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YOLOv5 v7.0 release updates (#10245)

* YOLOv5 v7.0 splash image update * Update tutorial.ipynb Signed-off-by: 's avatarGlenn Jocher <glenn.jocher@ultralytics.com> * Update tutorial.ipynb Signed-off-by: 's avatarGlenn Jocher <glenn.jocher@ultralytics.com> * Update tutorial.ipynb Signed-off-by: 's avatarGlenn Jocher <glenn.jocher@ultralytics.com> * readme segmentation section * readme segmentation section * readme segmentation section * readme segmentation section * readme segmentation section * Update README.md Signed-off-by: 's avatarGlenn Jocher <glenn.jocher@ultralytics.com> * Update README.md Signed-off-by: 's avatarGlenn Jocher <glenn.jocher@ultralytics.com> * Update tutorial.ipynb Signed-off-by: 's avatarGlenn Jocher <glenn.jocher@ultralytics.com> * Update README.md Signed-off-by: 's avatarGlenn Jocher <glenn.jocher@ultralytics.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update README.md Signed-off-by: 's avatarGlenn Jocher <glenn.jocher@ultralytics.com> * Update README.md Signed-off-by: 's avatarGlenn Jocher <glenn.jocher@ultralytics.com> * Update download URLs to 7.0 assets Signed-off-by: 's avatarGlenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: 's avatarpre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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.github/README_cn.md
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<div align="center"> <div align="center">
<p> <p>
<a align="center" href="https://ultralytics.com/yolov5" target="_blank"> <a align="center" href="https://ultralytics.com/yolov5" target="_blank">
<img width="850" src="https://github.com/ultralytics/assets/blob/master/yolov5/v62/splash_readme.png"></a> <img width="850" src="https://raw.githubusercontent.com/ultralytics/assets/master/yolov5/v70/splash.png"></a>
</p> </p>
[English](../README.md) | 简体中文 [English](../README.md) | 简体中文
......
README.md
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<div align="center"> <div align="center">
<p> <p>
<a align="center" href="https://ultralytics.com/yolov5" target="_blank"> <a align="center" href="https://ultralytics.com/yolov5" target="_blank">
<img width="850" src="https://github.com/ultralytics/assets/blob/master/yolov5/v62/splash_readme.png"></a> <img width="850" src="https://raw.githubusercontent.com/ultralytics/assets/master/yolov5/v70/splash.png"></a>
</p> </p>
English | [简体中文](.github/README_cn.md) English | [简体中文](.github/README_cn.md)
...@@ -50,6 +50,79 @@ ...@@ -50,6 +50,79 @@
</div> </div>
## <div align="center">Segmentation ⭐ NEW</div>
<div align="center">
<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
<img width="800" src="https://user-images.githubusercontent.com/26833433/203348073-9b85607b-03e2-48e1-a6ba-fe1c1c31749c.png"></a>
</div>
Our new YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) instance segmentation models are the fastest and most accurate in the world, beating all current [SOTA benchmarks](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco). We've made them super simple to train, validate and deploy. See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v7.0) and visit our [YOLOv5 Segmentation Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) for quickstart tutorials.
<details>
<summary>Segmentation Checkpoints</summary>
<br>
We trained YOLOv5 segmentations models on COCO for 300 epochs at image size 640 using A100 GPUs. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) notebooks for easy reproducibility.
| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Train time<br><sup>300 epochs<br>A100 (hours) | Speed<br><sup>ONNX CPU<br>(ms) | Speed<br><sup>TRT A100<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
|----------------------------------------------------------------------------------------------------|-----------------------|----------------------|-----------------------|-----------------------------------------------|--------------------------------|--------------------------------|--------------------|------------------------|
| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** |
| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 |
| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 |
| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 |
| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 |
- All checkpoints are trained to 300 epochs with SGD optimizer with `lr0=0.01` and `weight_decay=5e-5` at image size 640 and all default settings.<br>Runs logged to https://wandb.ai/glenn-jocher/YOLOv5_v70_official
- **Accuracy** values are for single-model single-scale on COCO dataset.<br>Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`
- **Speed** averaged over 100 inference images using a [Colab Pro](https://colab.research.google.com/signup) A100 High-RAM instance. Values indicate inference speed only (NMS adds about 1ms per image). <br>Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1`
- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`. <br>Reproduce by `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half`
</details>
<details>
<summary>Segmentation Usage Examples &nbsp;<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>
### Train
YOLOv5 segmentation training supports auto-download COCO128-seg segmentation dataset with `--data coco128-seg.yaml` argument and manual download of COCO-segments dataset with `bash data/scripts/get_coco.sh --train --val --segments` and then `python train.py --data coco.yaml`.
