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import gradio as gr |
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import pandas as pd |
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from fuzzywuzzy import fuzz |
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def load_leaderboard(): |
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imagenet_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet.csv') |
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imagenet_real_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet-real.csv') |
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imagenetv2_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenetv2-matched-frequency.csv') |
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sketch_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-sketch.csv') |
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imagenet_a_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet-a.csv') |
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imagenet_r_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet-r.csv') |
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remove_column_names = ["top1_err", "top5_err", "top1_diff", "top5_diff", "rank_diff"] |
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for remove_column_name in remove_column_names: |
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if remove_column_name in imagenet_df.columns: |
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imagenet_df = imagenet_df.drop(columns=remove_column_name) |
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if remove_column_name in imagenet_real_df.columns: |
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imagenet_real_df = imagenet_real_df.drop(columns=remove_column_name) |
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if remove_column_name in imagenetv2_df.columns: |
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imagenetv2_df = imagenetv2_df.drop(columns=remove_column_name) |
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if remove_column_name in sketch_df.columns: |
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sketch_df = sketch_df.drop(columns=remove_column_name) |
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if remove_column_name in imagenet_a_df.columns: |
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imagenet_a_df = imagenet_a_df.drop(columns=remove_column_name) |
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if remove_column_name in imagenet_r_df.columns: |
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imagenet_r_df = imagenet_r_df.drop(columns=remove_column_name) |
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imagenet_df = imagenet_df.rename(columns={"top1": "imagenet_top1", "top5": "imagenet_top5"}) |
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imagenet_real_df = imagenet_real_df.rename(columns={"top1": "imagenet_real_top1", "top5": "imagenet_real_top5"}) |
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imagenetv2_df = imagenetv2_df.rename(columns={"top1": "imagenetv2_top1", "top5": "imagenetv2_top5"}) |
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sketch_df = sketch_df.rename(columns={"top1": "sketch_top1", "top5": "sketch_top5"}) |
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imagenet_a_df = imagenet_a_df.rename(columns={"top1": "imagenet_a_top1", "top5": "imagenet_a_top5"}) |
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imagenet_r_df = imagenet_r_df.rename(columns={"top1": "imagenet_r_top1", "top5": "imagenet_r_top5"}) |
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result = pd.merge(imagenet_df, imagenet_real_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer') |
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result = pd.merge(result, imagenetv2_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer') |
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result = pd.merge(result, sketch_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer') |
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result = pd.merge(result, imagenet_a_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer') |
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result = pd.merge(result, imagenet_r_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer') |
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result['average_top1'] = result[['imagenet_top1', 'imagenet_real_top1', 'imagenetv2_top1', 'sketch_top1', 'imagenet_a_top1', 'imagenet_r_top1']].mean(axis=1) |
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result['average_top5'] = result[['imagenet_top5', 'imagenet_real_top5', 'imagenetv2_top5', 'sketch_top5', 'imagenet_a_top5', 'imagenet_r_top5']].mean(axis=1) |
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result = result[['model', 'average_top1', 'average_top5', 'param_count', 'img_size', 'crop_pct', 'interpolation', 'imagenet_top1', 'imagenet_top5', 'imagenet_real_top1', 'imagenet_real_top5', 'imagenetv2_top1', 'imagenetv2_top5', 'sketch_top1', 'sketch_top5', 'imagenet_a_top1', 'imagenet_a_top5', 'imagenet_r_top1', 'imagenet_r_top5']] |
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result = result.sort_values(by='average_top1', ascending=False) |
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result = result.round(3) |
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return result |
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global df |
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df = load_leaderboard() |
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def search_leaderboard(model_name): |
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if not model_name: |
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return df |
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threshold = 95 |
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def calculate_similarity(row): |
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similarity = fuzz.partial_ratio(model_name.lower(), row['model'].lower()) |
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return similarity if similarity >= threshold else 0 |
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df['similarity'] = df.apply(calculate_similarity, axis=1) |
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filtered_df = df[df['similarity'] > 0].sort_values('similarity', ascending=False) |
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filtered_df = filtered_df.drop('similarity', axis=1) |
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return filtered_df |
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with gr.Blocks("Timm Leaderboard") as app: |
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gr.HTML("<center><h1>PyTorch Image Models Leaderboard</h1></center>") |
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gr.HTML("<p>This leaderboard is based on the results of the models from the <a href='https://github.com/huggingface/pytorch-image-models'>PyTorch Image Models</a> repository.</p>") |
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with gr.Row(): |
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search_bar = gr.Textbox(lines=1, label="Search Model (You can press Enter to Search)", placeholder="Search for a model", scale=4) |
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search_btn = gr.Button(value="Search", variant="primary", scale=1) |
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leaderboard = gr.Dataframe(df) |
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refresh_button = gr.Button(value="Refresh Leaderboard", variant="primary") |
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refresh_button.click(load_leaderboard, outputs=[leaderboard]) |
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search_btn.click(search_leaderboard, inputs=[search_bar], outputs=[leaderboard]) |
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search_bar.submit(search_leaderboard, inputs=[search_bar], outputs=[leaderboard]) |
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app.launch() |