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I tried to reproduce the CoNSeP results mentioned in the HoVerNet paper. For this, I used the test images of the CoNSeP dataset, performed inference and calculated the results given the compute_stats.py from the HoVerNet repository. However, I "just" achieve: [ 0.8365, 0.5349, 0.6565, 0.7670, 0.5051]
vs. the referenced scores in the paper: [ 0.8530, 0.5710, 0.7020, 0.7780, 0.5470]
What I Did
import logging
import warnings
import os
os.environ["HOME"] = "/home/jovyan/hovernet"
if logging.getLogger().hasHandlers():
logging.getLogger().handlers.clear()
import cv2
import joblib
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path
from tiatoolbox import logger
from tiatoolbox.models.engine.nucleus_instance_segmentor import NucleusInstanceSegmentor
from tiatoolbox.utils.misc import download_data, imread
from natsort import natsorted as sorted
ON_GPU = True
INPUT_FOLDER = "consep-test-images"
OUTPUT_FOLDER = "results"
if __name__ == "__main__":
filelist = [str(f) for f in sorted(Path(INPUT_FOLDER).glob("*.png"))]
inst_segmentor = NucleusInstanceSegmentor(
pretrained_model="hovernet_original-consep",
num_loader_workers=4,
num_postproc_workers=4,
batch_size=4,
)
tile_output = inst_segmentor.predict(
filelist,
save_dir=OUTPUT_FOLDER,
mode="tile",
on_gpu=ON_GPU,
crash_on_exception=True,
)
The text was updated successfully, but these errors were encountered:
Description
I tried to reproduce the CoNSeP results mentioned in the HoVerNet paper. For this, I used the test images of the CoNSeP dataset, performed inference and calculated the results given the compute_stats.py from the HoVerNet repository. However, I "just" achieve:
[ 0.8365, 0.5349, 0.6565, 0.7670, 0.5051]
vs. the referenced scores in the paper:
[ 0.8530, 0.5710, 0.7020, 0.7780, 0.5470]
What I Did
The text was updated successfully, but these errors were encountered: