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202 lines
17 KiB
202 lines
17 KiB
2 years ago
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YOLO settings and hyperparameters play a critical role in the model's performance, speed, and accuracy. These settings
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and hyperparameters can affect the model's behavior at various stages of the model development process, including
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training, validation, and prediction.
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Properly setting and tuning these parameters can have a significant impact on the model's ability to learn effectively
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from the training data and generalize to new data. For example, choosing an appropriate learning rate, batch size, and
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optimization algorithm can greatly affect the model's convergence speed and accuracy. Similarly, setting the correct
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confidence threshold and non-maximum suppression (NMS) threshold can affect the model's performance on detection tasks.
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It is important to carefully consider and experiment with these settings and hyperparameters to achieve the best
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possible performance for a given task. This can involve trial and error, as well as using techniques such as
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hyperparameter optimization to search for the optimal set of parameters.
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In summary, YOLO settings and hyperparameters are a key factor in the success of a YOLO model, and it is important to
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pay careful attention to them to achieve the desired results.
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### Setting the operation type
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YOLO models can be used for a variety of tasks, including detection, segmentation, and classification. These tasks
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differ in the type of output they produce and the specific problem they are designed to solve.
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- Detection: Detection tasks involve identifying and localizing objects or regions of interest in an image or video.
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YOLO models can be used for object detection tasks by predicting the bounding boxes and class labels of objects in an
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image.
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- Segmentation: Segmentation tasks involve dividing an image or video into regions or pixels that correspond to
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different objects or classes. YOLO models can be used for image segmentation tasks by predicting a mask or label for
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each pixel in an image.
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- Classification: Classification tasks involve assigning a class label to an input, such as an image or text. YOLO
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models can be used for image classification tasks by predicting the class label of an input image.
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YOLO models can be used in different modes depending on the specific problem you are trying to solve. These modes
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include train, val, and predict.
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- Train: The train mode is used to train the model on a dataset. This mode is typically used during the development and
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testing phase of a model.
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- Val: The val mode is used to evaluate the model's performance on a validation dataset. This mode is typically used to
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tune the model's hyperparameters and detect overfitting.
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- Predict: The predict mode is used to make predictions with the model on new data. This mode is typically used in
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production or when deploying the model to users.
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| Key | Value | Description |
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|--------|----------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| task | `detect` | Set the task via CLI. See Tasks for all supported tasks like - `detect`, `segment`, `classify`.<br> - `init` is a special case that creates a copy of default.yaml configs to the current working dir |
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| mode | `train` | Set the mode via CLI. It can be `train`, `val`, `predict` |
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| resume | `False` | Resume last given task when set to `True`. <br> Resume from a given checkpoint is `model.pt` is passed |
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| model | null | Set the model. Format can differ for task type. Supports `model_name`, `model.yaml` & `model.pt` |
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| data | null | Set the data. Format can differ for task type. Supports `data.yaml`, `data_folder`, `dataset_name` |
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### Training settings
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Training settings for YOLO models refer to the various hyperparameters and configurations used to train the model on a
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dataset. These settings can affect the model's performance, speed, and accuracy. Some common YOLO training settings
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include the batch size, learning rate, momentum, and weight decay. Other factors that may affect the training process
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include the choice of optimizer, the choice of loss function, and the size and composition of the training dataset. It
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is important to carefully tune and experiment with these settings to achieve the best possible performance for a given
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task.
