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ultralytics 8.0.57 Comet, AMP, Classify, Docker updates (#1601)

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Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2023-03-24 19:07:06 +01:00 committed by GitHub
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5
docker/Dockerfile
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@ -2,9 +2,8 @@
# Builds ultralytics/ultralytics:latest image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
# Image is CUDA-optimized for YOLOv8 single/multi-GPU training and inference
# Start FROM NVIDIA PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
# FROM docker.io/pytorch/pytorch:latest
FROM pytorch/pytorch:latest
# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch
FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime
# Downloads to user config dir
ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/

1
docs/modes/train.md
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@ -77,6 +77,7 @@ task.
| `cos_lr` | `False` | use cosine learning rate scheduler |
| `close_mosaic` | `10` | disable mosaic augmentation for final 10 epochs |
| `resume` | `False` | resume training from last checkpoint |
| `amp` | `True` | Automatic Mixed Precision (AMP) training, choices=[True, False] |
| `lr0` | `0.01` | initial learning rate (i.e. SGD=1E-2, Adam=1E-3) |
| `lrf` | `0.01` | final learning rate (lr0 * lrf) |
| `momentum` | `0.937` | SGD momentum/Adam beta1 |

50
docs/usage/cfg.md
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@ -14,48 +14,43 @@ YOLOv8 'yolo' CLI commands use the following syntax:
Where:
- `TASK` (optional) is one of `[detect, segment, classify]`. If it is not passed explicitly YOLOv8 will try to guess
- `TASK` (optional) is one of `[detect, segment, classify, pose]`. If it is not passed explicitly YOLOv8 will try to
guess
the `TASK` from the model type.
- `MODE` (required) is one of `[train, val, predict, export]`
- `MODE` (required) is one of `[train, val, predict, export, track, benchmark]`
- `ARGS` (optional) are any number of custom `arg=value` pairs like `imgsz=320` that override defaults.
For a full list of available `ARGS` see the [Configuration](cfg.md) page and `defaults.yaml`
GitHub [source](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/cfg/default.yaml).
#### Tasks
YOLO models can be used for a variety of tasks, including detection, segmentation, and classification. These tasks
YOLO models can be used for a variety of tasks, including detection, segmentation, classification and pose. These tasks
differ in the type of output they produce and the specific problem they are designed to solve.
- **Detect**: Detection tasks involve identifying and localizing objects or regions of interest in an image or video.
YOLO models can be used for object detection tasks by predicting the bounding boxes and class labels of objects in an
image.
- **Segment**: Segmentation tasks involve dividing an image or video into regions or pixels that correspond to
different objects or classes. YOLO models can be used for image segmentation tasks by predicting a mask or label for
each pixel in an image.
- **Classify**: Classification tasks involve assigning a class label to an input, such as an image or text. YOLO
models can be used for image classification tasks by predicting the class label of an input image.
**Detect**: For identifying and localizing objects or regions of interest in an image or video.
**Segment**: For dividing an image or video into regions or pixels that correspond to different objects or classes.
**Classify**: For predicting the class label of an input image.
**Pose**: For identifying objects and estimating their keypoints in an image or video.
| Key | Value | Description |
|--------|------------|-------------------------------------------------|
| `task` | `'detect'` | YOLO task, i.e. detect, segment, classify, pose |
#### Modes
YOLO models can be used in different modes depending on the specific problem you are trying to solve. These modes
include train, val, and predict.
include:
- **Train**: The train mode is used to train the model on a dataset. This mode is typically used during the development
and
testing phase of a model.
- **Val**: The val mode is used to evaluate the model's performance on a validation dataset. This mode is typically used
to
tune the model's hyperparameters and detect overfitting.
- **Predict**: The predict mode is used to make predictions with the model on new data. This mode is typically used in
production or when deploying the model to users.
**Train**: For training a YOLOv8 model on a custom dataset.
**Val**: For validating a YOLOv8 model after it has been trained.
**Predict**: For making predictions using a trained YOLOv8 model on new images or videos.
**Export**: For exporting a YOLOv8 model to a format that can be used for deployment.
**Track**: For tracking objects in real-time using a YOLOv8 model.
**Benchmark**: For benchmarking YOLOv8 exports (ONNX, TensorRT, etc.) speed and accuracy.
| Key | Value | Description |
|----------|------------|-----------------------------------------------------------------------------------------------|
| `task` | `'detect'` | inference task, i.e. detect, segment, or classify |
| `mode` | `'train'` | YOLO mode, i.e. train, val, predict, or export |
| `resume` | `False` | resume training from last checkpoint or custom checkpoint if passed as resume=path/to/best.pt |
| `model` | `None` | path to model file, i.e. yolov8n.pt, yolov8n.yaml |
| `data` | `None` | path to data file, i.e. coco128.yaml |
| Key | Value | Description |
|--------|-----------|---------------------------------------------------------------|
| `mode` | `'train'` | YOLO mode, i.e. train, val, predict, export, track, benchmark |
### Training
@ -93,6 +88,7 @@ task.
| `cos_lr` | `False` | use cosine learning rate scheduler |
| `close_mosaic` | `10` | disable mosaic augmentation for final 10 epochs |
| `resume` | `False` | resume training from last checkpoint |
| `amp` | `True` | Automatic Mixed Precision (AMP) training, choices=[True, False] |
| `lr0` | `0.01` | initial learning rate (i.e. SGD=1E-2, Adam=1E-3) |
| `lrf` | `0.01` | final learning rate (lr0 * lrf) |
| `momentum` | `0.937` | SGD momentum/Adam beta1 |

