bbsnet

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Model Details

Model Description

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• Language(s) (NLP): [More Information Needed]
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Model Sources [optional]

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• Repository: https://github.com/DengPingFan/BBS-Net
• Paper [optional]: BBS-Net: RGB-D salient object detection with a bifurcated backbone strategy network, 2020
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Uses

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Direct Use

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from typing import Dict

import numpy as np
from datasets import load_dataset
from matplotlib import cm
from PIL import Image
from torch import Tensor
from transformers import AutoImageProcessor, AutoModel

model = AutoModel.from_pretrained("RGBD-SOD/bbsnet", trust_remote_code=True)
image_processor = AutoImageProcessor.from_pretrained(
    "RGBD-SOD/bbsnet", trust_remote_code=True
)
dataset = load_dataset("RGBD-SOD/test", "v1", split="train", cache_dir="data")

index = 0

"""
Get a specific sample from the dataset

sample = {
    'depth': <PIL.PngImagePlugin.PngImageFile image mode=L size=640x360>, 
    'rgb': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=640x360>, 
    'gt': <PIL.PngImagePlugin.PngImageFile image mode=L size=640x360>, 
    'name': 'COME_Train_5'
}
"""
sample = dataset[index]

depth: Image.Image = sample["depth"]
rgb: Image.Image = sample["rgb"]
gt: Image.Image = sample["gt"]
name: str = sample["name"]


"""
1. Preprocessing step

preprocessed_sample = {
    'rgb': tensor([[[[-0.8507, ....0365]]]]), 
    'gt': tensor([[[[0., 0., 0...., 0.]]]]), 
    'depth': tensor([[[[0.9529, 0....3490]]]])
}
"""
preprocessed_sample: Dict[str, Tensor] = image_processor.preprocess(sample)

"""
2. Prediction step

output = {
    'logits': tensor([[[[-5.1966, ...ackward0>)
}
"""
output: Dict[str, Tensor] = model(
    preprocessed_sample["rgb"], preprocessed_sample["depth"]
)

"""
3. Postprocessing step
"""
postprocessed_sample: np.ndarray = image_processor.postprocess(
    output["logits"], [sample["gt"].size[1], sample["gt"].size[0]]
)
prediction = Image.fromarray(np.uint8(cm.gist_earth(postprocessed_sample) * 255))

"""
Show the predicted salient map and the corresponding ground-truth(GT)
"""
prediction.show()
gt.show()

Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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BibTeX:

@inproceedings{fan2020bbs,
  title={BBS-Net: RGB-D salient object detection with a bifurcated backbone strategy network},
  author={Fan, Deng-Ping and Zhai, Yingjie and Borji, Ali and Yang, Jufeng and Shao, Ling},
  booktitle={Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XII},
  pages={275--292},
  year={2020},
  organization={Springer}
}

APA:

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Glossary [optional]

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