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https://github.com/ruvnet/RuView.git
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f3c77b1750
- Implemented the WiFi DensePose model in PyTorch, including CSI phase processing, modality translation, and DensePose prediction heads. - Added a comprehensive training utility for the model, including loss functions and training steps. - Created a CSV file to document hardware specifications, architecture details, training parameters, performance metrics, and advantages of the model.
63 lines
No EOL
1.8 KiB
Python
63 lines
No EOL
1.8 KiB
Python
import plotly.graph_objects as go
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# Data from the provided JSON
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data = {
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"wifi_same": {"AP": 43.5, "AP@50": 87.2, "AP@75": 44.6, "AP-m": 38.1, "AP-l": 46.4},
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"image_same": {"AP": 84.7, "AP@50": 94.4, "AP@75": 77.1, "AP-m": 70.3, "AP-l": 83.8},
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"wifi_diff": {"AP": 27.3, "AP@50": 51.8, "AP@75": 24.2, "AP-m": 22.1, "AP-l": 28.6}
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}
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# Extract metrics and values
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metrics = list(data["wifi_same"].keys())
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wifi_same_values = list(data["wifi_same"].values())
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image_same_values = list(data["image_same"].values())
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wifi_diff_values = list(data["wifi_diff"].values())
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# Define colors from the brand palette - using darker color for WiFi Diff
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colors = ['#1FB8CD', '#FFC185', '#5D878F']
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# Create the grouped bar chart
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fig = go.Figure()
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# Add bars for each method with hover data
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fig.add_trace(go.Bar(
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name='WiFi Same',
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x=metrics,
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y=wifi_same_values,
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marker_color=colors[0],
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hovertemplate='<b>WiFi Same</b><br>Metric: %{x}<br>Score: %{y}<extra></extra>'
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))
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fig.add_trace(go.Bar(
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name='Image Same',
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x=metrics,
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y=image_same_values,
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marker_color=colors[1],
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hovertemplate='<b>Image Same</b><br>Metric: %{x}<br>Score: %{y}<extra></extra>'
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))
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fig.add_trace(go.Bar(
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name='WiFi Diff',
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x=metrics,
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y=wifi_diff_values,
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marker_color=colors[2],
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hovertemplate='<b>WiFi Diff</b><br>Metric: %{x}<br>Score: %{y}<extra></extra>'
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))
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# Update layout
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fig.update_layout(
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title='DensePose Performance Comparison',
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xaxis_title='AP Metrics',
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yaxis_title='Score',
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barmode='group',
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legend=dict(orientation='h', yanchor='bottom', y=1.05, xanchor='center', x=0.5),
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plot_bgcolor='rgba(0,0,0,0)',
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paper_bgcolor='white'
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)
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# Add grid for better readability
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fig.update_yaxes(showgrid=True, gridcolor='lightgray')
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fig.update_xaxes(showgrid=False)
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# Save the chart
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fig.write_image('densepose_performance_chart.png') |