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MohamedRashad
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Commit
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ca938da
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Parent(s):
466a0b7
chore: Add Gradio and Pandas dependencies, and create a leaderboard for PyTorch Image Models
Browse files- app.py +56 -0
- requirements.txt +2 -0
app.py
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import gradio as gr
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import pandas as pd
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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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# columns to remove from each dataframe
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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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# Rename top1 and top5 columns to the name of the dataframe+top1/top5
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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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# Merge all dataframes
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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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# Average top1 and top5 and add the average column after `model` column
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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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# Round the values to 3 decimal places
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result = result.round(3)
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with gr.Blocks("Timm Leaderboard") as leaderboard:
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gr.HTML("<center><h1>PyTorch Image Models Leaderboard</h1></center>")
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gr.Markdown("This leaderboard is based on the results of the models from the [PyTorch Image Models]('https://github.com/huggingface/pytorch-image-models') repository.")
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gr.DataFrame(result)
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leaderboard.launch()
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requirements.txt
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gradio
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pandas
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