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UltralyticsAssistant
parent
c6c6f3dd07
commit
866e022e21
classify
segment
utils
flask_rest_api
loggers
38
README.md
38
README.md
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@ -104,8 +104,8 @@ results = model(img)
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# Process the results (options: .print(), .show(), .save(), .crop(), .pandas())
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results.print() # Print results to console
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results.show() # Display results in a window
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results.save() # Save results to runs/detect/exp
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results.show() # Display results in a window
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results.save() # Save results to runs/detect/exp
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```
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</details>
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@ -221,8 +221,8 @@ Explore Ultralytics' key integrations with leading AI platforms. These collabora
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<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="Neural Magic logo"></a>
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</div>
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| Ultralytics HUB 🚀 | W&B | Comet ⭐ NEW | Neural Magic |
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| :--------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
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| Ultralytics HUB 🚀 | W&B | Comet ⭐ NEW | Neural Magic |
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| :--------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: |
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| Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics HUB](https://www.ultralytics.com/hub). Try now! | Track experiments, hyperparameters, and results seamlessly with [Weights & Biases](https://docs.wandb.ai/guides/integrations/ultralytics/). | Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLOv5 models, resume training, and interactively visualize and debug predictions. | Run YOLOv5 inference up to 6x faster on CPUs with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic). |
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## ⭐ Ultralytics HUB
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@ -256,18 +256,18 @@ YOLOv5 is designed for simplicity and ease of use. We prioritize real-world perf
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This table shows the performance metrics for various YOLOv5 models trained on the COCO dataset.
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| 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) |
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| ----------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- |
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| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
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| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
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| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
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| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
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| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
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| | | | | | | | | |
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| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
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| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
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| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
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| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
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| 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) |
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| ------------------------------------------------------------------------------------------------------ | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- |
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| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
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| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
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| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
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| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
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| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
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| | | | | | | | | |
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| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
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| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
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| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
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| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
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| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/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>- |
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<details>
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@ -347,13 +347,11 @@ python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg
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```python
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# Load model from PyTorch Hub (Note: Inference support might vary)
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model = torch.hub.load(
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"ultralytics/yolov5", "custom", "yolov5m-seg.pt"
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)
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model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5m-seg.pt")
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```
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|  |  |
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| :---------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------: |
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| :-----------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------: |
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### Export
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@ -1,5 +1,4 @@
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""
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Run YOLOv5 benchmarks on all supported export formats.
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""
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Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""
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Train a YOLOv5 classifier model on a classification dataset.
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""
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Validate a trained YOLOv5 classification model on a classification dataset.
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@ -1,5 +1,4 @@
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""
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Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""
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Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit.
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""
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PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5.
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""Common modules."""
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import ast
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""Experimental modules."""
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import math
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""
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TensorFlow, Keras and TFLite versions of YOLOv5
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Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127.
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""
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YOLO-specific modules.
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""
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Run YOLOv5 segmentation inference on images, videos, directories, streams, etc.
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""
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Train a YOLOv5 segment model on a segment dataset Models and datasets download automatically from the latest YOLOv5
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release.
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""
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Validate a trained YOLOv5 segment model on a segment dataset.
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1
train.py
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train.py
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""
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Train a YOLOv5 model on a custom dataset. Models and datasets download automatically from the latest YOLOv5 release.
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""utils/initialization."""
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import contextlib
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""Activation functions."""
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import torch
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""Image augmentation functions."""
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import math
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""AutoAnchor utils."""
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import random
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""Auto-batch utils."""
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from copy import deepcopy
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""Callback utils."""
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import threading
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""Dataloaders and dataset utils."""
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import contextlib
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""Download utils."""
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import logging
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""Perform test request."""
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import pprint
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""Run a Flask REST API exposing one or more YOLOv5s models."""
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import argparse
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""General utils."""
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import contextlib
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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"""Logging utils."""
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import json
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[ClearML](https://clear.ml/) is an [open-source](https://github.com/clearml/clearml) MLOps platform designed to streamline your machine learning workflow and save you valuable time ⏱️. Integrating ClearML with Ultralytics YOLOv5 allows you to leverage a powerful suite of tools:
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- **Experiment Management:** 🔨 Track every [YOLOv5](https://docs.ultralytics.com/models/yolov5/) training run, including parameters, metrics, and outputs. See the [Ultralytics ClearML integration guide](https://docs.ultralytics.com/integrations/clearml/) for more details.
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- **Data Versioning:** 🔧 Version and easily access your custom training data using the integrated ClearML Data Versioning Tool, similar to concepts in [DVC integration](https://docs.ultralytics.com/integrations/dvc/).
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- **Remote Execution:** 🔦 [Remotely train and monitor](https://docs.ultralytics.com/hub/cloud-training/) your YOLOv5 models using ClearML Agent.
