yolov8_test/docs/reference/nn/tasks.md

---
description: Learn how to work with Ultralytics YOLO Detection, Segmentation & Classification Models, load weights and parse models in PyTorch.
keywords: neural network, deep learning, computer vision, object detection, image segmentation, image classification, model ensemble, PyTorch
---

# BaseModel
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:::ultralytics.nn.tasks.BaseModel
<br><br>

# DetectionModel
---
:::ultralytics.nn.tasks.DetectionModel
<br><br>

# SegmentationModel
---
:::ultralytics.nn.tasks.SegmentationModel
<br><br>

# PoseModel
---
:::ultralytics.nn.tasks.PoseModel
<br><br>

# ClassificationModel
---
:::ultralytics.nn.tasks.ClassificationModel
<br><br>

# RTDETRDetectionModel
---
:::ultralytics.nn.tasks.RTDETRDetectionModel
<br><br>

# Ensemble
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:::ultralytics.nn.tasks.Ensemble
<br><br>

# torch_safe_load
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:::ultralytics.nn.tasks.torch_safe_load
<br><br>

# attempt_load_weights
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:::ultralytics.nn.tasks.attempt_load_weights
<br><br>

# attempt_load_one_weight
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:::ultralytics.nn.tasks.attempt_load_one_weight
<br><br>

# parse_model
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:::ultralytics.nn.tasks.parse_model
<br><br>

# yaml_model_load
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:::ultralytics.nn.tasks.yaml_model_load
<br><br>

# guess_model_scale
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:::ultralytics.nn.tasks.guess_model_scale
<br><br>

# guess_model_task
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:::ultralytics.nn.tasks.guess_model_task
<br><br>