Deeplabv3 Mobilenet V2 1.0 513

🚀 MobileNetV2 with DeepLabV3+

A MobileNet V2 model pre - trained on PASCAL VOC at 513x513 resolution for image segmentation tasks.

🚀 Quick Start

You can use the raw model for semantic segmentation. Check out the model hub to find fine - tuned versions for tasks that interest you.

✨ Features

• The MobileNet V2 model is small, has low latency, and low power consumption, suitable for running on mobile devices.
• It can be used for various tasks such as classification, detection, embeddings, and segmentation.
• This repo adds a DeepLabV3+ head to the MobileNetV2 backbone for semantic segmentation.

📚 Documentation

Model description

From the original README:

MobileNets are small, low - latency, low - power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices [...] MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature.

The model in this repo adds a DeepLabV3+ head to the MobileNetV2 backbone for semantic segmentation.

Intended uses & limitations

You can use the raw model for semantic segmentation. See the model hub to look for fine - tuned versions on a task that interests you.

💻 Usage Examples

Basic Usage

from transformers import MobileNetV2FeatureExtractor, MobileNetV2ForSemanticSegmentation
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

feature_extractor = MobileNetV2FeatureExtractor.from_pretrained("Matthijs/deeplabv3_mobilenet_v2_1.0_513")
model = MobileNetV2ForSemanticSegmentation.from_pretrained("Matthijs/deeplabv3_mobilenet_v2_1.0_513")

inputs = feature_extractor(images=image, return_tensors="pt")

outputs = model(**inputs)
logits = outputs.logits
predicted_mask = logits.argmax(1).squeeze(0)

Currently, both the feature extractor and model support PyTorch.

📄 License

License: other

BibTeX entry and citation info

@inproceedings{deeplabv3plus2018,
  title={Encoder - Decoder with Atrous Separable Convolution for Semantic Image Segmentation},
  author={Liang - Chieh Chen and Yukun Zhu and George Papandreou and Florian Schroff and Hartwig Adam},
  booktitle={ECCV},
  year={2018}
}

Information Table