```bash
# Single-GPU
python segment/train.py --model yolov5s-seg.pt --data coco128-seg.yaml --epochs 5 --img 640
# Multi-GPU DDP
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --model yolov5s-seg.pt --data coco128-seg.yaml --epochs 5 --img 640 --device 0,1,2,3
```
### Val
Validate YOLOv5m-seg accuracy on ImageNet-1k dataset:
```bash
bash data/scripts/get_coco.sh --val --segments # download COCO val segments split (780MB, 5000 images)
python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate
```
### Predict
Use pretrained YOLOv5m-seg.pt to predict bus.jpg:
```bash
python segment/predict.py --weights yolov5m-seg.pt --data data/images/bus.jpg
```
```python
model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5m-seg.pt') # load from PyTorch Hub (WARNING: inference not yet supported)
```
![zidane](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![bus](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg)
--- |---
### Export
Export YOLOv5s-seg model to ONNX and TensorRT:
```bash
python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0
```
</details>
## <div align="center">Documentation</div> ## <div align="center">Documentation</div>
See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment. See below for quickstart examples. See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment. See below for quickstart examples.
...@@ -200,12 +273,12 @@ YOLOv5 has been designed to be super easy to get started and simple to learn. We ...@@ -200,12 +273,12 @@ YOLOv5 has been designed to be super easy to get started and simple to learn. We
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p> <p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
<details> <details>
<summary>YOLOv5-P5 640 Figure (click to expand)</summary> <summary>YOLOv5-P5 640 Figure</summary>
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p> <p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
</details> </details>
<details> <details>
<summary>Figure Notes (click to expand)</summary> <summary>Figure Notes</summary>
- **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536. - **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.
- **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32. - **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32.
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### Pretrained Checkpoints ### Pretrained Checkpoints
| Model | size<br><sup>(pixels) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Speed<br><sup>CPU b1<br>(ms) | Speed<br><sup>V100 b1<br>(ms) | Speed<br><sup>V100 b32<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@640 (B) | | Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | Speed<br><sup>CPU b1<br>(ms) | Speed<br><sup>V100 b1<br>(ms) | Speed<br><sup>V100 b32<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
|------------------------------------------------------------------------------------------------------|-----------------------|-------------------------|--------------------|------------------------------|-------------------------------|--------------------------------|--------------------|------------------------| |------------------------------------------------------------------------------------------------------|-----------------------|----------------------|-------------------|------------------------------|-------------------------------|--------------------------------|--------------------|------------------------|
| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** | | [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 | | [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 | | [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 | | [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 | | [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
| | | | | | | | | | | | | | | | | | | |
| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 | | [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 | | [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 | | [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 | | [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x6.pt)<br>+ [TTA][TTA] | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- | | [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x6.pt)<br>+ [TTA][TTA] | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- |
<details> <details>
<summary>Table Notes (click to expand)</summary> <summary>Table Notes</summary>
- All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml). - All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
...@@ -240,12 +313,13 @@ YOLOv5 has been designed to be super easy to get started and simple to learn. We ...@@ -240,12 +313,13 @@ YOLOv5 has been designed to be super easy to get started and simple to learn. We
</details> </details>
## <div align="center">Classification ⭐ NEW</div> ## <div align="center">Classification ⭐ NEW</div>
YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) brings support for classification model training, validation, prediction and export! We've made training classifier models super simple. Click below to get started. YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) brings support for classification model training, validation and deployment! See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v6.2) and visit our [YOLOv5 Classification Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) for quickstart tutorials.
<details> <details>
<summary>Classification Checkpoints (click to expand)</summary> <summary>Classification Checkpoints</summary>
<br> <br>
...@@ -280,7 +354,7 @@ We trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4x ...@@ -280,7 +354,7 @@ We trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4x
</details> </details>
<details> <details>
<summary>Classification Usage Examples (click to expand)</summary> <summary>Classification Usage Examples &nbsp;<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>
### Train ### Train
YOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the `--data` argument. To start training on MNIST for example use `--data mnist`. YOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the `--data` argument. To start training on MNIST for example use `--data mnist`.
