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| import gradio as gr | |
| import pandas as pd | |
| from fuzzywuzzy import fuzz | |
| def load_leaderboard(): | |
| imagenet_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet.csv') | |
| imagenet_real_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet-real.csv') | |
| imagenetv2_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenetv2-matched-frequency.csv') | |
| sketch_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-sketch.csv') | |
| imagenet_a_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet-a.csv') | |
| imagenet_r_df = pd.read_csv('https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet-r.csv') | |
| # columns to remove from each dataframe | |
| remove_column_names = ["top1_err", "top5_err", "top1_diff", "top5_diff", "rank_diff"] | |
| for remove_column_name in remove_column_names: | |
| if remove_column_name in imagenet_df.columns: | |
| imagenet_df = imagenet_df.drop(columns=remove_column_name) | |
| if remove_column_name in imagenet_real_df.columns: | |
| imagenet_real_df = imagenet_real_df.drop(columns=remove_column_name) | |
| if remove_column_name in imagenetv2_df.columns: | |
| imagenetv2_df = imagenetv2_df.drop(columns=remove_column_name) | |
| if remove_column_name in sketch_df.columns: | |
| sketch_df = sketch_df.drop(columns=remove_column_name) | |
| if remove_column_name in imagenet_a_df.columns: | |
| imagenet_a_df = imagenet_a_df.drop(columns=remove_column_name) | |
| if remove_column_name in imagenet_r_df.columns: | |
| imagenet_r_df = imagenet_r_df.drop(columns=remove_column_name) | |
| # Rename top1 and top5 columns to the name of the dataframe+top1/top5 | |
| imagenet_df = imagenet_df.rename(columns={"top1": "imagenet_top1", "top5": "imagenet_top5"}) | |
| imagenet_real_df = imagenet_real_df.rename(columns={"top1": "imagenet_real_top1", "top5": "imagenet_real_top5"}) | |
| imagenetv2_df = imagenetv2_df.rename(columns={"top1": "imagenetv2_top1", "top5": "imagenetv2_top5"}) | |
| sketch_df = sketch_df.rename(columns={"top1": "sketch_top1", "top5": "sketch_top5"}) | |
| imagenet_a_df = imagenet_a_df.rename(columns={"top1": "imagenet_a_top1", "top5": "imagenet_a_top5"}) | |
| imagenet_r_df = imagenet_r_df.rename(columns={"top1": "imagenet_r_top1", "top5": "imagenet_r_top5"}) | |
| # Merge all dataframes | |
| result = pd.merge(imagenet_df, imagenet_real_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer') | |
| result = pd.merge(result, imagenetv2_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer') | |
| result = pd.merge(result, sketch_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer') | |
| result = pd.merge(result, imagenet_a_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer') | |
| result = pd.merge(result, imagenet_r_df, on=['model', 'param_count', 'img_size', 'crop_pct', 'interpolation'], how='outer') | |
| # Average top1 and top5 and add the average column after `model` column | |
| result['average_top1'] = result[['imagenet_top1', 'imagenet_real_top1', 'imagenetv2_top1', 'sketch_top1', 'imagenet_a_top1', 'imagenet_r_top1']].mean(axis=1) | |
| result['average_top5'] = result[['imagenet_top5', 'imagenet_real_top5', 'imagenetv2_top5', 'sketch_top5', 'imagenet_a_top5', 'imagenet_r_top5']].mean(axis=1) | |
| 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']] | |
| result = result.sort_values(by='average_top1', ascending=False) | |
| # Round the values to 3 decimal places | |
| result = result.round(3) | |
| return result | |
| global df | |
| df = load_leaderboard() | |
| def search_leaderboard(model_name): | |
| if not model_name: | |
| return df | |
| threshold = 95 # You can adjust this value to make the search more or less strict | |
| def calculate_similarity(row): | |
| similarity = fuzz.partial_ratio(model_name.lower(), row['model'].lower()) | |
| return similarity if similarity >= threshold else 0 | |
| # Add a new column for similarity scores | |
| df['similarity'] = df.apply(calculate_similarity, axis=1) | |
| # Filter and sort the dataframe | |
| filtered_df = df[df['similarity'] > 0].sort_values('similarity', ascending=False) | |
| # Remove the similarity column before returning | |
| filtered_df = filtered_df.drop('similarity', axis=1) | |
| return filtered_df | |
| with gr.Blocks("Timm Leaderboard") as app: | |
| gr.HTML("<center><h1>PyTorch Image Models Leaderboard</h1></center>") | |
| 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>") | |
| with gr.Row(): | |
| search_bar = gr.Textbox(lines=1, label="Search Model (You can press Enter to Search)", placeholder="Search for a model", scale=4) | |
| search_btn = gr.Button(value="Search", variant="primary", scale=1) | |
| leaderboard = gr.Dataframe(df) | |
| refresh_button = gr.Button(value="Refresh Leaderboard", variant="primary") | |
| refresh_button.click(load_leaderboard, outputs=[leaderboard]) | |
| search_btn.click(search_leaderboard, inputs=[search_bar], outputs=[leaderboard]) | |
| search_bar.submit(search_leaderboard, inputs=[search_bar], outputs=[leaderboard]) | |
| app.launch() |