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gradio_app.py
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gradio_app.py
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import json
from pathlib import Path
from typing import Any, Dict, List
import gradio as gr
def load_results() -> List[Dict]:
results_list = []
for model_dir in Path("models/object_detection").iterdir():
if model_dir.is_file() or model_dir.name.startswith("_"):
continue
results_file = model_dir / "results.json"
if not results_file.exists():
print(f"Results file not found for {model_dir.name}")
continue
with open(results_file) as f:
results = json.load(f)
results_list.append(results)
results_list.sort(key=lambda x: x["metadata"]["model"])
return results_list
def get_result_header() -> List[str]:
return [
"Model",
"Parameters (M)",
"mAP 50:95",
"mAP 50",
"mAP 75",
"mAP 50:95 (Small)",
"mAP 50:95 (Medium)",
"mAP 50:95 (Large)",
]
def parse_result(result: Dict) -> List[Any]:
round_digits = 3
param_count = ""
if "param_count" in result["metadata"]:
param_count = round(result["metadata"]["param_count"] / 1e6, 2)
return [
result["metadata"]["model"],
param_count,
round(result["map50_95"], round_digits),
round(result["map50"], round_digits),
round(result["map75"], round_digits),
round(result["small_objects"]["map50_95"], round_digits),
round(result["medium_objects"]["map50_95"], round_digits),
round(result["large_objects"]["map50_95"], round_digits),
]
raw_results = load_results()
results = [parse_result(result) for result in raw_results]
header = get_result_header()
with gr.Blocks() as demo:
gr.Markdown("# Model Leaderboard")
gr.HTML(
"""
<italic>powered by:  <a href='https://github.com/roboflow/supervision'>
<img src='https://supervision.roboflow.com/latest/assets/supervision-lenny.png'
height=24 width=24 style='display: inline-block'> supervision</a></italic>
"""
)
gr.DataFrame(headers=header, value=results)
demo.launch()