkpoints) for a full comparison of all models.
-
+
## 4. Train
@@ -185,7 +185,7 @@ Training results are automatically logged with [Tensorboard](https://www.tensorf
This directory contains train and val statistics, mosaics, labels, predictions and augmented mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices.
-
+
Results file `results.csv` is updated after each epoch, and then plotted as `results.png` (below) after training completes. You can also plot any `results.csv` file manually:
@@ -195,7 +195,7 @@ from utils.plots import plot_results
plot_results("path/to/results.csv") # plot 'results.csv' as 'results.png'
```
-
+
## Next Steps
diff --git a/tests/test_python.py b/tests/test_python.py
index 6f2aba97..97441a78 100644
--- a/tests/test_python.py
+++ b/tests/test_python.py
@@ -90,7 +90,7 @@ def test_predict_img(model_name):
batch = [
str(SOURCE), # filename
Path(SOURCE), # Path
- "https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg" if ONLINE else SOURCE, # URI
+ "https://github.com/ultralytics/assets/releases/download/v0.0.0/zidane.jpg" if ONLINE else SOURCE, # URI
cv2.imread(str(SOURCE)), # OpenCV
Image.open(SOURCE), # PIL
np.zeros((320, 640, 3), dtype=np.uint8), # numpy
@@ -149,7 +149,7 @@ def test_track_stream():
Note imgsz=160 required for tracking for higher confidence and better matches.
"""
- video_url = "https://github.com/ultralytics/yolov5/releases/download/v1.0/decelera_portrait_min.mov"
+ video_url = "https://github.com/ultralytics/assets/releases/download/v0.0.0/decelera_portrait_min.mov"
model = YOLO(MODEL)
model.track(video_url, imgsz=160, tracker="bytetrack.yaml")
model.track(video_url, imgsz=160, tracker="botsort.yaml", save_frames=True) # test frame saving also
@@ -290,7 +290,7 @@ def test_data_converter():
from ultralytics.data.converter import coco80_to_coco91_class, convert_coco
file = "instances_val2017.json"
- download(f"https://github.com/ultralytics/yolov5/releases/download/v1.0/{file}", dir=TMP)
+ download(f"https://github.com/ultralytics/assets/releases/download/v0.0.0/{file}", dir=TMP)
convert_coco(labels_dir=TMP, save_dir=TMP / "yolo_labels", use_segments=True, use_keypoints=False, cls91to80=True)
coco80_to_coco91_class()
diff --git a/ultralytics/cfg/datasets/DOTAv1.5.yaml b/ultralytics/cfg/datasets/DOTAv1.5.yaml
index 701535fc..b59ff881 100644
--- a/ultralytics/cfg/datasets/DOTAv1.5.yaml
+++ b/ultralytics/cfg/datasets/DOTAv1.5.yaml
@@ -33,4 +33,4 @@ names:
15: container crane
# Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/DOTAv1.5.zip
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/DOTAv1.5.zip
diff --git a/ultralytics/cfg/datasets/DOTAv1.yaml b/ultralytics/cfg/datasets/DOTAv1.yaml
index f6364d34..d1c950b9 100644
--- a/ultralytics/cfg/datasets/DOTAv1.yaml
+++ b/ultralytics/cfg/datasets/DOTAv1.yaml
@@ -32,4 +32,4 @@ names:
14: swimming pool
# Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/DOTAv1.zip
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/DOTAv1.zip
diff --git a/ultralytics/cfg/datasets/GlobalWheat2020.yaml b/ultralytics/cfg/datasets/GlobalWheat2020.yaml
index ae6bfa0d..95749a11 100644
--- a/ultralytics/cfg/datasets/GlobalWheat2020.yaml
+++ b/ultralytics/cfg/datasets/GlobalWheat2020.yaml
@@ -37,7 +37,7 @@ download: |
# Download
dir = Path(yaml['path']) # dataset root dir
urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
- 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
+ 'https://github.com/ultralytics/assets/releases/download/v0.0.0/GlobalWheat2020_labels.zip']
download(urls, dir=dir)
# Make Directories
diff --git a/ultralytics/cfg/datasets/VOC.yaml b/ultralytics/cfg/datasets/VOC.yaml
index cd6d5ade..7311d891 100644
--- a/ultralytics/cfg/datasets/VOC.yaml
+++ b/ultralytics/cfg/datasets/VOC.yaml
@@ -76,7 +76,7 @@ download: |
# Download
dir = Path(yaml['path']) # dataset root dir
- url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
+ url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
diff --git a/ultralytics/cfg/datasets/VisDrone.yaml b/ultralytics/cfg/datasets/VisDrone.yaml
index 773f0b08..9c28d918 100644
--- a/ultralytics/cfg/datasets/VisDrone.yaml
+++ b/ultralytics/cfg/datasets/VisDrone.yaml
