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Ultralytics Asset URL Update (#14345)

Browse source 
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-07-10 21:12:33 +02:00 • committed by GitHub
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35 changed files with 55 additions and 53 deletions
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2
docker/Dockerfile-arm64
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@ -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

4
docker/Dockerfile-jetson-jetpack4
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@ -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

6
docs/en/datasets/classify/imagenet10.md
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@ -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.

2
docs/en/integrations/tensorrt.md
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@ -13,7 +13,7 @@ By using the TensorRT export format, you can enhance your [Ultralytics YOLOv8](h
## TensorRT
<p align="center">
<img width="100%" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/tensorrt-overview.jpg" alt="TensorRT Overview">
<img width="100%" src="https://github.com/ultralytics/ultralytics/assets/26833433/7fea48c2-9709-4deb-8d04-eaf95d12a91d" alt="TensorRT Overview">
</p>
[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.

2
docs/en/yolov5/tutorials/multi_gpu_training.md
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@ -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.
<p align="center"><img width="700" alt="YOLOv5 Models" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/model_comparison.png"></p>
<p align="center"><img width="700" alt="YOLOv5 Models" src="https://github.com/ultralytics/assets/releases/download/v0.0.0/model_comparison.png"></p>
### Single GPU

2
docs/en/yolov5/tutorials/tips_for_best_training_results.md
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@ -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.
<p align="center"><img width="700" alt="YOLOv5 Models" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/model_comparison.png"></p>
<p align="center"><img width="700" alt="YOLOv5 Models" src="https://github.com/ultralytics/assets/releases/download/v0.0.0/model_comparison.png"></p>
- **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).

6
docs/en/yolov5/tutorials/train_custom_data.md
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@ -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.
<p align="center"><img width="800" alt="YOLOv5 models" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/model_comparison.png"></p>
<p align="center"><img width="800" alt="YOLOv5 models" src="https://github.com/ultralytics/assets/releases/download/v0.0.0/model_comparison.png"></p>
## 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.
<img alt="Local logging results" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/image-local_logging.jpg" width="1280">
<img alt="Local logging results" src="https://github.com/ultralytics/assets/releases/download/v0.0.0/image-local_logging.jpg" width="1280">
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'
```
<p align="center"><img width="800" alt="results.png" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/results.png"></p>
<p align="center"><img width="800" alt="results.png" src="https://github.com/ultralytics/assets/releases/download/v0.0.0/results.png"></p>
## Next Steps

6
tests/test_python.py
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@ -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()

2
ultralytics/cfg/datasets/DOTAv1.5.yaml
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@ -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

2
ultralytics/cfg/datasets/DOTAv1.yaml
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@ -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

2
ultralytics/cfg/datasets/GlobalWheat2020.yaml
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@ -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

2
ultralytics/cfg/datasets/VOC.yaml
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@ -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

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ultralytics/cfg/datasets/VisDrone.yaml
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@ -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

2
ultralytics/cfg/datasets/african-wildlife.yaml
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@ -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

2
ultralytics/cfg/datasets/brain-tumor.yaml
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@ -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

2
ultralytics/cfg/datasets/carparts-seg.yaml
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@ -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

2
ultralytics/cfg/datasets/coco-pose.yaml
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@ -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

2
ultralytics/cfg/datasets/coco.yaml
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@ -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

2
ultralytics/cfg/datasets/coco128-seg.yaml
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@ -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

2
ultralytics/cfg/datasets/coco128.yaml
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@ -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

2
ultralytics/cfg/datasets/coco8-pose.yaml
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@ -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

2
ultralytics/cfg/datasets/coco8-seg.yaml
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@ -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

2
ultralytics/cfg/datasets/coco8.yaml
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@ -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

2
ultralytics/cfg/datasets/crack-seg.yaml
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@ -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

2
ultralytics/cfg/datasets/dota8.yaml
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@ -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

2
ultralytics/cfg/datasets/lvis.yaml
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@ -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

2
ultralytics/cfg/datasets/package-seg.yaml
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@ -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

2
ultralytics/cfg/datasets/signature.yaml
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@ -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

2
ultralytics/cfg/datasets/tiger-pose.yaml
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@ -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

2
ultralytics/data/scripts/get_coco.sh
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@ -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

2
ultralytics/data/scripts/get_coco128.sh
View file

@ -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 &

2
ultralytics/data/utils.py
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@ -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)

2
ultralytics/utils/benchmarks.py
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@ -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:

2
ultralytics/utils/checks.py
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@ -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

18
ultralytics/utils/downloads.py
View file

@ -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