đ Model Card for Model ID
This model card serves as a base template for new models, aiming to provide a comprehensive overview of the model.
đ Documentation
đ Model Details
đ Model Description

- Developed by: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: MIT
- Finetuned from model [optional]: [More Information Needed]
đ Model Sources [optional]
đ ī¸ Uses
đ Direct Use
This section demonstrates the model use without fine - tuning or plugging into a larger ecosystem/app.
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()
âĄī¸ Downstream Use [optional]
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â Out-of-Scope Use
This section addresses misuse, malicious use, and uses that the model will not work well for.
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â ī¸ Bias, Risks, and Limitations
This section is meant to convey both technical and sociotechnical limitations.
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đĄ Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
đ How to Get Started with the Model
Use the code below to get started with the model.
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đī¸â Training Details
đ Training Data
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đ Training Procedure
âī¸ Preprocessing [optional]
[More Information Needed]
âī¸ Training Hyperparameters
- Training regime: [More Information Needed]
âąī¸ Speeds, Sizes, Times [optional]
This section provides information about throughput, start/end time, checkpoint size if relevant.
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đ Evaluation
đ Testing Data, Factors & Metrics
đ Testing Data
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đ Factors
These are the things the evaluation is disaggregating by, e.g., subpopulations or domains.
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đ Metrics
These are the evaluation metrics being used, ideally with a description of why.
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đ Results
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đ Summary
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đą Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
âī¸ Technical Specifications [optional]
đ§ Model Architecture and Objective
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đģ Compute Infrastructure
đĨī¸ Hardware
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đŊ Software
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đ Citation [optional]
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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đ Glossary [optional]
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âšī¸ More Information [optional]
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đ Model Card Authors [optional]
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đ Model Card Contact
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