license: apache-2.0
tags:
- darknet
- yolo
yolo-lightnet
This is an optimized YOLO model. It is optimized for running on NVDLA.
NOTE: This is darknet format NOT Pytorch.
Model Details
File name is formatted like lightnet-{name}-{resolution}.weights.
driving
- target: car, bus, person, bike, truck, motor, train, rider, traffic_sign, traffic_light
- training data: BDD100K
face
- target: face
- train data: wider face
head_body
- target: head, body(include hidden area)
- train data: crowdhuman
head_body-visible
- target: head, body(only visible area)
- train data: crowdhuman
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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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.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
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Evaluation
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
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]
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]
BibTeX:
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APA:
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Glossary [optional]
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More Information [optional]
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Model Card Authors [optional]
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Model Card Contact
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