Update Dockerfile `FROM pytorch/pytorch:latest` (#10902)

.pre-commit-config.yaml

@@ -4,6 +4,7 @@
 default_language_version:
   python: python3.8
 
+exclude: 'docs/'
 # Define bot property if installed via https://github.com/marketplace/pre-commit-ci
 ci:
   autofix_prs: true
@@ -28,13 +29,13 @@ repos:
     hooks:
       - id: pyupgrade
         name: Upgrade code
-        args: [ --py37-plus ]
+        args: [--py37-plus]
 
-  - repo: https://github.com/PyCQA/isort
-    rev: 5.11.4
-    hooks:
-      - id: isort
-        name: Sort imports
+  # - repo: https://github.com/PyCQA/isort
+  #   rev: 5.11.4
+  #   hooks:
+  #     - id: isort
+  #       name: Sort imports
 
   - repo: https://github.com/pre-commit/mirrors-yapf
     rev: v0.32.0
@@ -50,15 +51,22 @@ repos:
         additional_dependencies:
           - mdformat-gfm
           - mdformat-black
-        exclude: "README.md|README.zh-CN.md"
-
-  - repo: https://github.com/asottile/yesqa
-    rev: v1.4.0
-    hooks:
-      - id: yesqa
+        # exclude: "README.md|README.zh-CN.md|CONTRIBUTING.md"
 
   - repo: https://github.com/PyCQA/flake8
-    rev: 6.0.0
+    rev: 5.0.4
     hooks:
       - id: flake8
         name: PEP8
+
+  #- repo: https://github.com/codespell-project/codespell
+  #  rev: v2.2.2
+  #  hooks:
+  #    - id: codespell
+  #      args:
+  #        - --ignore-words-list=crate,nd
+
+  #- repo: https://github.com/asottile/yesqa
+  #  rev: v1.4.0
+  #  hooks:
+  #    - id: yesqa

classify/tutorial.ipynb

@@ -78,7 +78,7 @@
       "source": [
         "# 1. Predict\n",
         "\n",
-        "`classify/predict.py` runs YOLOv5 Classifcation inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/predict-cls`. Example inference sources are:\n",
+        "`classify/predict.py` runs YOLOv5 Classification inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/predict-cls`. Example inference sources are:\n",
         "\n",
         "```shell\n",
         "python classify/predict.py --source 0  # webcam\n",

classify/val.py

@@ -128,9 +128,9 @@ def run(
         LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}")
         LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}")
         for i, c in model.names.items():
-            aci = acc[targets == i]
-            top1i, top5i = aci.mean(0).tolist()
-            LOGGER.info(f"{c:>24}{aci.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}")
+            acc_i = acc[targets == i]
+            top1i, top5i = acc_i.mean(0).tolist()
+            LOGGER.info(f"{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}")
 
         # Print results
         t = tuple(x.t / len(dataloader.dataset.samples) * 1E3 for x in dt)  # speeds per image

utils/docker/Dockerfile

@@ -3,23 +3,33 @@
 # Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference
 
 # Start FROM NVIDIA PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
-FROM nvcr.io/nvidia/pytorch:22.12-py3
-RUN rm -rf /opt/pytorch  # remove 1.2GB dir
+# FROM docker.io/pytorch/pytorch:latest
+FROM pytorch/pytorch:latest
 
 # Downloads to user config dir
 ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
 
 # Install linux packages
-RUN apt update && apt install --no-install-recommends -y zip htop screen libgl1-mesa-glx
+ENV DEBIAN_FRONTEND noninteractive
+RUN apt update
+RUN TZ=Etc/UTC apt install -y tzdata
+RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg
+# RUN alias python=python3
 
-# Install pip packages (uninstall torch nightly in favor of stable)
+# Create working directory
+RUN mkdir -p /usr/src/app
+WORKDIR /usr/src/app
+
+# Copy contents
+# COPY . /usr/src/app  (issues as not a .git directory)
+RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
+
+# Install pip packages
 COPY requirements.txt .
-RUN python -m pip install --upgrade pip wheel
-RUN pip uninstall -y Pillow torchtext torch torchvision
-RUN pip install --no-cache -U pycocotools  # install --upgrade
-RUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook 'opencv-python<4.6.0.66'  \
-    Pillow>=9.1.0 ultralytics \
-    --extra-index-url https://download.pytorch.org/whl/cu113
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook \
+    coremltools onnx onnx-simplifier onnxruntime openvino-dev>=2022.3
+    # tensorflow tensorflowjs \
 