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classify/tutorial.ipynb
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"<div align=\"center\">\n", "<div align=\"center\">\n",
"\n", "\n",
" <a href=\"https://ultralytics.com/yolov5\" target=\"_blank\">\n", " <a href=\"https://ultralytics.com/yolov5\" target=\"_blank\">\n",
" <img width=\"1024\", src=\"https://github.com/ultralytics/assets/raw/master/yolov5/v62/splash_readme.png\"></a>\n", " <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/master/yolov5/v70/splash.png\"></a>\n",
"\n", "\n",
"\n", "\n",
"<br>\n", "<br>\n",
...@@ -1452,7 +1452,8 @@ ...@@ -1452,7 +1452,8 @@
"accelerator": "GPU", "accelerator": "GPU",
"colab": { "colab": {
"name": "YOLOv5 Classification Tutorial", "name": "YOLOv5 Classification Tutorial",
"provenance": [] "provenance": [],
"toc_visible": true
}, },
"kernelspec": { "kernelspec": {
"display_name": "Python 3 (ipykernel)", "display_name": "Python 3 (ipykernel)",
......
data/scripts/download_weights.sh
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...@@ -11,11 +11,12 @@ ...@@ -11,11 +11,12 @@
python - <<EOF python - <<EOF
from utils.downloads import attempt_download from utils.downloads import attempt_download
p5 = ['n', 's', 'm', 'l', 'x'] # P5 models p5 = list('nsmlx') # P5 models
p6 = [f'{x}6' for x in p5] # P6 models p6 = [f'{x}6' for x in p5] # P6 models
cls = [f'{x}-cls' for x in p5] # classification models cls = [f'{x}-cls' for x in p5] # classification models
seg = [f'{x}-seg' for x in p5] # classification models
for x in p5 + p6 + cls: for x in p5 + p6 + cls + seg:
attempt_download(f'weights/yolov5{x}.pt') attempt_download(f'weights/yolov5{x}.pt')
EOF EOF
segment/tutorial.ipynb
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...@@ -9,7 +9,7 @@ ...@@ -9,7 +9,7 @@
"<div align=\"center\">\n", "<div align=\"center\">\n",
"\n", "\n",
" <a href=\"https://ultralytics.com/yolov5\" target=\"_blank\">\n", " <a href=\"https://ultralytics.com/yolov5\" target=\"_blank\">\n",
" <img width=\"1024\", src=\"https://github.com/ultralytics/assets/raw/master/yolov5/v62/splash_readme.png\"></a>\n", " <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/master/yolov5/v70/splash.png\"></a>\n",
"\n", "\n",
"\n", "\n",
"<br>\n", "<br>\n",
......
tutorial.ipynb
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"<div align=\"center\">\n", "<div align=\"center\">\n",
"\n", "\n",
" <a href=\"https://ultralytics.com/yolov5\" target=\"_blank\">\n", " <a href=\"https://ultralytics.com/yolov5\" target=\"_blank\">\n",
" <img width=\"1024\", src=\"https://github.com/ultralytics/assets/raw/master/yolov5/v62/splash_readme.png\"></a>\n", " <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/master/yolov5/v70/splash.png\"></a>\n",
"\n", "\n",
"\n", "\n",
"<br>\n", "<br>\n",
......
utils/downloads.py
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...@@ -59,14 +59,14 @@ def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''): ...@@ -59,14 +59,14 @@ def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''):
LOGGER.info('') LOGGER.info('')
def attempt_download(file, repo='ultralytics/yolov5', release='v6.2'): def attempt_download(file, repo='ultralytics/yolov5', release='v7.0'):
# Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v6.2', etc. # Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v7.0', etc.
from utils.general import LOGGER from utils.general import LOGGER
def github_assets(repository, version='latest'): def github_assets(repository, version='latest'):
# Return GitHub repo tag (i.e. 'v6.2') and assets (i.e. ['yolov5s.pt', 'yolov5m.pt', ...]) # Return GitHub repo tag (i.e. 'v7.0') and assets (i.e. ['yolov5s.pt', 'yolov5m.pt', ...])
if version != 'latest': if version != 'latest':
version = f'tags/{version}' # i.e. tags/v6.2 version = f'tags/{version}' # i.e. tags/v7.0
response = requests.get(f'https://api.github.com/repos/{repository}/releases/{version}').json() # github api response = requests.get(f'https://api.github.com/repos/{repository}/releases/{version}').json() # github api
return response['tag_name'], [x['name'] for x in response['assets']] # tag, assets return response['tag_name'], [x['name'] for x in response['assets']] # tag, assets
......
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