@@ -61,10 +61,10 @@ download: |
# Download
dir = Path(yaml['path']) # dataset root dir
- urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
- 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
- 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
- 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
+ urls = ['https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-train.zip',
+ 'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-val.zip',
+ 'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-dev.zip',
+ 'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-challenge.zip']
download(urls, dir=dir, curl=True, threads=4)
# Convert
diff --git a/ultralytics/cfg/datasets/african-wildlife.yaml b/ultralytics/cfg/datasets/african-wildlife.yaml
index 95feeb81..eaccb1a8 100644
--- a/ultralytics/cfg/datasets/african-wildlife.yaml
+++ b/ultralytics/cfg/datasets/african-wildlife.yaml
@@ -21,4 +21,4 @@ names:
3: zebra
# Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/african-wildlife.zip
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/african-wildlife.zip
diff --git a/ultralytics/cfg/datasets/brain-tumor.yaml b/ultralytics/cfg/datasets/brain-tumor.yaml
index 4e582dfe..115532a3 100644
--- a/ultralytics/cfg/datasets/brain-tumor.yaml
+++ b/ultralytics/cfg/datasets/brain-tumor.yaml
@@ -19,4 +19,4 @@ names:
1: positive
# Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/brain-tumor.zip
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/brain-tumor.zip
diff --git a/ultralytics/cfg/datasets/carparts-seg.yaml b/ultralytics/cfg/datasets/carparts-seg.yaml
index 433f968d..d15da6e5 100644
--- a/ultralytics/cfg/datasets/carparts-seg.yaml
+++ b/ultralytics/cfg/datasets/carparts-seg.yaml
@@ -40,4 +40,4 @@ names:
22: wheel
# Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/carparts-seg.zip
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/carparts-seg.zip
diff --git a/ultralytics/cfg/datasets/coco-pose.yaml b/ultralytics/cfg/datasets/coco-pose.yaml
index b50b7a5b..7d71c83d 100644
--- a/ultralytics/cfg/datasets/coco-pose.yaml
+++ b/ultralytics/cfg/datasets/coco-pose.yaml
@@ -28,7 +28,7 @@ download: |
# Download labels
dir = Path(yaml['path']) # dataset root dir
- url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
+ url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
urls = [url + 'coco2017labels-pose.zip'] # labels
download(urls, dir=dir.parent)
# Download data
diff --git a/ultralytics/cfg/datasets/coco.yaml b/ultralytics/cfg/datasets/coco.yaml
index d0297f76..3bb9aacc 100644
--- a/ultralytics/cfg/datasets/coco.yaml
+++ b/ultralytics/cfg/datasets/coco.yaml
@@ -104,7 +104,7 @@ download: |
# Download labels
segments = True # segment or box labels
dir = Path(yaml['path']) # dataset root dir
- url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
+ url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels
download(urls, dir=dir.parent)
# Download data
diff --git a/ultralytics/cfg/datasets/coco128-seg.yaml b/ultralytics/cfg/datasets/coco128-seg.yaml
index 4218b3d2..dcd961c6 100644
--- a/ultralytics/cfg/datasets/coco128-seg.yaml
+++ b/ultralytics/cfg/datasets/coco128-seg.yaml
@@ -97,4 +97,4 @@ names:
79: toothbrush
# Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128-seg.zip
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128-seg.zip
diff --git a/ultralytics/cfg/datasets/coco128.yaml b/ultralytics/cfg/datasets/coco128.yaml
index fde15852..1b515592 100644
--- a/ultralytics/cfg/datasets/coco128.yaml
+++ b/ultralytics/cfg/datasets/coco128.yaml
@@ -97,4 +97,4 @@ names:
79: toothbrush
# Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128.zip
diff --git a/ultralytics/cfg/datasets/coco8-pose.yaml b/ultralytics/cfg/datasets/coco8-pose.yaml
index 2e08daa1..68678fa7 100644
--- a/ultralytics/cfg/datasets/coco8-pose.yaml
+++ b/ultralytics/cfg/datasets/coco8-pose.yaml
@@ -22,4 +22,4 @@ names:
0: person
# Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco8-pose.zip
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8-pose.zip
diff --git a/ultralytics/cfg/datasets/coco8-seg.yaml b/ultralytics/cfg/datasets/coco8-seg.yaml
