iter: 20, loss min|avg|max: 0.713|2.607|60.013, batch-p@3: 4.43%, ETA: 6:01:18 (0.87s/it)
iter: 40, loss min|avg|max: 0.696|1.204|21.239, batch-p@3: 5.99%, ETA: 5:58:53 (0.86s/it)
iter: 60, loss min|avg|max: 0.696|1.643|25.543, batch-p@3: 4.69%, ETA: 5:36:32 (0.81s/it)
iter: 80, loss min|avg|max: 0.695|1.679|42.339, batch-p@3: 7.03%, ETA: 5:58:01 (0.86s/it)
iter: 100, loss min|avg|max: 0.694|1.806|47.572, batch-p@3: 6.51%, ETA: 6:08:57 (0.89s/it)
iter: 120, loss min|avg|max: 0.695|1.200|21.791, batch-p@3: 4.43%, ETA: 6:14:15 (0.90s/it)
iter: 140, loss min|avg|max: 0.694|2.744|87.940, batch-p@3: 5.47%, ETA: 6:21:29 (0.92s/it)
iter: 20, loss min|avg|max: 0.741|14.827|440.151, batch-p@3: 1.04%, ETA: 6:23:26 (0.92s/it)
iter: 40, loss min|avg|max: 0.712|9.662|146.125, batch-p@3: 2.86%, ETA: 6:03:24 (0.87s/it)
iter: 60, loss min|avg|max: 0.697|3.944|100.707, batch-p@3: 4.17%, ETA: 6:10:44 (0.89s/it)
iter: 80, loss min|avg|max: 0.695|2.408|75.002, batch-p@3: 2.86%, ETA: 5:44:48 (0.83s/it)
iter: 100, loss min|avg|max: 0.694|2.272|67.504, batch-p@3: 2.86%, ETA: 6:03:45 (0.88s/it)
iter: 120, loss min|avg|max: 0.694|1.091|17.292, batch-p@3: 2.86%, ETA: 5:42:45 (0.83s/it)
iter: 140, loss min|avg|max: 0.693|1.069|15.975, batch-p@3: 5.73%, ETA: 5:46:48 (0.84s/it)
...
iter: 900, loss min|avg|max: 0.693|0.694| 0.709, batch-p@3: 2.08%, ETA: 5:15:00 (0.78s/it)
iter: 920, loss min|avg|max: 0.693|0.693| 0.701, batch-p@3: 2.34%, ETA: 5:39:12 (0.85s/it)
iter: 940, loss min|avg|max: 0.693|0.694| 0.704, batch-p@3: 5.99%, ETA: 5:46:12 (0.86s/it)
iter: 960, loss min|avg|max: 0.693|0.693| 0.705, batch-p@3: 2.86%, ETA: 5:24:59 (0.81s/it)
iter: 980, loss min|avg|max: 0.693|0.693| 0.700, batch-p@3: 3.65%, ETA: 5:39:47 (0.85s/it)
iter: 1000, loss min|avg|max: 0.693|0.693| 0.698, batch-p@3: 3.39%, ETA: 5:27:59 (0.82s/it)
iter: 1020, loss min|avg|max: 0.693|0.693| 0.700, batch-p@3: 6.51%, ETA: 5:36:38 (0.84s/it)
iter: 1040, loss min|avg|max: 0.693|0.694| 0.699, batch-p@3: 2.86%, ETA: 5:22:05 (0.81s/it)
...
iter: 1640, loss min|avg|max: 0.693|0.693| 0.694, batch-p@3: 2.60%, ETA: 5:09:58 (0.80s/it)
iter: 1660, loss min|avg|max: 0.693|0.693| 0.694, batch-p@3: 2.08%, ETA: 5:48:27 (0.90s/it)
iter: 1680, loss min|avg|max: 0.693|0.693| 0.694, batch-p@3: 4.43%, ETA: 5:23:23 (0.83s/it)
iter: 1700, loss min|avg|max: 0.693|0.693| 0.694, batch-p@3: 6.51%, ETA: 5:25:04 (0.84s/it)
iter: 1720, loss min|avg|max: 0.693|0.693| 0.694, batch-p@3: 3.12%, ETA: 5:39:08 (0.87s/it)
What does this affectively mean? "Nonzero triplets never decreases" - not quite sure what that means?
class_a/file.jpg
class_b/file.jpg
class_c/file.jpg
...
classes = [path for path in os.listdir(DATA_DIR) if os.path.isdir(os.path.join(DATA_DIR, path))]
for c in classes:
for file in glob.glob(DATA_DIR+c+"/*.jpg"):
pids.append(c)
fids.append(file)