2
examples/tutorial.ipynb
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@ -151,7 +151,7 @@
"# Download COCO val\n",
"import torch\n",
"torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n",
"!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip"
"!unzip -q tmp.zip -d datasets && rm tmp.zip # unzip"
],
"execution_count": null,
"outputs": []

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ultralytics/__init__.py
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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
__version__ = '8.0.56'
__version__ = '8.0.57'
from ultralytics.yolo.engine.model import YOLO
from ultralytics.yolo.utils.checks import check_yolo as checks

5
ultralytics/yolo/cfg/default.yaml
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@ -1,8 +1,8 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# Default training settings and hyperparameters for medium-augmentation COCO training
task: detect # inference task, i.e. detect, segment, classify
mode: train # YOLO mode, i.e. train, val, predict, export
task: detect # YOLO task, i.e. detect, segment, classify, pose
mode: train # YOLO mode, i.e. train, val, predict, export, track, benchmark
# Train settings -------------------------------------------------------------------------------------------------------
model: # path to model file, i.e. yolov8n.pt, yolov8n.yaml
@ -30,6 +30,7 @@ rect: False # support rectangular training if mode='train', support rectangular
cos_lr: False # use cosine learning rate scheduler
close_mosaic: 10 # disable mosaic augmentation for final 10 epochs
resume: False # resume training from last checkpoint
amp: True # Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
# Segmentation
overlap_mask: True # masks should overlap during training (segment train only)
mask_ratio: 4 # mask downsample ratio (segment train only)

14
ultralytics/yolo/data/utils.py
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@ -207,12 +207,20 @@ def check_det_dataset(dataset, autodownload=True):
data = yaml_load(data, append_filename=True) # dictionary
# Checks
for k in 'train', 'val', 'names':
for k in 'train', 'val':
if k not in data:
raise SyntaxError(
emojis(f"{dataset} '{k}:' key missing ❌.\n'train', 'val' and 'names' are required in all data YAMLs."))
emojis(f"{dataset} '{k}:' key missing ❌.\n'train' and 'val' are required in all data YAMLs."))
if 'names' not in data and 'nc' not in data:
raise SyntaxError(emojis(f"{dataset} key missing ❌.\n either 'names' or 'nc' are required in all data YAMLs."))
if 'names' in data and 'nc' in data and len(data['names']) != data['nc']:
raise SyntaxError(emojis(f"{dataset} 'names' length {len(data['names'])} and 'nc: {data['nc']}' must match."))
if 'names' not in data:
data['names'] = [f'class_{i}' for i in range(data['nc'])]
else:
data['nc'] = len(data['names'])
data['names'] = check_class_names(data['names'])
data['nc'] = len(data['names'])
# Resolve paths
path = Path(extract_dir or data.get('path') or Path(data.get('yaml_file', '')).parent) # dataset root

1
ultralytics/yolo/engine/model.py
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@ -142,6 +142,7 @@ class YOLO:
self.task = task or guess_model_task(weights)
self.ckpt_path = weights
self.overrides['model'] = weights
self.overrides['task'] = self.task
def _check_is_pytorch_model(self):
"""

4
ultralytics/yolo/engine/trainer.py
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@ -203,8 +203,8 @@ class BaseTrainer:
self.model = self.model.to(self.device)
self.set_model_attributes()
# Check AMP
self.amp = torch.tensor(True).to(self.device)
if RANK in (-1, 0): # Single-GPU and DDP
self.amp = torch.tensor(self.args.amp).to(self.device) # True or False
if self.amp and RANK in (-1, 0): # Single-GPU and DDP
callbacks_backup = callbacks.default_callbacks.copy() # backup callbacks as check_amp() resets them
self.amp = torch.tensor(check_amp(self.model), device=self.device)
callbacks.default_callbacks = callbacks_backup # restore callbacks

1
ultralytics/yolo/utils/callbacks/comet.py
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@ -14,6 +14,7 @@ except (ImportError, AssertionError):
def on_pretrain_routine_start(trainer):
try:
experiment = comet_ml.Experiment(project_name=trainer.args.project or 'YOLOv8')
experiment.set_name(trainer.args.name)
experiment.log_parameters(vars(trainer.args))
except Exception as e:
LOGGER.warning(f'WARNING ⚠️ Comet installed but not initialized correctly, not logging this run. {e}')