Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-11-07 20:01:47 -08:00
parent 2efe423b34
commit d67b1cb1ad
2 changed files with 3 additions and 3 deletions
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2
test.py
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@ -56,7 +56,7 @@ def test(cfg,
seen = 0 seen = 0
model.eval() model.eval()
coco91class = coco80_to_coco91_class() coco91class = coco80_to_coco91_class()
s = ('%20s' + '%10s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP', 'F1') s = ('%20s' + '%10s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@0.5', 'F1')
p, r, f1, mp, mr, map, mf1 = 0., 0., 0., 0., 0., 0., 0. p, r, f1, mp, mr, map, mf1 = 0., 0., 0., 0., 0., 0., 0.
loss = torch.zeros(3) loss = torch.zeros(3)
jdict, stats, ap, ap_class = [], [], [], [] jdict, stats, ap, ap_class = [], [], [], []

4
utils/utils.py
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@ -881,7 +881,7 @@ def plot_results(start=0, stop=0): # from utils.utils import *; plot_results()
fig, ax = plt.subplots(2, 5, figsize=(14, 7)) fig, ax = plt.subplots(2, 5, figsize=(14, 7))
ax = ax.ravel() ax = ax.ravel()
s = ['GIoU', 'Objectness', 'Classification', 'Precision', 'Recall', s = ['GIoU', 'Objectness', 'Classification', 'Precision', 'Recall',
'val GIoU', 'val Objectness', 'val Classification', 'mAP', 'F1'] 'val GIoU', 'val Objectness', 'val Classification', 'mAP@0.5', 'F1']
for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')): for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
n = results.shape[1] # number of rows n = results.shape[1] # number of rows
@ -902,7 +902,7 @@ def plot_results(start=0, stop=0): # from utils.utils import *; plot_results()
def plot_results_overlay(start=0, stop=0): # from utils.utils import *; plot_results_overlay() def plot_results_overlay(start=0, stop=0): # from utils.utils import *; plot_results_overlay()
# Plot training results files 'results*.txt', overlaying train and val losses # Plot training results files 'results*.txt', overlaying train and val losses
s = ['train', 'train', 'train', 'Precision', 'mAP', 'val', 'val', 'val', 'Recall', 'F1'] # legends s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'F1'] # legends
t = ['GIoU', 'Objectness', 'Classification', 'P-R', 'mAP-F1'] # titles t = ['GIoU', 'Objectness', 'Classification', 'P-R', 'mAP-F1'] # titles
for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')): for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T