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# yoloev8m-seg-torch

Model creating masks for any object type you name in text.

**Details**

- Model name: `yoloev8m-seg-torch`
- Model source: [https://docs.ultralytics.com/models/yoloe](https://docs.ultralytics.com/models/yoloe)
- Model author: Glenn Jocher, et al.
- Model license: AGPL-3.0
- Model size: 62.75 MB
- Exposes embeddings? no
- Tags: `instances, torch, yolo, zero-shot, official`

**Requirements**

- Packages: `torch>=1.7.0, torchvision>=0.8.1, ultralytics>=8.3.99, clip @ git+https://github.com/openai/CLIP.git`
- CPU support
  - yes
- GPU support
  - yes

**Example usage**

```python
import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset(
    "coco-2017",
    split="validation",
    dataset_name=fo.get_default_dataset_name(),
    max_samples=50,
    shuffle=True,
)

model = foz.load_zoo_model("yoloev8m-seg-torch")

dataset.apply_model(model, label_field="predictions")

session = fo.launch_app(dataset)

#
# Make zero-shot predictions with custom classes
#

model = foz.load_zoo_model(
    "yoloev8m-seg-torch",
    classes=["person", "dog", "cat", "bird", "car", "tree", "chair"],
)

dataset.apply_model(model, label_field="predictions")
session.refresh()
```