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| Key | Value | Description |
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|-----------------|---------|-----------------------------------------------------------------------------|
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| device | '' | cuda device, i.e. 0 or 0,1,2,3 or cpu. `''` selects available cuda 0 device |
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| epochs | 100 | Number of epochs to train |
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| workers | 8 | Number of cpu workers used per process. Scales automatically with DDP |
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| batch_size | 16 | Batch size of the dataloader |
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| imgsz | 640 | Image size of data in dataloader |
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| optimizer | SGD | Optimizer used. Supported optimizer are: `Adam`, `SGD`, `RMSProp` |
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| single_cls | False | Train on multi-class data as single-class |
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| image_weights | False | Use weighted image selection for training |
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| rect | False | Enable rectangular training |
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| cos_lr | False | Use cosine LR scheduler |
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| lr0 | 0.01 | Initial learning rate |
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| lrf | 0.01 | Final OneCycleLR learning rate |
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| momentum | 0.937 | Use as `momentum` for SGD and `beta1` for Adam |
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| weight_decay | 0.0005 | Optimizer weight decay |
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| warmup_epochs | 3.0 | Warmup epochs. Fractions are ok. |
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| warmup_momentum | 0.8 | Warmup initial momentum |
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| warmup_bias_lr | 0.1 | Warmup initial bias lr |
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| box | 0.05 | Box loss gain |
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| cls | 0.5 | cls loss gain |
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| cls_pw | 1.0 | cls BCELoss positive_weight |
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| obj | 1.0 | bj loss gain (scale with pixels) |
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| obj_pw | 1.0 | obj BCELoss positive_weight |
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| iou_t | 0.20 | IOU training threshold |
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| anchor_t | 4.0 | anchor-multiple threshold |
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| fl_gamma | 0.0 | focal loss gamma |
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| label_smoothing | 0.0 | |
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| nbs | 64 | nominal batch size |
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| overlap_mask | `True` | **Segmentation**: Use mask overlapping during training |
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| mask_ratio | 4 | **Segmentation**: Set mask downsampling |
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| dropout | `False` | **Classification**: Use dropout while training |
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### Prediction Settings
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Prediction settings for YOLO models refer to the various hyperparameters and configurations used to make predictions
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with the model on new data. These settings can affect the model's performance, speed, and accuracy. Some common YOLO
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prediction settings include the confidence threshold, non-maximum suppression (NMS) threshold, and the number of classes
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to consider. Other factors that may affect the prediction process include the size and format of the input data, the
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presence of additional features such as masks or multiple labels per box, and the specific task the model is being used
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for. It is important to carefully tune and experiment with these settings to achieve the best possible performance for a
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given task.
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| Key | Value | Description |
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|----------------|----------------------|-------------------------------------------------|
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| source | `ultralytics/assets` | Input source. Accepts image, folder, video, url |
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| view_img | `False` | View the prediction images |
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| save_txt | `False` | Save the results in a txt file |
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| save_conf | `False` | Save the condidence scores |
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| save_crop | `Fasle` | |
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| hide_labels | `False` | Hide the labels |
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| hide_conf | `False` | Hide the confidence scores |
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| vid_stride | `False` | Input video frame-rate stride |
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| line_thickness | `3` | Bounding-box thickness (pixels) |
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| visualize | `False` | Visualize model features |
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| augment | `False` | Augmented inference |
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| agnostic_nms | `False` | Class-agnostic NMS |
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| retina_masks | `False` | **Segmentation:** High resolution masks |
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### Validation settings
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Validation settings for YOLO models refer to the various hyperparameters and configurations used to
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evaluate the model's performance on a validation dataset. These settings can affect the model's performance, speed, and
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accuracy. Some common YOLO validation settings include the batch size, the frequency with which validation is performed
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during training, and the metrics used to evaluate the model's performance. Other factors that may affect the validation
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process include the size and composition of the validation dataset and the specific task the model is being used for. It
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is important to carefully tune and experiment with these settings to ensure that the model is performing well on the
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validation dataset and to detect and prevent overfitting.
