about IOU

[sunxia233]

Thank you for your work. According to your suggestion, I trained after Convert polygons to parsing masks without modifying any code. This is my training log but IOU is not as high as in the paper. (py36) ➜ parsing python train_parsing.py --train-set trainval --masks-path ../outputs/veri776_parsing3165 --image-path /media/soar/data/176/VeRi/image_train

Epoch: 0 train: 100%|████| 396/396 [00:55<00:00, 7.67it/s, bce_dice_loss - 1.606, iou_score - 0.2795] valid: 100%|█████| 500/500 [00:09<00:00, 52.51it/s, bce_dice_loss - 1.519, iou_score - 0.434] Model saved!

Epoch: 1 train: 100%|████| 396/396 [00:56<00:00, 7.62it/s, bce_dice_loss - 1.581, iou_score - 0.3631] valid: 100%|████| 500/500 [00:09<00:00, 52.48it/s, bce_dice_loss - 1.502, iou_score - 0.5235] Model saved!

Epoch: 2 train: 100%|████| 396/396 [00:56<00:00, 7.61it/s, bce_dice_loss - 1.572, iou_score - 0.4105] valid: 100%|████| 500/500 [00:09<00:00, 52.79it/s, bce_dice_loss - 1.493, iou_score - 0.5486] Model saved!

Epoch: 3 train: 100%|████| 396/396 [00:56<00:00, 7.62it/s, bce_dice_loss - 1.564, iou_score - 0.4222] valid: 100%|█████| 500/500 [00:09<00:00, 52.67it/s, bce_dice_loss - 1.49, iou_score - 0.5551] Model saved!

Epoch: 4 train: 100%|████| 396/396 [00:56<00:00, 7.58it/s, bce_dice_loss - 1.563, iou_score - 0.4299] valid: 100%|████| 500/500 [00:09<00:00, 52.67it/s, bce_dice_loss - 1.485, iou_score - 0.5562] Model saved!

Epoch: 5 train: 100%|████| 396/396 [00:56<00:00, 7.61it/s, bce_dice_loss - 1.555, iou_score - 0.4416] valid: 100%|████| 500/500 [00:09<00:00, 52.69it/s, bce_dice_loss - 1.482, iou_score - 0.5473]

Epoch: 6 train: 100%|████| 396/396 [00:56<00:00, 7.62it/s, bce_dice_loss - 1.552, iou_score - 0.4419] valid: 100%|████| 500/500 [00:09<00:00, 52.81it/s, bce_dice_loss - 1.474, iou_score - 0.5468]

Epoch: 7 train: 100%|████| 396/396 [00:56<00:00, 7.60it/s, bce_dice_loss - 1.545, iou_score - 0.4489] valid: 100%|████| 500/500 [00:09<00:00, 52.79it/s, bce_dice_loss - 1.469, iou_score - 0.5649] Model saved!

Epoch: 8 train: 100%|████| 396/396 [00:56<00:00, 7.58it/s, bce_dice_loss - 1.544, iou_score - 0.4505] valid: 100%|████| 500/500 [00:09<00:00, 52.74it/s, bce_dice_loss - 1.467, iou_score - 0.5736] Model saved!

Epoch: 9 train: 100%|████| 396/396 [00:56<00:00, 7.62it/s, bce_dice_loss - 1.536, iou_score - 0.4599] valid: 100%|████| 500/500 [00:09<00:00, 52.66it/s, bce_dice_loss - 1.463, iou_score - 0.5841] Model saved!

Epoch: 10 train: 100%|████| 396/396 [00:56<00:00, 7.60it/s, bce_dice_loss - 1.534, iou_score - 0.4581] valid: 100%|████| 500/500 [00:09<00:00, 52.85it/s, bce_dice_loss - 1.459, iou_score - 0.5761]

Epoch: 11 train: 100%|████| 396/396 [00:56<00:00, 7.62it/s, bce_dice_loss - 1.531, iou_score - 0.4612] valid: 100%|████| 500/500 [00:09<00:00, 52.90it/s, bce_dice_loss - 1.455, iou_score - 0.5709]

Epoch: 12 train: 100%|████| 396/396 [00:56<00:00, 7.61it/s, bce_dice_loss - 1.525, iou_score - 0.4895] valid: 100%|█████| 500/500 [00:09<00:00, 52.63it/s, bce_dice_loss - 1.451, iou_score - 0.607] Model saved!

Epoch: 13 train: 100%|████| 396/396 [00:56<00:00, 7.60it/s, bce_dice_loss - 1.522, iou_score - 0.4973] valid: 100%|████| 500/500 [00:09<00:00, 52.69it/s, bce_dice_loss - 1.446, iou_score - 0.6365] Model saved!

