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Metric-Confidence plots feature addition #2057

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merged 8 commits into from
Jan 28, 2021
Merged

Metric-Confidence plots feature addition #2057

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b5c2eed
Metric-Confidence plots feature addition
glenn-jocher Jan 27, 2021
3027908
cleanup
glenn-jocher Jan 27, 2021
0f2d9b2
Metric-Confidence plots feature addition
glenn-jocher Jan 27, 2021
dc515b2
cleanup
glenn-jocher Jan 27, 2021
92ab4ff
Merge remote-tracking branch 'origin/new_plots' into new_plots
glenn-jocher Jan 28, 2021
79a732b
Update run-once lines
glenn-jocher Jan 28, 2021
1a3a223
cleanup
glenn-jocher Jan 28, 2021
2ea582a
save all 4 curves to wandb
glenn-jocher Jan 28, 2021
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cleanup
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@glenn-jocher
glenn-jocher committed Jan 27, 2021
commit 3027908d0681e7a1244bdcd85e1e54b249cbcc3a
5 changes: 2 additions & 3 deletions utils/metrics.py
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Expand Up @@ -38,7 +38,6 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision

# Create Precision-Recall curve and compute AP for each class
px, py = np.linspace(0, 1, 1000), [] # for plotting
# pr_score = np.linspace(0, 1, 100) # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898
s = [unique_classes.shape[0], tp.shape[1]] # number class, number iou thresholds (i.e. 10 for mAP0.5...0.95)
ap, p, r = np.zeros(s), np.zeros(s + [1000]), np.zeros(s + [1000])
for ci, c in enumerate(unique_classes):
Expand Down Expand Up @@ -210,9 +209,9 @@ def plot_mc_curve(px, py, save_dir='.', names=(), xlabel='Confidence', ylabel='M

if 0 < len(names) < 21: # display per-class legend if < 21 classes
for i, y in enumerate(py.T):
ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(recall, precision)
ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric)
else:
ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision)
ax.plot(px, py, linewidth=1, color='grey') # plot(confidence, metric)

y = py.mean(1)
ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')
Expand Down