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| Key | Value | Description |
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|-------------|---------|-----------------------------------|
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| noval | `False` | ??? |
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| save_json | `False` | |
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| save_hybrid | `False` | |
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| conf_thres | `0.001` | Confidence threshold |
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| iou_thres | `0.6` | IoU threshold |
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| max_det | `300` | Maximum number of detections |
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| half | `True` | Use .half() mode. |
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| dnn | `False` | Use OpenCV DNN for ONNX inference |
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| plots | `False` | |
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### Export settings
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Export settings for YOLO models refer to the various configurations and options used to save or
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export the model for use in other environments or platforms. These settings can affect the model's performance, size,
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and compatibility with different systems. Some common YOLO export settings include the format of the exported model
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file (e.g. ONNX, TensorFlow SavedModel), the device on which the model will be run (e.g. CPU, GPU), and the presence of
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additional features such as masks or multiple labels per box. Other factors that may affect the export process include
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the specific task the model is being used for and the requirements or constraints of the target environment or platform.
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It is important to carefully consider and configure these settings to ensure that the exported model is optimized for
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the intended use case and can be used effectively in the target environment.
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### Augmentation settings
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Augmentation settings for YOLO models refer to the various transformations and modifications
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applied to the training data to increase the diversity and size of the dataset. These settings can affect the model's
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performance, speed, and accuracy. Some common YOLO augmentation settings include the type and intensity of the
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transformations applied (e.g. random flips, rotations, cropping, color changes), the probability with which each
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transformation is applied, and the presence of additional features such as masks or multiple labels per box. Other
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factors that may affect the augmentation process include the size and composition of the original dataset and the
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specific task the model is being used for. It is important to carefully tune and experiment with these settings to
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ensure that the augmented dataset is diverse and representative enough to train a high-performing model.
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| hsv_h | 0.015 | Image HSV-Hue augmentation (fraction) |
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|-------------|-------|-------------------------------------------------|
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| hsv_s | 0.7 | Image HSV-Saturation augmentation (fraction) |
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| hsv_v | 0.4 | Image HSV-Value augmentation (fraction) |
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| degrees | 0.0 | Image rotation (+/- deg) |
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| translate | 0.1 | Image translation (+/- fraction) |
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| scale | 0.5 | Image scale (+/- gain) |
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| shear | 0.0 | Image shear (+/- deg) |
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| perspective | 0.0 | Image perspective (+/- fraction), range 0-0.001 |
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| flipud | 0.0 | Image flip up-down (probability) |
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| fliplr | 0.5 | Image flip left-right (probability) |
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| mosaic | 1.0 | Image mosaic (probability) |
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| mixup | 0.0 | Image mixup (probability) |
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| copy_paste | 0.0 | Segment copy-paste (probability) |
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### Logging, checkpoints, plotting and file management
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Logging, checkpoints, plotting, and file management are important considerations when training a YOLO model.
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- Logging: It is often helpful to log various metrics and statistics during training to track the model's progress and
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diagnose any issues that may arise. This can be done using a logging library such as TensorBoard or by writing log
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messages to a file.
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- Checkpoints: It is a good practice to save checkpoints of the model at regular intervals during training. This allows
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you to resume training from a previous point if the training process is interrupted or if you want to experiment with
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different training configurations.
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- Plotting: Visualizing the model's performance and training progress can be helpful for understanding how the model is
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behaving and identifying potential issues. This can be done using a plotting library such as matplotlib or by
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generating plots using a logging library such as TensorBoard.
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- File management: Managing the various files generated during the training process, such as model checkpoints, log
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files, and plots, can be challenging. It is important to have a clear and organized file structure to keep track of
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these files and make it easy to access and analyze them as needed.
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Effective logging, checkpointing, plotting, and file management can help you keep track of the model's progress and make
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it easier to debug and optimize the training process.
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| Key | Value | Description |
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|-----------|---------|---------------------------------------------------------------------------------------------|
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| project: | 'runs' | The project name |
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| name: | 'exp' | The run name. `exp` gets automatically incremented if not specified, i.e, `exp`, `exp2` ... |
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| exist_ok: | `False` | ??? |
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| plots | `False` | **Validation**: Save plots while validation |
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| nosave | `False` | Don't save any plots, models or files |
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