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- **Hyperparameter Optimization:** 🔬 Achieve optimal [Mean Average Precision (mAP)](https://docs.ultralytics.com/guides/yolo-performance-metrics/) using ClearML's [Hyperparameter Optimization](https://docs.ultralytics.com/guides/hyperparameter-tuning/) capabilities.
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- **Model Deployment:** 🔭 Turn your trained YOLOv5 model into an API with just a few commands using ClearML Serving, complementing [Ultralytics deployment options](https://docs.ultralytics.com/guides/model-deployment-options/).
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- **Experiment Management:** 🔨 Track every [YOLOv5](https://docs.ultralytics.com/models/yolov5/) training run, including parameters, metrics, and outputs. See the [Ultralytics ClearML integration guide](https://docs.ultralytics.com/integrations/clearml/) for more details.
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- **Data Versioning:** 🔧 Version and easily access your custom training data using the integrated ClearML Data Versioning Tool, similar to concepts in [DVC integration](https://docs.ultralytics.com/integrations/dvc/).
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- **Remote Execution:** 🔦 [Remotely train and monitor](https://docs.ultralytics.com/hub/cloud-training/) your YOLOv5 models using ClearML Agent.
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- **Hyperparameter Optimization:** 🔬 Achieve optimal [Mean Average Precision (mAP)](https://docs.ultralytics.com/guides/yolo-performance-metrics/) using ClearML's [Hyperparameter Optimization](https://docs.ultralytics.com/guides/hyperparameter-tuning/) capabilities.
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- **Model Deployment:** 🔭 Turn your trained YOLOv5 model into an API with just a few commands using ClearML Serving, complementing [Ultralytics deployment options](https://docs.ultralytics.com/guides/model-deployment-options/).
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You can choose to use only the experiment manager or combine multiple tools into a comprehensive MLOps pipeline.
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Follow these steps to get started:
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1. Install the `clearml` Python package:
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```bash
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pip install clearml
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```
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*Note: This package is included in the `requirements.txt` of YOLOv5.*
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_Note: This package is included in the `requirements.txt` of YOLOv5._
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2. Connect the ClearML SDK to your server. [Create credentials](https://app.clear.ml/settings/workspace-configuration) (Settings -> Workspace -> Create new credentials), then run the following command and follow the prompts:
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```bash
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@ -62,17 +64,17 @@ python train.py --project my_yolo_project --name experiment_001 --img 640 --batc
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ClearML automatically captures comprehensive information about your training run:
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- Source code and uncommitted changes
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- Installed Python packages
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- Hyperparameters and configuration settings
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- Model checkpoints (use `--save-period n` to save every `n` epochs)
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- Console output logs
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- Performance metrics ([mAP_0.5](https://docs.ultralytics.com/guides/yolo-performance-metrics/), mAP_0.5:0.95, [precision, recall](https://docs.ultralytics.com/guides/yolo-performance-metrics/), [losses](https://docs.ultralytics.com/reference/utils/loss/), [learning rates](https://www.ultralytics.com/glossary/learning-rate), etc.)
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- System details (machine specs, runtime, creation date)
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- Generated plots (e.g., label correlogram, [confusion matrix](https://www.ultralytics.com/glossary/confusion-matrix))
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- Images with bounding boxes per epoch
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- Mosaic augmentation previews per epoch
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- Validation images per epoch
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- Source code and uncommitted changes
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- Installed Python packages
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- Hyperparameters and configuration settings
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- Model checkpoints (use `--save-period n` to save every `n` epochs)
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- Console output logs
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- Performance metrics ([mAP_0.5](https://docs.ultralytics.com/guides/yolo-performance-metrics/), mAP_0.5:0.95, [precision, recall](https://docs.ultralytics.com/guides/yolo-performance-metrics/), [losses](https://docs.ultralytics.com/reference/utils/loss/), [learning rates](https://www.ultralytics.com/glossary/learning-rate), etc.)
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- System details (machine specs, runtime, creation date)
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- Generated plots (e.g., label correlogram, [confusion matrix](https://www.ultralytics.com/glossary/confusion-matrix))
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- Images with bounding boxes per epoch
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- Mosaic augmentation previews per epoch
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||||
- Validation images per epoch
|
||||
|
||||
This wealth of information 🤯 can be visualized in the ClearML UI. You can customize table views, sort experiments by metrics like mAP, and directly compare multiple runs. This detailed tracking enables advanced features like hyperparameter optimization and remote execution.
|
||||
|
||||
|
|
@ -133,7 +135,8 @@ clearml-data add --files .
