ultralytics 8.0.175 StreamLoader wait for missing frames (#4814)
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Glenn Jocher
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@ -14,16 +14,16 @@ Hyperparameter tuning is not just a one-time set-up but an iterative process aim
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Hyperparameters are high-level, structural settings for the algorithm. They are set prior to the training phase and remain constant during it. Here are some commonly tuned hyperparameters in Ultralytics YOLO:
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- **Learning Rate**: Determines the step size at each iteration while moving towards a minimum in the loss function.
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- **Batch Size**: Number of training samples utilized in one iteration.
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- **Number of Epochs**: An epoch is one complete forward and backward pass of all the training examples.
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- **Architecture Specifics**: Such as anchor box sizes, number of layers, types of activation functions, etc.
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- **Learning Rate** `lr0`: Determines the step size at each iteration while moving towards a minimum in the loss function.
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- **Batch Size** `batch`: Number of images processed simultaneously in a forward pass.
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- **Number of Epochs** `epochs`: An epoch is one complete forward and backward pass of all the training examples.
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- **Architecture Specifics**: Such as channel counts, number of layers, types of activation functions, etc.
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<p align="center">
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<img width="1000" src="https://user-images.githubusercontent.com/26833433/263858934-4f109a2f-82d9-4d08-8bd6-6fd1ff520bcd.png" alt="Hyperparameter Tuning Visual">
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<img width="640" src="https://user-images.githubusercontent.com/26833433/263858934-4f109a2f-82d9-4d08-8bd6-6fd1ff520bcd.png" alt="Hyperparameter Tuning Visual">
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</p>
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For a full list of augmentation hyperparameters used in YOLOv8 please refer to https://docs.ultralytics.com/usage/cfg/#augmentation.
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For a full list of augmentation hyperparameters used in YOLOv8 please refer to [https://docs.ultralytics.com/usage/cfg/#augmentation)(https://docs.ultralytics.com/usage/cfg/#augmentation).
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### Genetic Evolution and Mutation
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@ -67,7 +67,7 @@ The process is repeated until either the set number of iterations is reached or
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## Usage Example
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Here's how to use the `model.tune()` method to utilize the `Tuner` class for hyperparameter tuning:
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Here's how to use the `model.tune()` method to utilize the `Tuner` class for hyperparameter tuning of YOLOv8n on COCO8 for 30 epochs with an AdamW optimizer and skipping plotting, checkpointing and validation other than on final epoch for faster Tuning.
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!!! example ""
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@ -79,10 +79,120 @@ Here's how to use the `model.tune()` method to utilize the `Tuner` class for hyp
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# Initialize the YOLO model
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model = YOLO('yolov8n.pt')
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# Perform hyperparameter tuning
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model.tune(data='coco8.yaml', imgsz=640, epochs=30, iterations=300)
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# Tune hyperparameters on COCO8 for 30 epochs
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model.tune(data='coco8.yaml', epochs=30, iterations=300, optimizer='AdamW', plots=False, save=False, val=False)
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```
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## Results
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After you've successfully completed the hyperparameter tuning process, you will obtain several files and directories that encapsulate the results of the tuning. The following describes each:
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### File Structure
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Here's what the directory structure of the results will look like. Training directories like `train1/` contain individual tuning iterations, i.e. one model trained with one set of hyperparameters. The `tune/` directory contains tuning results from all the individual model trainings:
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```plaintext
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runs/
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└── detect/
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├── train1/
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├── train2/
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├── ...
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└── tune/
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├── best_hyperparameters.yaml
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├── best_fitness.png
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├── tune_results.csv
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├── tune_scatter_plots.png
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└── weights/
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├── last.pt
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└── best.pt
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```
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### File Descriptions
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#### best_hyperparameters.yaml
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This YAML file contains the best-performing hyperparameters found during the tuning process. You can use this file to initialize future trainings with these optimized settings.
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- **Format**: YAML
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- **Usage**: Hyperparameter results
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- **Example**:
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```yaml
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# 558/900 iterations complete ✅ (45536.81s)
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# Results saved to /usr/src/ultralytics/runs/detect/tune
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# Best fitness=0.64297 observed at iteration 498
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# Best fitness metrics are {'metrics/precision(B)': 0.87247, 'metrics/recall(B)': 0.71387, 'metrics/mAP50(B)': 0.79106, 'metrics/mAP50-95(B)': 0.62651, 'val/box_loss': 2.79884, 'val/cls_loss': 2.72386, 'val/dfl_loss': 0.68503, 'fitness': 0.64297}
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# Best fitness model is /usr/src/ultralytics/runs/detect/train498
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# Best fitness hyperparameters are printed below.