index a3fe2e43..42fc02b0 100644
--- a/ultralytics/cfg/datasets/coco8-seg.yaml
+++ b/ultralytics/cfg/datasets/coco8-seg.yaml
@@ -97,4 +97,4 @@ names:
79: toothbrush
# Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco8-seg.zip
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8-seg.zip
diff --git a/ultralytics/cfg/datasets/coco8.yaml b/ultralytics/cfg/datasets/coco8.yaml
index 58afea94..50a1133c 100644
--- a/ultralytics/cfg/datasets/coco8.yaml
+++ b/ultralytics/cfg/datasets/coco8.yaml
@@ -97,4 +97,4 @@ names:
79: toothbrush
# Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco8.zip
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8.zip
diff --git a/ultralytics/cfg/datasets/crack-seg.yaml b/ultralytics/cfg/datasets/crack-seg.yaml
index 354389d9..f6fe9aa2 100644
--- a/ultralytics/cfg/datasets/crack-seg.yaml
+++ b/ultralytics/cfg/datasets/crack-seg.yaml
@@ -18,4 +18,4 @@ names:
0: crack
# Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/crack-seg.zip
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/crack-seg.zip
diff --git a/ultralytics/cfg/datasets/dota8.yaml b/ultralytics/cfg/datasets/dota8.yaml
index f58b501f..a4dbe61c 100644
--- a/ultralytics/cfg/datasets/dota8.yaml
+++ b/ultralytics/cfg/datasets/dota8.yaml
@@ -31,4 +31,4 @@ names:
14: swimming pool
# Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/dota8.zip
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/dota8.zip
diff --git a/ultralytics/cfg/datasets/lvis.yaml b/ultralytics/cfg/datasets/lvis.yaml
index a3910681..9a79bde6 100644
--- a/ultralytics/cfg/datasets/lvis.yaml
+++ b/ultralytics/cfg/datasets/lvis.yaml
@@ -1225,7 +1225,7 @@ download: |
# Download labels
dir = Path(yaml['path']) # dataset root dir
- url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
+ url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
urls = [url + 'lvis-labels-segments.zip'] # labels
download(urls, dir=dir.parent)
# Download data
diff --git a/ultralytics/cfg/datasets/package-seg.yaml b/ultralytics/cfg/datasets/package-seg.yaml
index a81a5a0c..6c2a6b60 100644
--- a/ultralytics/cfg/datasets/package-seg.yaml
+++ b/ultralytics/cfg/datasets/package-seg.yaml
@@ -18,4 +18,4 @@ names:
0: package
# Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/package-seg.zip
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/package-seg.zip
diff --git a/ultralytics/cfg/datasets/signature.yaml b/ultralytics/cfg/datasets/signature.yaml
index f217edec..d838fd78 100644
--- a/ultralytics/cfg/datasets/signature.yaml
+++ b/ultralytics/cfg/datasets/signature.yaml
@@ -17,4 +17,4 @@ names:
0: signature
# Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/signature.zip
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/signature.zip
diff --git a/ultralytics/cfg/datasets/tiger-pose.yaml b/ultralytics/cfg/datasets/tiger-pose.yaml
index 2e05b6fa..dbcda757 100644
--- a/ultralytics/cfg/datasets/tiger-pose.yaml
+++ b/ultralytics/cfg/datasets/tiger-pose.yaml
@@ -21,4 +21,4 @@ names:
0: tiger
# Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/tiger-pose.zip
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/tiger-pose.zip
diff --git a/ultralytics/data/scripts/get_coco.sh b/ultralytics/data/scripts/get_coco.sh
index 764e280a..780e3e00 100755
--- a/ultralytics/data/scripts/get_coco.sh
+++ b/ultralytics/data/scripts/get_coco.sh
@@ -28,7 +28,7 @@ fi
# Download/unzip labels
d='../datasets' # unzip directory
-url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
+url=https://github.com/ultralytics/assets/releases/download/v0.0.0/
if [ "$segments" == "true" ]; then
f='coco2017labels-segments.zip' # 169 MB
elif [ "$sama" == "true" ]; then
diff --git a/ultralytics/data/scripts/get_coco128.sh b/ultralytics/data/scripts/get_coco128.sh
index 73897916..8260f018 100755
--- a/ultralytics/data/scripts/get_coco128.sh
+++ b/ultralytics/data/scripts/get_coco128.sh
@@ -9,7 +9,7 @@
# Download/unzip images and labels
d='../datasets' # unzip directory
-url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
+url=https://github.com/ultralytics/assets/releases/download/v0.0.0/
f='coco128.zip' # or 'coco128-segments.zip', 68 MB
echo 'Downloading' $url$f ' ...'
