diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml index 9813f33..ebaf609 100644 --- a/.github/workflows/greetings.yml +++ b/.github/workflows/greetings.yml @@ -32,6 +32,8 @@ jobs: If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our [Tips for Best Training Results](https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results/). + Join the vibrant [Ultralytics Discord](https://discord.gg/YVsATxj6wr) 🎧 community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users. + ## Install Pip install the `ultralytics` package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a [**Python>=3.7**](https://www.python.org/) environment with [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 60b2b30..de38116 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -22,7 +22,7 @@ repos: - id: detect-private-key - repo: https://github.com/asottile/pyupgrade - rev: v3.3.2 + rev: v3.4.0 hooks: - id: pyupgrade name: Upgrade code diff --git a/docs/modes/train.md b/docs/modes/train.md index d905084..1738975 100644 --- a/docs/modes/train.md +++ b/docs/modes/train.md @@ -72,7 +72,7 @@ task. | `name` | `None` | experiment name | | `exist_ok` | `False` | whether to overwrite existing experiment | | `pretrained` | `False` | whether to use a pretrained model | -| `optimizer` | `'SGD'` | optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto] | +| `optimizer` | `'auto'` | optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto] | | `verbose` | `False` | whether to print verbose output | | `seed` | `0` | random seed for reproducibility | | `deterministic` | `True` | whether to enable deterministic mode | @@ -102,3 +102,60 @@ task. | `mask_ratio` | `4` | mask downsample ratio (segment train only) | | `dropout` | `0.0` | use dropout regularization (classify train only) | | `val` | `True` | validate/test during training | + +## Logging + +In training a YOLOv8 model, you might find it valuable to keep track of the model's performance over time. This is where logging comes into play. Ultralytics' YOLO provides support for three types of loggers - Comet, ClearML, and TensorBoard. + +To use a logger, select it from the dropdown menu in the code snippet above and run it. The chosen logger will be installed and initialized. + +### Comet + +[Comet](https://www.comet.ml/site/) is a platform that allows data scientists and developers to track, compare, explain and optimize experiments and models. It provides functionalities such as real-time metrics, code diffs, and hyperparameters tracking. + +To use Comet: + +```python +# pip install comet_ml +import comet_ml + +comet_ml.init() +``` + +Remember to sign in to your Comet account on their website and get your API key. You will need to add this to your environment variables or your script to log your experiments. + +### ClearML + +[ClearML](https://www.clear.ml/) is an open-source platform that automates tracking of experiments and helps with efficient sharing of resources. It is designed to help teams manage, execute, and reproduce their ML work more efficiently. + +To use ClearML: + +```python +# pip install clearml +import clearml + +clearml.browser_login() +``` + +After running this script, you will need to sign in to your ClearML account on the browser and authenticate your session. + +### TensorBoard + +[TensorBoard](https://www.tensorflow.org/tensorboard) is a visualization toolkit for TensorFlow. It allows you to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. + +To use TensorBoard in [Google Colab](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb): + +```bash +load_ext tensorboard +tensorboard --logdir ultralytics/runs # replace with 'runs' directory +``` + +To use TensorBoard locally run the below command and view results at http://localhost:6006/. + +```bash +tensorboard --logdir ultralytics/runs # replace with 'runs' directory +``` + +This will load TensorBoard and direct it to the directory where your training logs are saved. + +After setting up your logger, you can then proceed with your model training. All training metrics will be automatically logged in your chosen platform, and you can access these logs to monitor your model's performance over time, compare different models, and identify areas for improvement. \ No newline at end of file diff --git a/docs/tasks/classify.md b/docs/tasks/classify.md index ed14b37..47c6cb7 100644 --- a/docs/tasks/classify.md +++ b/docs/tasks/classify.md @@ -6,8 +6,7 @@ description: Check YOLO class label with only one class for the whole image, usi Image classification is the simplest of the three tasks and involves classifying an entire image into one of a set of predefined classes. -
