Model Card for ControlNet - Hand Depth
Finetuned
ControlNet - depth from
HandRefiner .
image/png Model Details
Model converted from
checkpoint using the following command.
1 python3 /workspace/diffusers/scripts/convert_original_controlnet_to_diffusers.py \
2 --checkpoint_path inpaint_depth_control.ckpt \
3 --original_config_file control_depth_inpaint.yaml \
4 --num_in_channels 9 \
5 --to_safetensors \
6 --dump_path controlnet-depth-hand-converted
Model Description
Developed by: Wenquan Lu et al.Funded by [optional]: [More Information Needed]Shared by [optional]: [More Information Needed]Model type: ControlNetLanguage(s) (NLP): [More Information Needed]License: MITFinetuned from model [optional]: [More Information Needed]
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Uses
Direct 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.
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Training Details
Training Data
RHD
Static Gesture Dataset
Training Procedure
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Training Hyperparameters
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Evaluation
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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) .
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