 # Create working directory
 RUN mkdir -p /usr/src/app
@@ -32,6 +42,9 @@ RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
 # Set environment variables
 ENV OMP_NUM_THREADS=1
 
+# Cleanup
+ENV DEBIAN_FRONTEND teletype
+
 
 # Usage Examples -------------------------------------------------------------------------------------------------------
 

utils/loggers/__init__.py

@@ -84,10 +84,6 @@ class Loggers():
         self.csv = True  # always log to csv
 
         # Messages
-        # if not wandb:
-        #     prefix = colorstr('Weights & Biases: ')
-        #     s = f"{prefix}run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases"
-        #     self.logger.info(s)
         if not clearml:
             prefix = colorstr('ClearML: ')
             s = f"{prefix}run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML"
@@ -105,14 +101,8 @@ class Loggers():
 
         # W&B
         if wandb and 'wandb' in self.include:
-            wandb_artifact_resume = isinstance(self.opt.resume, str) and self.opt.resume.startswith('wandb-artifact://')
-            run_id = torch.load(self.weights).get('wandb_id') if self.opt.resume and not wandb_artifact_resume else None
             self.opt.hyp = self.hyp  # add hyperparameters
-            self.wandb = WandbLogger(self.opt, run_id)
-            # temp warn. because nested artifacts not supported after 0.12.10
-            # if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.11'):
-            #    s = "YOLOv5 temporarily requires wandb version 0.12.10 or below. Some features may not work as expected."
-            #    self.logger.warning(s)
+            self.wandb = WandbLogger(self.opt)
         else:
             self.wandb = None
 
@@ -175,7 +165,7 @@ class Loggers():
                 self.comet_logger.on_pretrain_routine_end(paths)
 
     def on_train_batch_end(self, model, ni, imgs, targets, paths, vals):
-        log_dict = dict(zip(self.keys[0:3], vals))
+        log_dict = dict(zip(self.keys[:3], vals))
         # Callback runs on train batch end
         # ni: number integrated batches (since train start)
         if self.plots:
@@ -221,10 +211,10 @@ class Loggers():
         # Callback runs on val end
         if self.wandb or self.clearml:
             files = sorted(self.save_dir.glob('val*.jpg'))
-            if self.wandb:
-                self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]})
-            if self.clearml:
-                self.clearml.log_debug_samples(files, title='Validation')
+        if self.wandb:
+            self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]})
+        if self.clearml:
+            self.clearml.log_debug_samples(files, title='Validation')
 
         if self.comet_logger:
             self.comet_logger.on_val_end(nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)
@@ -253,7 +243,7 @@ class Loggers():
                 for i, name in enumerate(self.best_keys):
                     self.wandb.wandb_run.summary[name] = best_results[i]  # log best results in the summary
             self.wandb.log(x)
-            self.wandb.end_epoch(best_result=best_fitness == fi)
+            self.wandb.end_epoch()
 
         if self.clearml:
             self.clearml.current_epoch_logged_images = set()  # reset epoch image limit

utils/loggers/clearml/README.md

@@ -23,7 +23,6 @@ And so much more. It's up to you how many of these tools you want to use, you ca
 
 ![ClearML scalars dashboard](https://github.com/thepycoder/clearml_screenshots/raw/main/experiment_manager_with_compare.gif)
 
-
 <br />
 <br />
 
@@ -35,15 +34,15 @@ Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-t
 
 1. Install the `clearml` python package:
 
-    ```bash
-    pip install clearml
-    ```
+   ```bash
+   pip install clearml
+   ```
 
 1. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions:
 
-    ```bash
-    clearml-init
-    ```
+   ```bash
+   clearml-init
+   ```
 
 That's it! You're done 😎
 
@@ -60,18 +59,20 @@ pip install clearml>=1.2.0
 This will enable integration with the YOLOv5 training script. Every training run from now on, will be captured and stored by the ClearML experiment manager.
 
 If you want to change the `project_name` or `task_name`, use the `--project` and `--name` arguments of the `train.py` script, by default the project will be called `YOLOv5` and the task `Training`.
-PLEASE NOTE: ClearML uses `/` as a delimter for subprojects, so be careful when using `/` in your project name!
+PLEASE NOTE: ClearML uses `/` as a delimiter for subprojects, so be careful when using `/` in your project name!
 
 ```bash
 python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache
 ```
 
 or with custom project and task name:
+
 ```bash
 python train.py --project my_project --name my_training --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache
 ```
 
 This will capture:
+
 - Source code + uncommitted changes
 - Installed packages
 - (Hyper)parameters
@@ -94,7 +95,7 @@ There even more we can do with all of this information, like hyperparameter opti
 
 ## 🔗 Dataset Version Management
 
-Versioning your data separately from your code is generally a good idea and makes it easy to aqcuire the latest version too. This repository supports supplying a dataset version ID and it will make sure to get the data if it's not there yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know for sure which data was used in which experiment!
+Versioning your data separately from your code is generally a good idea and makes it easy to acquire the latest version too. This repository supports supplying a dataset version ID, and it will make sure to get the data if it's not there yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know for sure which data was used in which experiment!
 
 ![ClearML Dataset Interface](https://github.com/thepycoder/clearml_screenshots/raw/main/clearml_data.gif)
 
@@ -112,6 +113,7 @@ The YOLOv5 repository supports a number of different datasets by using yaml file
         |_ LICENSE
         |_ README.txt
 ```
+
 But this can be any dataset you wish. Feel free to use your own, as long as you keep to this folder structure.
 
 Next, ⚠️**copy the corresponding yaml file to the root of the dataset folder**⚠️. This yaml files contains the information ClearML will need to properly use the dataset. You can make this yourself too, of course, just follow the structure of the example yamls.
@@ -132,13 +134,15 @@ Basically we need the following keys: `path`, `train`, `test`, `val`, `nc`, `nam
 
 ### Upload Your Dataset
 
-To get this dataset into ClearML as a versionned dataset, go to the dataset root folder and run the following command:
+To get this dataset into ClearML as a versioned dataset, go to the dataset root folder and run the following command:
+
 ```bash
 cd coco128
 clearml-data sync --project YOLOv5 --name coco128 --folder .
 ```
 
 The command `clearml-data sync` is actually a shorthand command. You could also run these commands one after the other:
+
 ```bash
 # Optionally add --parent <parent_dataset_id> if you want to base
 # this version on another dataset version, so no duplicate files are uploaded!
@@ -177,7 +181,7 @@ python utils/loggers/clearml/hpo.py
 
 ## 🤯 Remote Execution (advanced)
 
-Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access to a very powerful GPU machine on-site or you have some budget to use cloud GPUs.
+Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access to a very powerful GPU machine on-site, or you have some budget to use cloud GPUs.
 This is where the ClearML Agent comes into play. Check out what the agent can do here:
 
 - [YouTube video](https://youtu.be/MX3BrXnaULs)
@@ -186,6 +190,7 @@ This is where the ClearML Agent comes into play. Check out what the agent can do
 In short: every experiment tracked by the experiment manager contains enough information to reproduce it on a different machine (installed packages, uncommitted changes etc.). So a ClearML agent does just that: it listens to a queue for incoming tasks and when it finds one, it recreates the environment and runs it while still reporting scalars, plots etc. to the experiment manager.
 
 You can turn any machine (a cloud VM, a local GPU machine, your own laptop ... ) into a ClearML agent by simply running:
+
 ```bash
 clearml-agent daemon --queue <queues_to_listen_to> [--docker]
 ```
@@ -194,11 +199,11 @@ clearml-agent daemon --queue <queues_to_listen_to> [--docker]
 
 With our agent running, we can give it some work. Remember from the HPO section that we can clone a task and edit the hyperparameters? We can do that from the interface too!
 
-🪄 Clone the experiment by right clicking it
+🪄 Clone the experiment by right-clicking it
 
 🎯 Edit the hyperparameters to what you wish them to be
 
-⏳ Enqueue the task to any of the queues by right clicking it
+⏳ Enqueue the task to any of the queues by right-clicking it
 
 ![Enqueue a task from the UI](https://github.com/thepycoder/clearml_screenshots/raw/main/enqueue.gif)
 
@@ -206,7 +211,8 @@ With our agent running, we can give it some work. Remember from the HPO section
 
 Now you can clone a task like we explained above, or simply mark your current script by adding `task.execute_remotely()` and on execution it will be put into a queue, for the agent to start working on!
 
-To run the YOLOv5 training script remotely, all you have to do is add this line to the training.py script after the clearml logger has been instatiated:
+To run the YOLOv5 training script remotely, all you have to do is add this line to the training.py script after the clearml logger has been instantiated:
+
 ```python
 # ...
 # Loggers
@@ -214,16 +220,17 @@ data_dict = None
 if RANK in {-1, 0}:
     loggers = Loggers(save_dir, weights, opt, hyp, LOGGER)  # loggers instance
     if loggers.clearml:
-        loggers.clearml.task.execute_remotely(queue='my_queue')  # <------ ADD THIS LINE
+        loggers.clearml.task.execute_remotely(queue="my_queue")  # <------ ADD THIS LINE
         # Data_dict is either None is user did not choose for ClearML dataset or is filled in by ClearML
         data_dict = loggers.clearml.data_dict
 # ...
 ```
+
 When running the training script after this change, python will run the script up until that line, after which it will package the code and send it to the queue instead!
 
 ### Autoscaling workers
 
-ClearML comes with autoscalers too! This tool will automatically spin up new remote machines in the cloud of your choice (AWS, GCP, Azure) and turn them into ClearML agents for you whenever there are experiments detected in the queue. Once the tasks are processed, the autoscaler will automatically shut down the remote machines and you stop paying!
+ClearML comes with autoscalers too! This tool will automatically spin up new remote machines in the cloud of your choice (AWS, GCP, Azure) and turn them into ClearML agents for you whenever there are experiments detected in the queue. Once the tasks are processed, the autoscaler will automatically shut down the remote machines, and you stop paying!
 
 Check out the autoscalers getting started video below.
 

utils/loggers/comet/README.md

@@ -23,7 +23,7 @@ pip install comet_ml
 
 There are two ways to configure Comet with YOLOv5.
 
-You can either set your credentials through enviroment variables
+You can either set your credentials through environment variables
 
 **Environment Variables**
 
@@ -49,11 +49,12 @@ project_name=<Your Comet Project Name> # This will default to 'yolov5'
 python train.py --img 640 --batch 16 --epochs 5 --data coco128.yaml --weights yolov5s.pt
 ```
 
-That's it! Comet will automatically log your hyperparameters, command line arguments, training and valiation metrics. You can visualize and analyze your runs in the Comet UI
+That's it! Comet will automatically log your hyperparameters, command line arguments, training and validation metrics. You can visualize and analyze your runs in the Comet UI
 
 <img width="1920" alt="yolo-ui" src="https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png">
 
 # Try out an Example!
+
 Check out an example of a [completed run here](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)
 
 Or better yet, try it out yourself in this Colab Notebook
@@ -65,6 +66,7 @@ Or better yet, try it out yourself in this Colab Notebook
 By default, Comet will log the following items
 
 ## Metrics
+
 - Box Loss, Object Loss, Classification Loss for the training and validation data
 - mAP_0.5, mAP_0.5:0.95 metrics for the validation data.
 - Precision and Recall for the validation data
@@ -121,7 +123,6 @@ You can control the frequency of logged predictions and the associated images by
 
 Here is an [example project using the Panel](https://www.comet.com/examples/comet-example-yolov5?shareable=YcwMiJaZSXfcEXpGOHDD12vA1&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)
 
-
 ```shell
 python train.py \
 --img 640 \
@@ -192,6 +193,7 @@ If you would like to use a dataset from Comet Artifacts, set the `path` variable
 # contents of artifact.yaml file
 path: "comet://<workspace name>/<artifact name>:<artifact version or alias>"
 ```
+
 Then pass this file to your training script in the following way
 
 ```shell
@@ -221,7 +223,7 @@ python train.py \
 
 ## Hyperparameter Search with the Comet Optimizer
 
-YOLOv5 is also integrated with Comet's Optimizer, making is simple to visualie hyperparameter sweeps in the Comet UI.
+YOLOv5 is also integrated with Comet's Optimizer, making is simple to visualize hyperparameter sweeps in the Comet UI.
 
 ### Configuring an Optimizer Sweep
 

utils/loggers/wandb/log_dataset.py

-import argparse
-
-from wandb_utils import WandbLogger
-
-from utils.general import LOGGER
-
-WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
-
-
-def create_dataset_artifact(opt):
-    logger = WandbLogger(opt, None, job_type='Dataset Creation')  # TODO: return value unused
-    if not logger.wandb:
-        LOGGER.info("install wandb using `pip install wandb` to log the dataset")
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
-    parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
-    parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project')
-    parser.add_argument('--entity', default=None, help='W&B entity')
-    parser.add_argument('--name', type=str, default='log dataset', help='name of W&B run')
-
-    opt = parser.parse_args()
-    opt.resume = False  # Explicitly disallow resume check for dataset upload job
-
-    create_dataset_artifact(opt)

