tensorboard updates
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12
train.py
12
train.py
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@ -284,7 +284,7 @@ def train():
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plot_images(imgs=imgs, targets=targets, paths=paths, fname=f)
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plot_images(imgs=imgs, targets=targets, paths=paths, fname=f)
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if tb_writer:
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if tb_writer:
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tb_writer.add_image(f, cv2.imread(f)[:, :, ::-1], dataformats='HWC')
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tb_writer.add_image(f, cv2.imread(f)[:, :, ::-1], dataformats='HWC')
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# tb_writer.add_graph(model, imgs)
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# tb_writer.add_graph(model, imgs) # add model to tensorboard
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# end batch ------------------------------------------------------------------------------------------------
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# end batch ------------------------------------------------------------------------------------------------
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@ -313,11 +313,11 @@ def train():
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# Write Tensorboard results
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# Write Tensorboard results
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if tb_writer:
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if tb_writer:
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x = list(mloss) + list(results)
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tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss',
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titles = ['GIoU', 'Objectness', 'Classification', 'Train loss',
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'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/F1',
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'Precision', 'Recall', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification']
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'val/giou_loss', 'val/obj_loss', 'val/cls_loss']
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for xi, title in zip(x, titles):
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for x, tag in zip(list(mloss[:-1]) + list(results), tags):
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tb_writer.add_scalar(title, xi, epoch)
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tb_writer.add_scalar(tag, x, epoch)
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# Update best mAP
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# Update best mAP
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fi = fitness(np.array(results).reshape(1, -1)) # fitness_i = weighted combination of [P, R, mAP, F1]
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fi = fitness(np.array(results).reshape(1, -1)) # fitness_i = weighted combination of [P, R, mAP, F1]
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