<jupyter_start><jupyter_text>CoNSeP<jupyter_code>if __name__ == "__main__":
    true_dir = r'DATA\CoNSeP\Test\Labels'
    pred_dir = r"test_output\mat"
    
    mode = "instance";
    if mode == "instance":
        run_nuclei_inst_stat(pred_dir, true_dir, print_img_stats=False)
    if mode == "type":
        run_nuclei_type_stat(pred_dir, true_dir)<jupyter_output>test_output\mat
[ 0.84817  0.54631  0.66803  0.77078  0.51617  0.57440]<jupyter_text>PanNuke<jupyter_code>if __name__ == "__main__":
    true_dir = r'dataset\PanNuke\Fold 1\masks\fold1\consep_format'
    pred_dir = r"test_output\PanNuke\mat"
    
    mode = "instance";
    if mode == "instance":
        run_nuclei_inst_stat(pred_dir, true_dir, print_img_stats=False)
    if mode == "type":
        run_nuclei_type_stat(pred_dir, true_dir)<jupyter_output>test_output\PanNuke\mat
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d[...]<jupyter_text>New Weights after Retraining with merged data  CONSEP<jupyter_code>if __name__ == "__main__":
    true_dir = r'dataset\CoNSeP\Test\Labels'
    pred_dir = r"test_output\new_retrained_weights\consep_new\mat"
    
    mode = "instance";
    if mode == "instance":
        run_nuclei_inst_stat(pred_dir, true_dir, print_img_stats=False)
    if mode == "type":
        run_nuclei_type_stat(pred_dir, true_dir)<jupyter_output>test_output\new_retrained_weights\consep_new\mat
(6, 14)
[[ 0.85906  0.84276  0.85231  0.84266  0.85212  0.79813  0.77090  0.87562
   0.88604  0.82256  0.86871  0.81239  0.86988  0.83922]
 [ 0.53889  0.58563  0.44516  0.62305  0.53209  0.60021  0.51685  0.58422
   0.58612  0.51957  0.54445  0.47920  0.48632  0.53758]
 [ 0.70254  0.73469  0.49383  0.75878  0.66981  0.73112  0.64646  0.66038
   0.66741  0.71506  0.65819  0.57286  0.59969  0.70160]
 [ 0.76518  0.78569  0.74569  0.76924  0.78885  0.79487  0.74375  0.77812
   0.77335  0.76863  0.78510  0.73460  0.74960  0.78261]
 [ 0.53756  0.57724  0.36824  0.58368  0.52838  0.58115  0.48081  0.51386
   0.51614  0.54961  0.51674  0.42082  0.44953  0.54909]
 [ 0.58749  0.60596  0.53719  0.65947  0.59459  0.60794  0.52478  0.60657
   0.61281  0.57381  0.56500  0.50452  0.51996  0.57645]]
[ 0.84231  0.54138  0.66517  0.76895  0.51235  0.57690]<jupyter_text>PanNuke<jupyter_code>if __name__ == "__main__":
    true_dir = r'dataset\PanNuke\Fold 1\masks\fold1\consep_format'
    pred_dir = r"test_output\new_retrained_weights\pannuke_new\mat"
    
    mode = "instance";
    if mode == "instance":
        run_nuclei_inst_stat(pred_dir, true_dir, print_img_stats=False)
    if mode == "type":
        run_nuclei_type_stat(pred_dir, true_dir)<jupyter_output>test_output\new_retrained_weights\pannuke_new\mat
(6, 2545)
[[ 0.76683  0.84613  0.80593 ...  0.06265  0.00000  0.72194]
 [ 0.58158  0.67018  0.60852 ...  0.03234  0.00000  0.56487]
 [ 0.66667  0.66667  0.72222 ...  0.00000  0.00000  0.50000]
 [ 0.80574  0.82857  0.79410 ...  0.00000  0.00000  0.84433]
 [ 0.53716  0.55238  0.57352 ...  0.00000  0.00000  0.42216]
 [ 0.58158  0.69118  0.60852 ...  0.03234  0.00000  0.56487]]
[ 0.81678  0.65151  0.75846  0.80855  0.62407  0.66314]<jupyter_text>Combined Data<jupyter_code>if __name__ == "__main__":
    true_dir = r'dataset\training_data\combined\test\mat'
    pred_dir = r"test_output\new_retrained_weights\combined_test_new\mat"
    
    mode = "instance";
    if mode == "instance":
        run_nuclei_inst_stat(pred_dir, true_dir, print_img_stats=False)
    if mode == "type":
        run_nuclei_type_stat(pred_dir, true_dir)<jupyter_output>test_output\new_retrained_weights\combined_test_new\mat
(6, 2274)
[[0.92208241 0.86351246 0.90725511 ... 0.43432959 0.93043478 0.7772779 ]
 [0.84344583 0.65677419 0.78668765 ... 0.27740806 0.82042042 0.63569465]
 [0.88372093 0.60869565 0.80769231 ... 0.4        0.86363636 0.76923077]
 [0.87882373 0.8127107  0.85715272 ... 0.72794817 0.88104459 0.77783929]
 [0.77663492 0.49469347 0.69231566 ... 0.29117927 0.76090215 0.59833792]
 [0.84344583 0.65677419 0.79256067 ... 0.27740806 0.82494421 0.63569465]]
[ 0.80804  0.63882  0.74415  0.80355  0.61069  0.65023]
{'fraction_non_alphanumeric': 0.11139844837875472, 'fraction_numerical': 0.26894768251442214, 'mean_word_length': 3.12807881773399, 'pattern_counts': {'":': 15, '<': 16, '<?xml version=': 0, '>': 16, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '251550', 'license': 'MIT', 'n_tokens_mistral': 2997, 'n_tokens_neox': 2215, 'n_words': 488, 'path': 'ComputeStatsNB.ipynb', 'repo_name': 'Taslim-M/hover_net', 'size': '5027'}