utils/loggers/wandb/sweep.py

-import sys
-from pathlib import Path
-
-import wandb
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[3]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-
-from train import parse_opt, train
-from utils.callbacks import Callbacks
-from utils.general import increment_path
-from utils.torch_utils import select_device
-
-
-def sweep():
-    wandb.init()
-    # Get hyp dict from sweep agent. Copy because train() modifies parameters which confused wandb.
-    hyp_dict = vars(wandb.config).get("_items").copy()
-
-    # Workaround: get necessary opt args
-    opt = parse_opt(known=True)
-    opt.batch_size = hyp_dict.get("batch_size")
-    opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
-    opt.epochs = hyp_dict.get("epochs")
-    opt.nosave = True
-    opt.data = hyp_dict.get("data")
-    opt.weights = str(opt.weights)
-    opt.cfg = str(opt.cfg)
-    opt.data = str(opt.data)
-    opt.hyp = str(opt.hyp)
-    opt.project = str(opt.project)
-    device = select_device(opt.device, batch_size=opt.batch_size)
-
-    # train
-    train(hyp_dict, opt, device, callbacks=Callbacks())
-
-
-if __name__ == "__main__":
-    sweep()

utils/loggers/wandb/sweep.yaml

-# Hyperparameters for training
-# To set range-
-# Provide min and max values as:
-#      parameter:
-#
-#         min: scalar
-#         max: scalar
-# OR
-#
-# Set a specific list of search space-
-#     parameter:
-#         values: [scalar1, scalar2, scalar3...]
-#
-# You can use grid, bayesian and hyperopt search strategy
-# For more info on configuring sweeps visit - https://docs.wandb.ai/guides/sweeps/configuration
-
-program: utils/loggers/wandb/sweep.py
-method: random
-metric:
-  name: metrics/mAP_0.5
-  goal: maximize
-
-parameters:
-  # hyperparameters: set either min, max range or values list
-  data:
-    value: "data/coco128.yaml"
-  batch_size:
-    values: [64]
-  epochs:
-    values: [10]
-
-  lr0:
-    distribution: uniform
-    min: 1e-5
-    max: 1e-1
-  lrf:
-    distribution: uniform
-    min: 0.01
-    max: 1.0
-  momentum:
-    distribution: uniform
-    min: 0.6
-    max: 0.98
-  weight_decay:
-    distribution: uniform
-    min: 0.0
-    max: 0.001
-  warmup_epochs:
-    distribution: uniform
-    min: 0.0
-    max: 5.0
-  warmup_momentum:
-    distribution: uniform
-    min: 0.0
-    max: 0.95
-  warmup_bias_lr:
-    distribution: uniform
-    min: 0.0
-    max: 0.2
-  box:
-    distribution: uniform
-    min: 0.02
-    max: 0.2
-  cls:
-    distribution: uniform
-    min: 0.2
-    max: 4.0
-  cls_pw:
-    distribution: uniform
-    min: 0.5
-    max: 2.0
-  obj:
-    distribution: uniform
-    min: 0.2
-    max: 4.0
-  obj_pw:
-    distribution: uniform
-    min: 0.5
-    max: 2.0
-  iou_t:
-    distribution: uniform
-    min: 0.1
-    max: 0.7
-  anchor_t:
-    distribution: uniform
-    min: 2.0
-    max: 8.0
-  fl_gamma:
-    distribution: uniform
-    min: 0.0
-    max: 4.0
-  hsv_h:
-    distribution: uniform
-    min: 0.0
-    max: 0.1
-  hsv_s:
-    distribution: uniform
-    min: 0.0
-    max: 0.9
-  hsv_v:
-    distribution: uniform
-    min: 0.0
-    max: 0.9
-  degrees:
-    distribution: uniform
-    min: 0.0
-    max: 45.0
-  translate:
-    distribution: uniform
-    min: 0.0
-    max: 0.9
-  scale:
-    distribution: uniform
-    min: 0.0
-    max: 0.9
-  shear:
-    distribution: uniform
-    min: 0.0
-    max: 10.0
-  perspective:
-    distribution: uniform
-    min: 0.0
-    max: 0.001
-  flipud:
-    distribution: uniform
-    min: 0.0
-    max: 1.0
-  fliplr:
-    distribution: uniform
-    min: 0.0
-    max: 1.0
-  mosaic:
-    distribution: uniform
-    min: 0.0
-    max: 1.0
-  mixup:
-    distribution: uniform
-    min: 0.0
-    max: 1.0
-  copy_paste:
-    distribution: uniform
-    min: 0.0
-    max: 1.0