Cleanup (#197)

Co-authored-by: Onuralp Sezer <thunderbirdtr@gmail.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>

README.md

@@ -10,7 +10,7 @@
 <div>
     <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
     <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
-    <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
+    <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
     <br>
     <a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a>
     <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = "8.0.0"
+__version__ = "8.0.2"
 
 from ultralytics.hub import checks
 from ultralytics.yolo.engine.model import YOLO

ultralytics/models/README.md

@@ -1,24 +1,36 @@
-## Models HUB
+## Models
 
-Here are the models that are supported out-of-the-box with Ultralytics. For a detailed view and navigation, visit [model hub](<>) section of the docs.
+Welcome to the Ultralytics Models directory! Here you will find a wide variety of pre-configured model configuration
+files (`*.yaml`s) that can be used to create custom YOLO models. The models in this directory have been expertly crafted
+and fine-tuned by the Ultralytics team to provide the best performance for a wide range of object detection and image
+segmentation tasks.
+
+These model configurations cover a wide range of scenarios, from simple object detection to more complex tasks like
+instance segmentation and object tracking. They are also designed to run efficiently on a variety of hardware platforms,
+from CPUs to GPUs. Whether you are a seasoned machine learning practitioner or just getting started with YOLO, this
+directory provides a great starting point for your custom model development needs.
+
+To get started, simply browse through the models in this directory and find one that best suits your needs. Once you've
+selected a model, you can use the provided `*.yaml` file to train and deploy your custom YOLO model with ease. See full
+details at the Ultralytics [Docs](https://docs.ultralytics.com), and if you need help or have any questions, feel free
+to reach out to the Ultralytics team for support. So, don't wait, start creating your custom YOLO model now!
 
 ### Usage
 
-You can simply set the `model` parameter to any available yaml config or pretained weights
+Model `*.yaml` files may be used directly in the Command Line Interface (CLI) with a `yolo` command:
 
 ```bash
-yolo task=... mode=... model=yolov5n.yaml
+yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100
 ```
 
-| Model              | Version/ | size (pixels) | mAPval 50-95 | Speed CPU b1 (ms) | params (M) | FLOPs @640 (B) | model file    | Pretrained Weights |
+They may also be used directly in a Python environment, and accepts the same
-| ------------------ | -------- | ------------- | ------------ | ----------------- | ---------- | -------------- | ------------- | ------------------ |
+[arguments](https://docs.ultralytics.com/config/) as in the CLI example above:
-| YOLOv5n            | v6.3     | 640           | 28.0         | 45                | 1.9        | 4.5            | yolov5n.yaml  | -                  |
+
-| YOLOv5s            | -        | 640           | 37.4         | 98                | 7.2        | 16.5           | yolov5s.yaml  | -                  |
+```python
-| YOLOv5m            | -        | 640           | 45.4         | 224               | 21.2       | 49.0           | yolov5m.yaml  | -                  |
+from ultralytics import YOLO
-| YOLOv5l            | -        | 640           | 49.0         | 430               | 46.5       | 109.1          | yolov5l.yaml  | -                  |
+
-| YOLOv5x            | -        | 640           | 50.7         | 766               | 86.7       | 205.7          | yolov5x.yaml  | -                  |
+model = YOLO("yolov8n.yaml")  # build a YOLOv8n model from scratch
-| YOLOv5n6           | -        | 1280          | 36.0         | 153               | 3.2        | 4.6            | yolov5n6.yaml | -                  |
+
-| YOLOv5s6           | -        | 1280          | 44.8         | 385               | 12.6       | 16.8           | yolov5s6.yaml | -                  |
+model.info()  # display model information
-| YOLOv5m6           | -        | 1280          | 51.3         | 887               | 35.7       | 50.0           | yolov5m6.yaml | -                  |
+model.train(data="coco128.yaml", epochs=100)  # train the model
-| YOLOv5l6           | -        | 1280          | 53.7         | 1784              | 76.8       | 111.4          | yolov5l6.yaml | -                  |
+```
-| YOLOv5x6 + \[TTA\] | -        | 1280 1536     | 55.0 55.8    | 3136 -            | 140.7 -    | 209.8 -        | yolov5x6.yaml | -                  |

ultralytics/yolo/utils/callbacks/base.py

@@ -143,8 +143,7 @@ def add_integration_callbacks(instance):
     from .comet import callbacks as comet_callbacks
     from .hub import callbacks as hub_callbacks
     from .tensorboard import callbacks as tb_callbacks
-    from .wb import callbacks as wb_callbacks
 
-    for x in clearml_callbacks, comet_callbacks, hub_callbacks, tb_callbacks, wb_callbacks:
+    for x in clearml_callbacks, comet_callbacks, hub_callbacks, tb_callbacks:
         for k, v in x.items():
             instance.callbacks[k].append(v)  # callback[name].append(func)

ultralytics/yolo/utils/callbacks/wb.py

@@ -1,48 +0,0 @@
-# Ultralytics YOLO 🚀, GPL-3.0 license
-
-from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
-
-try:
-    import wandb
-
-    assert hasattr(wandb, '__version__')
-except (ImportError, AssertionError):
-    wandb = None
-
-
-def on_pretrain_routine_start(trainer):
-    wandb.init(project=trainer.args.project or "YOLOv8", name=trainer.args.name, config=dict(
-        trainer.args)) if not wandb.run else wandb.run
-
-
-def on_fit_epoch_end(trainer):
-    wandb.run.log(trainer.metrics, step=trainer.epoch + 1)
-    if trainer.epoch == 0:
-        model_info = {
-            "model/parameters": get_num_params(trainer.model),
-            "model/GFLOPs": round(get_flops(trainer.model), 3),
-            "model/speed(ms)": round(trainer.validator.speed[1], 3)}
-        wandb.run.log(model_info, step=trainer.epoch + 1)
-
-
-def on_train_epoch_end(trainer):
-    wandb.run.log(trainer.label_loss_items(trainer.tloss, prefix="train"), step=trainer.epoch + 1)
-    wandb.run.log(trainer.lr, step=trainer.epoch + 1)
-    if trainer.epoch == 1:
-        wandb.run.log({f.stem: wandb.Image(str(f))
-                       for f in trainer.save_dir.glob('train_batch*.jpg')},
-                      step=trainer.epoch + 1)
-
-
-def on_train_end(trainer):
-    art = wandb.Artifact(type="model", name=f"run_{wandb.run.id}_model")
-    if trainer.best.exists():
-        art.add_file(trainer.best)
-        wandb.run.log_artifact(art)
-
-
-callbacks = {
-    "on_pretrain_routine_start": on_pretrain_routine_start,
-    "on_train_epoch_end": on_train_epoch_end,
-    "on_fit_epoch_end": on_fit_epoch_end,
-    "on_train_end": on_train_end} if wandb else {}