- + The output of an image classifier is a single class label and a confidence score. Image classification is useful when you need to know only what class an image belongs to and don't need to know where objects diff --git a/docs/tasks/detect.md b/docs/tasks/detect.md index 10149bb..6942060 100644 --- a/docs/tasks/detect.md +++ b/docs/tasks/detect.md @@ -5,8 +5,7 @@ description: Learn how to use YOLOv8, an object detection model pre-trained with Object detection is a task that involves identifying the location and class of objects in an image or video stream. -
- + The output of an object detector is a set of bounding boxes that enclose the objects in the image, along with class labels and confidence scores for each box. Object detection is a good choice when you need to identify objects of interest in a scene, but don't need to know exactly where the object is or its exact shape. diff --git a/docs/tasks/pose.md b/docs/tasks/pose.md index 65148fe..68ccd19 100644 --- a/docs/tasks/pose.md +++ b/docs/tasks/pose.md @@ -8,7 +8,7 @@ to as keypoints. The keypoints can represent various parts of the object such as features. The locations of the keypoints are usually represented as a set of 2D `[x, y]` or 3D `[x, y, visible]` coordinates. - + The output of a pose estimation model is a set of points that represent the keypoints on an object in the image, usually along with the confidence scores for each point. Pose estimation is a good choice when you need to identify specific diff --git a/docs/tasks/segment.md b/docs/tasks/segment.md index 9bcc90b..8eb5db8 100644 --- a/docs/tasks/segment.md +++ b/docs/tasks/segment.md @@ -6,8 +6,7 @@ description: Learn what Instance segmentation is. Get pretrained YOLOv8 segment Instance segmentation goes a step further than object detection and involves identifying individual objects in an image and segmenting them from the rest of the image. -
- + The output of an instance segmentation model is a set of masks or contours that outline each object in the image, along with class labels and confidence scores for each object. Instance diff --git a/docs/usage/cfg.md b/docs/usage/cfg.md index f6dc187..b7ddb49 100644 --- a/docs/usage/cfg.md +++ b/docs/usage/cfg.md @@ -94,7 +94,7 @@ The training settings for YOLO models encompass various hyperparameters and conf | `name` | `None` | experiment name | | `exist_ok` | `False` | whether to overwrite existing experiment | | `pretrained` | `False` | whether to use a pretrained model | -| `optimizer` | `'SGD'` | optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto] | +| `optimizer` | `'auto'` | optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto] | | `verbose` | `False` | whether to print verbose output | | `seed` | `0` | random seed for reproducibility | | `deterministic` | `True` | whether to enable deterministic mode | diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py index 86f7d7c..aa3e47e 100644 --- a/ultralytics/__init__.py +++ b/ultralytics/__init__.py @@ -1,6 +1,6 @@ # Ultralytics YOLO 🚀, AGPL-3.0 license -__version__ = '8.0.113' +__version__ = '8.0.114' from ultralytics.hub import start from ultralytics.vit.rtdetr import RTDETR diff --git a/ultralytics/yolo/cfg/default.yaml b/ultralytics/yolo/cfg/default.yaml index cd73148..35be2e9 100644 --- a/ultralytics/yolo/cfg/default.yaml +++ b/ultralytics/yolo/cfg/default.yaml @@ -20,7 +20,7 @@ project: # project name name: # experiment name, results saved to 'project/name' directory exist_ok: False # whether to overwrite existing experiment pretrained: False # whether to use a pretrained model -optimizer: SGD # optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto] +optimizer: auto # optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto] verbose: True # whether to print verbose output seed: 0 # random seed for reproducibility deterministic: True # whether to enable deterministic mode diff --git a/ultralytics/yolo/engine/trainer.py b/ultralytics/yolo/engine/trainer.py index b0da4df..5ef0d53 100644 --- a/ultralytics/yolo/engine/trainer.py +++ b/ultralytics/yolo/engine/trainer.py @@ -627,8 +627,10 @@ class BaseTrainer: g = [], [], [] # optimizer parameter groups bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) # normalization layers, i.e. BatchNorm2d() if name == 'auto': - name, lr, momentum = ('SGD', 0.01, 0.9) if iterations > 6000 else ('NAdam', 0.001, 0.9) - self.args.warmup_bias_lr = 0.0 # no higher than 0.01 for NAdam + nc = getattr(model, 'nc', 10) # number of classes + lr_fit = round(0.002 * 5 / (4 + nc), 6) # lr0 fit equation to 6 decimal places + name, lr, momentum = ('SGD', 0.01, 0.9) if iterations > 10000 else ('AdamW', lr_fit, 0.9) + self.args.warmup_bias_lr = 0.0 # no higher than 0.01 for Adam for module_name, module in model.named_modules(): for param_name, param in module.named_parameters(recurse=False):