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---
library_name: segmentation-models-pytorch
license: other
pipeline_tag: image-segmentation
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- segmentation-models-pytorch
- semantic-segmentation
- pytorch
- segformer
languages:
- python
---
# Segformer Model Card
Table of Contents:
- [Load trained model](#load-trained-model)
- [Model init parameters](#model-init-parameters)
- [Model metrics](#model-metrics)
- [Dataset](#dataset)
## Load trained model
[](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/segformer_inference_pretrained.ipynb)
1. Install requirements.
```bash
pip install -U segmentation_models_pytorch albumentations
```
2. Run inference.
```python
import torch
import requests
import numpy as np
import albumentations as A
import segmentation_models_pytorch as smp
from PIL import Image
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load pretrained model and preprocessing function
checkpoint = "smp-hub/segformer-b1-1024x1024-city-160k"
model = smp.from_pretrained(checkpoint).eval().to(device)
preprocessing = A.Compose.from_pretrained(checkpoint)
# Load image
url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
image = Image.open(requests.get(url, stream=True).raw)
# Preprocess image
np_image = np.array(image)
normalized_image = preprocessing(image=np_image)["image"]
input_tensor = torch.as_tensor(normalized_image)
input_tensor = input_tensor.permute(2, 0, 1).unsqueeze(0) # HWC -> BCHW
input_tensor = input_tensor.to(device)
# Perform inference
with torch.no_grad():
output_mask = model(input_tensor)
# Postprocess mask
mask = torch.nn.functional.interpolate(
output_mask, size=(image.height, image.width), mode="bilinear", align_corners=False
)
mask = mask.argmax(1).cpu().numpy() # argmax over predicted classes (channels dim)
```
## Model init parameters
```python
model_init_params = {
"encoder_name": "mit_b1",
"encoder_depth": 5,
"encoder_weights": None,
"decoder_segmentation_channels": 256,
"in_channels": 3,
"classes": 19,
"activation": None,
"aux_params": None
}
```
## Dataset
Dataset name: [Cityscapes](https://paperswithcode.com/dataset/cityscapes)
## More Information
- Library: https://github.com/qubvel/segmentation_models.pytorch
- Docs: https://smp.readthedocs.io/en/latest/
- License: https://github.com/NVlabs/SegFormer/blob/master/LICENSE
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) |