Original repo here
However, the original repo uses ignite for training and saving the model, which is incompatible with model using SyncBN, So I reimplement the baseline without the use of ignite.(???)
Some code in this repo is borrowed from the original one.
- Clone the repo using
git clone - Compile the code for Cython accelerated evaluation code
cd evaluate/eval_cylib && make - the SyncBN module is pure pytorch implementation, so no need to compile once you have pytorch.
- Modify the training config in configs folder.
- Start training:
# training with only one GPU
CUDA_VISIBLE_DEVICES=1 python main.py -c configs/debug.yml
# testing with one GPU
CUDA_VISIBLE_DEVICES=1 python main.py -t -c configs/debug.yml TEST.WEIGHT /path/to/saved/weights
# training with multi-GPU
CUDA_VISIBLE_DEVICES=1,2 python main.py -c configs/debug_multi-gpu.yml
# testing with multi-GPU
CUDA_VISIBLE_DEVICES=1,2 python main.py -t -c configs/debug_multi-gpu.yml TEST.WEIGHT /path/to/saved/weights
# fp16 training
CUDA_VISIBLE_DEVICES=1 python main.py -c configs/debug.yml SOLVER.FP16 TrueI only trained the model with 30 epoch, so the model may not be fully converged.
Note that the Resnet50 model is trained with Warmup, Random erasing augmentation, Last stride=1 and BNNeck.
2019-07-06 13:43:29,884 reid_baseline.eval INFO: Validation Result:
2019-07-06 13:43:29,885 reid_baseline.eval INFO: CMC Rank-1: 89.55%
2019-07-06 13:43:29,885 reid_baseline.eval INFO: CMC Rank-5: 96.76%
2019-07-06 13:43:29,885 reid_baseline.eval INFO: CMC Rank-10: 98.28%
2019-07-06 13:43:29,885 reid_baseline.eval INFO: mAP: 74.50%
2019-07-06 13:43:29,885 reid_baseline.eval INFO: --------------------
2019-07-06 13:45:40,783 reid_baseline.eval INFO: ReRanking Result:
2019-07-06 13:45:40,783 reid_baseline.eval INFO: CMC Rank-1: 92.67%
2019-07-06 13:45:40,783 reid_baseline.eval INFO: CMC Rank-5: 96.41%
2019-07-06 13:45:40,784 reid_baseline.eval INFO: CMC Rank-10: 97.48%
2019-07-06 13:45:40,784 reid_baseline.eval INFO: mAP: 90.00%
2019-07-06 13:45:40,784 reid_baseline.eval INFO: --------------------2019-07-06 14:24:32,171 reid_baseline.eval INFO: Use multi gpu to inference
2019-07-06 14:25:41,053 reid_baseline.eval INFO: Validation Result:
2019-07-06 14:25:41,054 reid_baseline.eval INFO: CMC Rank-1: 87.44%
2019-07-06 14:25:41,054 reid_baseline.eval INFO: CMC Rank-5: 96.17%
2019-07-06 14:25:41,054 reid_baseline.eval INFO: CMC Rank-10: 97.54%
2019-07-06 14:25:41,054 reid_baseline.eval INFO: mAP: 72.13%
2019-07-06 14:25:41,054 reid_baseline.eval INFO: --------------------
2019-07-06 14:27:23,449 reid_baseline.eval INFO: ReRanking Result:
2019-07-06 14:27:23,450 reid_baseline.eval INFO: CMC Rank-1: 91.30%
2019-07-06 14:27:23,450 reid_baseline.eval INFO: CMC Rank-5: 95.64%
2019-07-06 14:27:23,450 reid_baseline.eval INFO: CMC Rank-10: 96.79%
2019-07-06 14:27:23,450 reid_baseline.eval INFO: mAP: 88.92%
2019-07-06 14:27:23,450 reid_baseline.eval INFO: --------------------2019-07-07 12:29:20,649 reid_baseline.eval INFO: Validation Result:
2019-07-07 12:29:20,650 reid_baseline.eval INFO: CMC Rank-1: 90.44%
2019-07-07 12:29:20,650 reid_baseline.eval INFO: CMC Rank-5: 96.47%
2019-07-07 12:29:20,650 reid_baseline.eval INFO: CMC Rank-10: 98.13%
2019-07-07 12:29:20,650 reid_baseline.eval INFO: mAP: 75.88%
2019-07-07 12:29:20,650 reid_baseline.eval INFO: --------------------
2019-07-07 12:31:01,269 reid_baseline.eval INFO: ReRanking Result:
2019-07-07 12:31:01,270 reid_baseline.eval INFO: CMC Rank-1: 93.14%
2019-07-07 12:31:01,270 reid_baseline.eval INFO: CMC Rank-5: 96.26%
2019-07-07 12:31:01,270 reid_baseline.eval INFO: CMC Rank-10: 97.12%
2019-07-07 12:31:01,270 reid_baseline.eval INFO: mAP: 90.22%
2019-07-07 12:31:01,270 reid_baseline.eval INFO: --------------------2019-07-08 18:15:14,299 reid_baseline.eval INFO: Validation Result:
2019-07-08 18:15:14,299 reid_baseline.eval INFO: CMC Rank-1: 45.38%
2019-07-08 18:15:14,300 reid_baseline.eval INFO: CMC Rank-3: 57.14%
2019-07-08 18:15:14,300 reid_baseline.eval INFO: mAP: 55.76%
2019-07-08 18:15:14,300 reid_baseline.eval INFO: --------------------
2019-07-08 18:15:14,678 reid_baseline.eval INFO: ReRanking Result:
2019-07-08 18:15:14,678 reid_baseline.eval INFO: CMC Rank-1: 28.57%
2019-07-08 18:15:14,678 reid_baseline.eval INFO: CMC Rank-3: 48.74%
2019-07-08 18:15:14,678 reid_baseline.eval INFO: mAP: 42.62%
2019-07-08 18:15:14,679 reid_baseline.eval INFO: --------------------Result on VeRI-WILD small test set
2019-07-12 22:47:57,250 reid_baseline.eval INFO: Validation Result:
2019-07-12 22:47:57,250 reid_baseline.eval INFO: CMC Rank-1: 90.43%
2019-07-12 22:47:57,250 reid_baseline.eval INFO: CMC Rank-5: 96.85%
2019-07-12 22:47:57,250 reid_baseline.eval INFO: CMC Rank-10: 98.19%
2019-07-12 22:47:57,250 reid_baseline.eval INFO: mAP: 74.27%
2019-07-12 22:47:57,251 reid_baseline.eval INFO: --------------------
2019-07-12 22:57:10,006 reid_baseline.eval INFO: ReRanking Result:
2019-07-12 22:57:10,006 reid_baseline.eval INFO: CMC Rank-1: 89.66%
2019-07-12 22:57:10,006 reid_baseline.eval INFO: CMC Rank-5: 95.45%
2019-07-12 22:57:10,006 reid_baseline.eval INFO: CMC Rank-10: 97.46%
2019-07-12 22:57:10,007 reid_baseline.eval INFO: mAP: 77.42%
2019-07-12 22:57:10,007 reid_baseline.eval INFO: --------------------Result on VeRI776
2019-07-13 11:35:12,402 reid_baseline.eval INFO: Validation Result:
2019-07-13 11:35:12,402 reid_baseline.eval INFO: CMC Rank-1: 95.65%
2019-07-13 11:35:12,402 reid_baseline.eval INFO: CMC Rank-5: 97.91%
2019-07-13 11:35:12,403 reid_baseline.eval INFO: CMC Rank-10: 99.11%
2019-07-13 11:35:12,403 reid_baseline.eval INFO: mAP: 77.15%
2019-07-13 11:35:12,403 reid_baseline.eval INFO: --------------------
2019-07-13 11:36:02,050 reid_baseline.eval INFO: ReRanking Result:
2019-07-13 11:36:02,050 reid_baseline.eval INFO: CMC Rank-1: 97.20%
2019-07-13 11:36:02,050 reid_baseline.eval INFO: CMC Rank-5: 97.85%
2019-07-13 11:36:02,050 reid_baseline.eval INFO: CMC Rank-10: 98.75%
2019-07-13 11:36:02,050 reid_baseline.eval INFO: mAP: 81.74%
2019-07-13 11:36:02,050 reid_baseline.eval INFO: --------------------