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lr0: 0.00269
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lrf: 0.00288
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momentum: 0.73375
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weight_decay: 0.00015
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warmup_epochs: 1.22935
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warmup_momentum: 0.1525
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box: 18.27875
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cls: 1.32899
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dfl: 0.56016
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hsv_h: 0.01148
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hsv_s: 0.53554
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hsv_v: 0.13636
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degrees: 0.0
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translate: 0.12431
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scale: 0.07643
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shear: 0.0
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perspective: 0.0
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flipud: 0.0
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fliplr: 0.08631
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mosaic: 0.42551
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mixup: 0.0
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copy_paste: 0.0
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```
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#### best_fitness.png
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This is a plot displaying fitness (typically a performance metric like AP50) against the number of iterations. It helps you visualize how well the genetic algorithm performed over time.
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- **Format**: PNG
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- **Usage**: Performance visualization
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<p align="center">
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<img width="640" src="https://user-images.githubusercontent.com/26833433/266847423-9d0aea13-d5c4-4771-b06e-0b817a498260.png" alt="Hyperparameter Tuning Fitness vs Iteration">
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</p>
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#### tune_results.csv
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A CSV file containing detailed results of each iteration during the tuning. Each row in the file represents one iteration, and it includes metrics like fitness score, precision, recall, as well as the hyperparameters used.
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- **Format**: CSV
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- **Usage**: Per-iteration results tracking.
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- **Example**:
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```csv
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fitness,lr0,lrf,momentum,weight_decay,warmup_epochs,warmup_momentum,box,cls,dfl,hsv_h,hsv_s,hsv_v,degrees,translate,scale,shear,perspective,flipud,fliplr,mosaic,mixup,copy_paste
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0.05021,0.01,0.01,0.937,0.0005,3.0,0.8,7.5,0.5,1.5,0.015,0.7,0.4,0.0,0.1,0.5,0.0,0.0,0.0,0.5,1.0,0.0,0.0
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0.07217,0.01003,0.00967,0.93897,0.00049,2.79757,0.81075,7.5,0.50746,1.44826,0.01503,0.72948,0.40658,0.0,0.0987,0.4922,0.0,0.0,0.0,0.49729,1.0,0.0,0.0
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0.06584,0.01003,0.00855,0.91009,0.00073,3.42176,0.95,8.64301,0.54594,1.72261,0.01503,0.59179,0.40658,0.0,0.0987,0.46955,0.0,0.0,0.0,0.49729,0.80187,0.0,0.0
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```
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#### tune_scatter_plots.png
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This file contains scatter plots generated from `tune_results.csv`, helping you visualize relationships between different hyperparameters and performance metrics. Note that hyperparameters initialized to 0 will not be tuned, such as `degrees` and `shear` below.
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- **Format**: PNG
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- **Usage**: Exploratory data analysis
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<p align="center">
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<img width="1000" src="https://user-images.githubusercontent.com/26833433/266847488-ec382f3d-79bc-4087-a0e0-42fb8b62cad2.png" alt="Hyperparameter Tuning Scatter Plots">
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</p>
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#### weights/
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This directory contains the saved PyTorch models for the last and the best iterations during the hyperparameter tuning process.
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- **`last.pt`**: The last.pt weights for the iteration that achieved the best fitness score.
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- **`best.pt`**: The best.pt weights for the iteration that achieved the best fitness score.
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Using these results, you can make more informed decisions for your future model trainings and analyses. Feel free to consult these artifacts to understand how well your model performed and how you might improve it further.
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## Conclusion
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The hyperparameter tuning process in Ultralytics YOLO is simplified yet powerful, thanks to its genetic algorithm-based approach focused on mutation. Following the steps outlined in this guide will assist you in systematically tuning your model to achieve better performance.
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@ -93,4 +203,4 @@ The hyperparameter tuning process in Ultralytics YOLO is simplified yet powerful
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2. [YOLOv5 Hyperparameter Evolution Guide](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/)
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3. [Efficient Hyperparameter Tuning with Ray Tune and YOLOv8](https://docs.ultralytics.com/integrations/ray-tune/)
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For deeper insights, you can explore the `Tuner` class source code and accompanying documentation. Should you have any questions, feature requests, or need further assistance, feel free to reach out to our support team.
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For deeper insights, you can explore the `Tuner` class source code and accompanying documentation. Should you have any questions, feature requests, or need further assistance, feel free to reach out to us on [GitHub](https://github.com/ultralytics/ultralytics/issues/new/choose) or [Discord](https://ultralytics.com/discord)
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