Cleanup (#197)
Co-authored-by: Onuralp Sezer <thunderbirdtr@gmail.com> Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>single_channel
Glenn Jocher
2 years ago
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## Models HUB
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## Models
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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.
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Welcome to the Ultralytics Models directory! Here you will find a wide variety of pre-configured model configuration
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files (`*.yaml`s) that can be used to create custom YOLO models. The models in this directory have been expertly crafted
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and fine-tuned by the Ultralytics team to provide the best performance for a wide range of object detection and image
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segmentation tasks.
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These model configurations cover a wide range of scenarios, from simple object detection to more complex tasks like
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instance segmentation and object tracking. They are also designed to run efficiently on a variety of hardware platforms,
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from CPUs to GPUs. Whether you are a seasoned machine learning practitioner or just getting started with YOLO, this
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directory provides a great starting point for your custom model development needs.
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To get started, simply browse through the models in this directory and find one that best suits your needs. Once you've
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selected a model, you can use the provided `*.yaml` file to train and deploy your custom YOLO model with ease. See full
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details at the Ultralytics [Docs](https://docs.ultralytics.com), and if you need help or have any questions, feel free
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to reach out to the Ultralytics team for support. So, don't wait, start creating your custom YOLO model now!
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### Usage
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You can simply set the `model` parameter to any available yaml config or pretained weights
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Model `*.yaml` files may be used directly in the Command Line Interface (CLI) with a `yolo` command:
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```bash
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yolo task=... mode=... model=yolov5n.yaml
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yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100
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```
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| Model | Version/ | size (pixels) | mAPval 50-95 | Speed CPU b1 (ms) | params (M) | FLOPs @640 (B) | model file | Pretrained Weights |
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| ------------------ | -------- | ------------- | ------------ | ----------------- | ---------- | -------------- | ------------- | ------------------ |
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| YOLOv5n | v6.3 | 640 | 28.0 | 45 | 1.9 | 4.5 | yolov5n.yaml | - |
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| YOLOv5s | - | 640 | 37.4 | 98 | 7.2 | 16.5 | yolov5s.yaml | - |
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| YOLOv5m | - | 640 | 45.4 | 224 | 21.2 | 49.0 | yolov5m.yaml | - |
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| YOLOv5l | - | 640 | 49.0 | 430 | 46.5 | 109.1 | yolov5l.yaml | - |
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| YOLOv5x | - | 640 | 50.7 | 766 | 86.7 | 205.7 | yolov5x.yaml | - |
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| YOLOv5n6 | - | 1280 | 36.0 | 153 | 3.2 | 4.6 | yolov5n6.yaml | - |
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| YOLOv5s6 | - | 1280 | 44.8 | 385 | 12.6 | 16.8 | yolov5s6.yaml | - |
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| YOLOv5m6 | - | 1280 | 51.3 | 887 | 35.7 | 50.0 | yolov5m6.yaml | - |
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| YOLOv5l6 | - | 1280 | 53.7 | 1784 | 76.8 | 111.4 | yolov5l6.yaml | - |
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| YOLOv5x6 + \[TTA\] | - | 1280 1536 | 55.0 55.8 | 3136 - | 140.7 - | 209.8 - | yolov5x6.yaml | - |
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They may also be used directly in a Python environment, and accepts the same
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[arguments](https://docs.ultralytics.com/config/) as in the CLI example above:
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```python
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from ultralytics import YOLO
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model = YOLO("yolov8n.yaml") # build a YOLOv8n model from scratch
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model.info() # display model information
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model.train(data="coco128.yaml", epochs=100) # train the model
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```
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# Ultralytics YOLO 🚀, GPL-3.0 license
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from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
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try:
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import wandb
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assert hasattr(wandb, '__version__')
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except (ImportError, AssertionError):
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wandb = None
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def on_pretrain_routine_start(trainer):
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wandb.init(project=trainer.args.project or "YOLOv8", name=trainer.args.name, config=dict(
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trainer.args)) if not wandb.run else wandb.run
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def on_fit_epoch_end(trainer):
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wandb.run.log(trainer.metrics, step=trainer.epoch + 1)
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if trainer.epoch == 0:
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model_info = {
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"model/parameters": get_num_params(trainer.model),
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"model/GFLOPs": round(get_flops(trainer.model), 3),
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"model/speed(ms)": round(trainer.validator.speed[1], 3)}
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wandb.run.log(model_info, step=trainer.epoch + 1)
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def on_train_epoch_end(trainer):
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wandb.run.log(trainer.label_loss_items(trainer.tloss, prefix="train"), step=trainer.epoch + 1)
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wandb.run.log(trainer.lr, step=trainer.epoch + 1)
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if trainer.epoch == 1:
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wandb.run.log({f.stem: wandb.Image(str(f))
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for f in trainer.save_dir.glob('train_batch*.jpg')},
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step=trainer.epoch + 1)
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def on_train_end(trainer):
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art = wandb.Artifact(type="model", name=f"run_{wandb.run.id}_model")
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if trainer.best.exists():
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art.add_file(trainer.best)
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wandb.run.log_artifact(art)
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callbacks = {
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"on_pretrain_routine_start": on_pretrain_routine_start,
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"on_train_epoch_end": on_train_epoch_end,
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"on_fit_epoch_end": on_fit_epoch_end,
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"on_train_end": on_train_end} if wandb else {}
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