From 7d7d7a6332e62c5639f58818729fb881a326ad82 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Fri, 21 Jun 2019 10:17:29 +0200 Subject: [PATCH] updates --- train.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/train.py b/train.py index 161f902c..c0efe1c6 100644 --- a/train.py +++ b/train.py @@ -208,7 +208,7 @@ def train( # Multi-Scale training if multi_scale: - if (i + 1 + nb * epoch) % 10 == 0: #  adjust (67% - 150%) every 10 batches + if ((i + 1) / accumulate + nb * epoch) % 10 == 0: #  adjust (67% - 150%) every 10 batches img_size = random.choice(range(img_size_min, img_size_max + 1)) * 32 print('img_size = %g' % img_size) scale_factor = img_size / max(imgs.shape[-2:]) @@ -360,7 +360,7 @@ if __name__ == '__main__': # Mutate hyperparameters old_hyp = hyp.copy() init_seeds(seed=int(time.time())) - s = [.5, .5, .5, .5, .5, .5, .5, .5, .5, .05, .5] # xy, wh, cls, conf, iou_t, lr0, lrf, momentum, weight_decay + s = [.4, .4, .4, .4, .4, .4, .4, .4, .4, .04, .4] # fractional sigmas for i, k in enumerate(hyp.keys()): x = (np.random.randn(1) * s[i] + 1) ** 1.1 # plt.hist(x.ravel(), 100) hyp[k] = hyp[k] * float(x) # vary by about 30% 1sigma