Create app

adding file

app

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+import gradio as gr
+import plotly.graph_objects as go
+import numpy as np
+import pandas as pd
+
+
+def create_sota_plot():
+    # State-of-the-art models data
+    sota_models = {
+        'SIFT + FVs': (2012, 53),
+        'AlexNet': (2012.5, 65),
+        'SPPNet': (2014.5, 74),
+        'Inception V3': (2015.5, 81),
+        'NASNET-A(6)': (2017, 82.7),
+        'CoAtNet-7': (2021.5, 90.88),
+        '': (2022, 87.79),  # Last point
+        '': (2022.2, 87.73)  # Final value shown
+    }
+
+    # Extract data for SOTA models
+    sota_years = [year for year, _ in sota_models.values() if year != '']
+    sota_accuracy = [acc for _, acc in sota_models.values() if acc != '']
+    sota_labels = [name for name in sota_models.keys() if name != '']
+
+    # Generate synthetic "other models" data (gray points)
+    np.random.seed(42)
+    n_other = 300
+    other_years = np.random.uniform(2010, 2023, n_other)
+    # Create a distribution that's mostly below SOTA but with some variance
+    other_accuracy = []
+    for year in other_years:
+        # Find approximate SOTA accuracy for this year
+        sota_at_year = np.interp(year, sota_years[:len(sota_accuracy)], sota_accuracy[:len(sota_accuracy)])
+        # Add models mostly below SOTA with some variance
+        if year < 2012:
+            acc = np.random.normal(45, 8)
+        else:
+            acc = np.random.normal(sota_at_year - 10, 5)
+            # Some models can be close to SOTA
+            if np.random.random() < 0.1:
+                acc = sota_at_year - np.random.uniform(0, 3)
+        other_accuracy.append(max(30, min(92, acc)))  # Clip to reasonable range
+
+    # Create the plot
+    fig = go.Figure()
+
+    # Add other models (gray scatter points)
+    fig.add_trace(go.Scatter(
+        x=other_years,
+        y=other_accuracy,
+        mode='markers',
+        name='Other models',
+        marker=dict(
+            color='lightgray',
+            size=6,
+            opacity=0.5
+        ),
+        hovertemplate='Year: %{x:.1f}<br>Accuracy: %{y:.1f}%<extra></extra>'
+    ))
+
+    # Add SOTA models line
+    fig.add_trace(go.Scatter(
+        x=sota_years[:len(sota_accuracy)],
+        y=sota_accuracy,
+        mode='lines+markers',
+        name='State-of-the-art models',
+        line=dict(color='#00CED1', width=3),
+        marker=dict(size=10, color='#00CED1'),
+        hovertemplate='%{text}<br>Year: %{x:.1f}<br>Accuracy: %{y:.1f}%<extra></extra>',
+        text=sota_labels[:len(sota_accuracy)]
+    ))
+
+    # Add labels for SOTA models
+    for i, (name, (year, acc)) in enumerate(sota_models.items()):
+        if name and i < len(sota_accuracy):  # Only label points with names
+            fig.add_annotation(
+                x=year,
+                y=acc,
+                text=name,
+                showarrow=False,
+                yshift=15,
+                font=dict(size=12)
+            )
+
+    # Add the final accuracy values
+    fig.add_annotation(
+        x=2022,
+        y=87.79,
+        text="87.79",
+        showarrow=False,
+        xshift=30,
+        font=dict(size=12, weight='bold')
+    )
+
+    fig.add_annotation(
+        x=2022.2,
+        y=87.73,
+        text="87.73",
+        showarrow=False,
+        xshift=30,
+        yshift=-10,
+        font=dict(size=12)
+    )
+
+    # Update layout
+    fig.update_layout(
+        title='Evolution of Model Performance on ImageNet',
+        xaxis_title='Year',
+        yaxis_title='TOP-1 ACCURACY',
+        xaxis=dict(
+            range=[2010, 2023],
+            tickmode='linear',
+            tick0=2012,
+            dtick=2,
+            showgrid=True,
+            gridcolor='lightgray'
+        ),
+        yaxis=dict(
+            range=[35, 100],
+            tickmode='linear',
+            tick0=40,
+            dtick=10,
+            showgrid=True,
+            gridcolor='lightgray'
+        ),
+        plot_bgcolor='white',
+        paper_bgcolor='white',
+        height=500,
+        legend=dict(
+            yanchor="bottom",
+            y=0.01,
+            xanchor="center",
+            x=0.5,
+            orientation="h"
+        )
+    )
+
+    return fig
+
+
+# Create Gradio interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# State-of-the-Art Models Timeline")
+    gr.Markdown(
+        "This visualization shows the evolution of state-of-the-art models' performance over time, similar to the ImageNet benchmark progression.")
+
+    plot = gr.Plot(label="Model Performance Evolution")
+
+    # Create plot on load
+    demo.load(fn=create_sota_plot, outputs=plot)
+
+    # Add interactive controls
+    with gr.Row():
+        refresh_btn = gr.Button("Refresh Plot")
+
+    refresh_btn.click(fn=create_sota_plot, outputs=plot)
+
+    gr.Markdown("""
+    ### About this visualization:
+    - **Cyan line**: State-of-the-art models showing the progression of best performance
+    - **Gray dots**: Other models representing the broader research landscape
+    - The plot shows how breakthrough models like AlexNet, Inception, and NASNET pushed the boundaries
+    - Notice the rapid improvement from 2012-2018, followed by more incremental gains
+    """)
+
+if __name__ == "__main__":
+    demo.launch()