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
diff --git a/ultralytics/data/utils.py b/ultralytics/data/utils.py
index fa9bfbb1..2393f1df 100644
--- a/ultralytics/data/utils.py
+++ b/ultralytics/data/utils.py
@@ -379,7 +379,7 @@ def check_cls_dataset(dataset, split=""):
if str(dataset) == "imagenet":
subprocess.run(f"bash {ROOT / 'data/scripts/get_imagenet.sh'}", shell=True, check=True)
else:
- url = f"https://github.com/ultralytics/yolov5/releases/download/v1.0/{dataset}.zip"
+ url = f"https://github.com/ultralytics/assets/releases/download/v0.0.0/{dataset}.zip"
download(url, dir=data_dir.parent)
s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
LOGGER.info(s)
diff --git a/ultralytics/utils/benchmarks.py b/ultralytics/utils/benchmarks.py
index 9a065833..db4a28df 100644
--- a/ultralytics/utils/benchmarks.py
+++ b/ultralytics/utils/benchmarks.py
@@ -195,7 +195,7 @@ class RF100Benchmark:
(shutil.rmtree("rf-100"), os.mkdir("rf-100")) if os.path.exists("rf-100") else os.mkdir("rf-100")
os.chdir("rf-100")
os.mkdir("ultralytics-benchmarks")
- safe_download("https://github.com/ultralytics/yolov5/releases/download/v1.0/datasets_links.txt")
+ safe_download("https://github.com/ultralytics/assets/releases/download/v0.0.0/datasets_links.txt")
with open(ds_link_txt, "r") as file:
for line in file:
diff --git a/ultralytics/utils/checks.py b/ultralytics/utils/checks.py
index 740c5455..dfd79228 100644
--- a/ultralytics/utils/checks.py
+++ b/ultralytics/utils/checks.py
@@ -315,7 +315,7 @@ def check_font(font="Arial.ttf"):
return matches[0]
# Download to USER_CONFIG_DIR if missing
- url = f"https://github.com/ultralytics/yolov5/releases/download/v1.0/{name}"
+ url = f"https://github.com/ultralytics/assets/releases/download/v0.0.0/{name}"
if downloads.is_url(url, check=True):
downloads.safe_download(url=url, file=file)
return file
diff --git a/ultralytics/utils/downloads.py b/ultralytics/utils/downloads.py
index 7441b90c..a13a6f0a 100644
--- a/ultralytics/utils/downloads.py
+++ b/ultralytics/utils/downloads.py
@@ -194,14 +194,12 @@ def unzip_file(file, path=None, exclude=(".DS_Store", "__MACOSX"), exist_ok=Fals
return path # return unzip dir
-def check_disk_space(
- url="https://github.com/ultralytics/yolov5/releases/download/v1.0/coco8.zip", path=Path.cwd(), sf=1.5, hard=True
-):
+def check_disk_space(url="https://ultralytics.com/assets/coco8.zip", path=Path.cwd(), sf=1.5, hard=True):
"""
Check if there is sufficient disk space to download and store a file.
Args:
- url (str, optional): The URL to the file. Defaults to 'https://ultralytics.com/assets/coco8.zip'.
+ url (str, optional): The URL to the file. Defaults to 'https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8.zip'.
path (str | Path, optional): The path or drive to check the available free space on.
sf (float, optional): Safety factor, the multiplier for the required free space. Defaults to 2.0.
hard (bool, optional): Whether to throw an error or not on insufficient disk space. Defaults to True.
@@ -322,7 +320,11 @@ def safe_download(
if "://" not in str(url) and Path(url).is_file(): # URL exists ('://' check required in Windows Python<3.10)
f = Path(url) # filename
elif not f.is_file(): # URL and file do not exist
- desc = f"Downloading {url if gdrive else clean_url(url)} to '{f}'"
+ uri = (url if gdrive else clean_url(url)).replace( # cleaned and aliased url
+ "https://github.com/ultralytics/assets/releases/download/v0.0.0/",
+ "https://ultralytics.com/assets/", # assets alias
+ )
+ desc = f"Downloading {uri} to '{f}'"
LOGGER.info(f"{desc}...")
f.parent.mkdir(parents=True, exist_ok=True) # make directory if missing
check_disk_space(url, path=f.parent)
@@ -356,10 +358,10 @@ def safe_download(
f.unlink() # remove partial downloads
except Exception as e:
if i == 0 and not is_online():
- raise ConnectionError(emojis(f"❌ Download failure for {url}. Environment is not online.")) from e
+ raise ConnectionError(emojis(f"❌ Download failure for {uri}. Environment is not online.")) from e
elif i >= retry:
- raise ConnectionError(emojis(f"❌ Download failure for {url}. Retry limit reached.")) from e
- LOGGER.warning(f"⚠️ Download failure, retrying {i + 1}/{retry} {url}...")
+ raise ConnectionError(emojis(f"❌ Download failure for {uri}. Retry limit reached.")) from e
+ LOGGER.warning(f"⚠️ Download failure, retrying {i + 1}/{retry} {uri}...")
if unzip and f.exists() and f.suffix in {"", ".zip", ".tar", ".gz"}:
from zipfile import is_zipfile