|
|||
# Finalize and upload the dataset version
|
||||
clearml-data close
|
||||
```
|
||||
*Tip: Use `--parent <parent_dataset_id>` with `clearml-data create` to link versions and avoid re-uploading unchanged files.*
|
||||
|
||||
_Tip: Use `--parent <parent_dataset_id>` with `clearml-data create` to link versions and avoid re-uploading unchanged files._
|
||||
|
||||
### Run Training Using a ClearML Dataset
|
||||
|
||||
|
|
@ -168,8 +171,8 @@ ClearML Agent allows you to execute experiments on remote machines (e.g., powerf
|
|||
|
||||
Learn more about ClearML Agent:
|
||||
|
||||
- [YouTube Introduction](https://www.youtube.com/watch?v=MX3BrXnaULs)
|
||||
- [Official Documentation](https://clear.ml/docs/latest/docs/clearml_agent)
|
||||
- [YouTube Introduction](https://www.youtube.com/watch?v=MX3BrXnaULs)
|
||||
- [Official Documentation](https://clear.ml/docs/latest/docs/clearml_agent)
|
||||
|
||||
Turn any machine into a ClearML agent by running:
|
||||
|
||||
|
|
@ -201,7 +204,7 @@ if RANK in {-1, 0}:
|
|||
# Check if ClearML logger is active and enqueue the task
|
||||
if loggers.clearml:
|
||||
# Specify the queue name for the remote agent
|
||||
loggers.clearml.task.execute_remotely(queue_name="my_remote_queue") # <------ ADD THIS LINE
|
||||
loggers.clearml.task.execute_remotely(queue_name="my_remote_queue") # <------ ADD THIS LINE
|
||||
# data_dict might be populated by ClearML if using a ClearML dataset
|
||||
data_dict = loggers.clearml.data_dict
|
||||
```
|
||||
|
|
|
|||
|
|
@ -1,5 +1,4 @@
|
|||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
"""Main Logger class for ClearML experiment tracking."""
|
||||
|
||||
import glob
|
||||
|
|
|
|||
|
|
@ -29,10 +29,12 @@ pip install comet_ml
|
|||
You can configure Comet in two ways:
|
||||
|
||||
1. **Environment Variables:** Set your credentials directly in your environment.
|
||||
|
||||
```shell
|
||||
export COMET_API_KEY=<Your Comet API Key>
|
||||
export COMET_PROJECT_NAME=<Your Comet Project Name> # Defaults to 'yolov5' if not set
|
||||
```
|
||||
|
||||
Find your API key in your [Comet Account Settings](https://www.comet.com/docs/v2/guides/getting-started/quickstart/#get-an-api-key?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github_readme).
|
||||
|
||||
2. **Configuration File:** Create a `.comet.config` file in your working directory with the following content:
|
||||
|
|
@ -200,7 +202,7 @@ To use a dataset stored in Comet Artifacts, update the `path` in your dataset `y
|
|||
# contents of artifact.yaml
|
||||
path: "comet://<workspace_name>/<artifact_name>:<artifact_version_or_alias>"
|
||||
train: images/train # Adjust subdirectory if needed
|
||||
val: images/val # Adjust subdirectory if needed
|
||||
val: images/val # Adjust subdirectory if needed
|
||||
|
||||
# Other dataset configurations...
|
||||
```
|
||||
|
|
@ -259,8 +261,8 @@ python utils/loggers/comet/hpo.py \
|
|||
Execute multiple sweep trials concurrently using the `comet optimizer` command:
|
||||
|
||||
```shell
|
||||
comet optimizer -j <num_workers> utils/loggers/comet/hpo.py \
|
||||
utils/loggers/comet/optimizer_config.json
|
||||
comet optimizer -j \
|
||||
utils/loggers/comet/optimizer_config.json < num_workers > utils/loggers/comet/hpo.py
|
||||
```
|
||||
|
||||
Replace `<num_workers>` with the desired number of parallel processes.
|
||||
|
|
|
|||
|
|
@ -1,5 +1,4 @@
|
|||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
"""Loss functions."""
|
||||
|
||||
import torch
|
||||
|
|
|
|||
|
|
@ -1,5 +1,4 @@
|
|||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
"""Model validation metrics."""
|
||||
|
||||
import math
|
||||
|
|
|
|||
|
|
@ -1,5 +1,4 @@
|
|||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
"""Plotting utils."""
|
||||
|
||||
import contextlib
|
||||
|
|
|
|||
|
|
@ -1,5 +1,4 @@
|
|||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
"""Image augmentation functions."""
|
||||
|
||||
import math
|
||||
|
|
|
|||
|
|
@ -1,5 +1,4 @@
|
|||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
"""Dataloaders."""
|
||||
|
||||
import os
|
||||
|
|
|
|||
|
|
@ -1,5 +1,4 @@
|
|||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
"""Model validation metrics."""
|
||||
|
||||
import numpy as np
|
||||
|
|
|
|||
|
|
@ -1,5 +1,4 @@
|
|||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
"""PyTorch utils."""
|
||||
|
||||
import math
|
||||
|
|
|
|||
|
|
@ -1,5 +1,4 @@
|
|||
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
||||
|
||||
"""Utils to interact with the Triton Inference Server."""
|
||||
|
||||
import typing
|
||||
|
|
|
|||
Loading…
Reference in New Issue