Epoch: 14 train: 100%|█████| 396/396 [00:56<00:00, 7.61it/s, bce_dice_loss - 1.52, iou_score - 0.5051] valid: 100%|████| 500/500 [00:09<00:00, 52.71it/s, bce_dice_loss - 1.443, iou_score - 0.6174]

Epoch: 15 train: 100%|████| 396/396 [00:56<00:00, 7.58it/s, bce_dice_loss - 1.517, iou_score - 0.5044] valid: 100%|████| 500/500 [00:09<00:00, 52.60it/s, bce_dice_loss - 1.442, iou_score - 0.6192]

Epoch: 16 train: 100%|████| 396/396 [00:56<00:00, 7.59it/s, bce_dice_loss - 1.514, iou_score - 0.5098] valid: 100%|████| 500/500 [00:09<00:00, 52.46it/s, bce_dice_loss - 1.439, iou_score - 0.6245]

Epoch: 17 train: 100%|█████| 396/396 [00:56<00:00, 7.61it/s, bce_dice_loss - 1.513, iou_score - 0.508] valid: 100%|████| 500/500 [00:09<00:00, 52.99it/s, bce_dice_loss - 1.435, iou_score - 0.6242]

Epoch: 18 train: 100%|█████| 396/396 [00:56<00:00, 7.60it/s, bce_dice_loss - 1.511, iou_score - 0.507] valid: 100%|████| 500/500 [00:09<00:00, 52.75it/s, bce_dice_loss - 1.432, iou_score - 0.6201]

Epoch: 19 train: 100%|████| 396/396 [00:56<00:00, 7.60it/s, bce_dice_loss - 1.504, iou_score - 0.5156] valid: 100%|████| 500/500 [00:09<00:00, 52.69it/s, bce_dice_loss - 1.431, iou_score - 0.6176]

Epoch: 20 train: 100%|████| 396/396 [00:56<00:00, 7.60it/s, bce_dice_loss - 1.502, iou_score - 0.5199] valid: 100%|████| 500/500 [00:09<00:00, 52.57it/s, bce_dice_loss - 1.429, iou_score - 0.6245]

Epoch: 21 train: 100%|████| 396/396 [00:56<00:00, 7.61it/s, bce_dice_loss - 1.502, iou_score - 0.5173] valid: 100%|████| 500/500 [00:09<00:00, 52.23it/s, bce_dice_loss - 1.426, iou_score - 0.6284]

Epoch: 22 train: 100%|██████| 396/396 [00:56<00:00, 7.63it/s, bce_dice_loss - 1.5, iou_score - 0.5171] valid: 100%|████| 500/500 [00:09<00:00, 52.68it/s, bce_dice_loss - 1.423, iou_score - 0.6391] Model saved!

Epoch: 23 train: 100%|████| 396/396 [00:56<00:00, 7.57it/s, bce_dice_loss - 1.494, iou_score - 0.5248] valid: 100%|████| 500/500 [00:09<00:00, 52.82it/s, bce_dice_loss - 1.421, iou_score - 0.6225]

Epoch: 24 train: 100%|████| 396/396 [00:56<00:00, 7.57it/s, bce_dice_loss - 1.494, iou_score - 0.5213] valid: 100%|██████| 500/500 [00:09<00:00, 52.59it/s, bce_dice_loss - 1.42, iou_score - 0.626]

Epoch: 25 train: 100%|████| 396/396 [00:56<00:00, 7.61it/s, bce_dice_loss - 1.493, iou_score - 0.5225] valid: 100%|████| 500/500 [00:09<00:00, 52.70it/s, bce_dice_loss - 1.417, iou_score - 0.6361] Decrease decoder learning rate to 1e-5!

Epoch: 26 train: 100%|████| 396/396 [00:56<00:00, 7.63it/s, bce_dice_loss - 1.488, iou_score - 0.5318] valid: 100%|████| 500/500 [00:09<00:00, 52.55it/s, bce_dice_loss - 1.416, iou_score - 0.6592] Model saved!

Epoch: 27 train: 100%|█████| 396/396 [00:56<00:00, 7.62it/s, bce_dice_loss - 1.49, iou_score - 0.5278] valid: 100%|████| 500/500 [00:09<00:00, 52.98it/s, bce_dice_loss - 1.415, iou_score - 0.6351]

Epoch: 28 train: 100%|████| 396/396 [00:56<00:00, 7.58it/s, bce_dice_loss - 1.488, iou_score - 0.5313] valid: 100%|████| 500/500 [00:09<00:00, 52.81it/s, bce_dice_loss - 1.414, iou_score - 0.6348]

Epoch: 29 train: 100%|████| 396/396 [00:56<00:00, 7.62it/s, bce_dice_loss - 1.488, iou_score - 0.5315] valid: 100%|████| 500/500 [00:09<00:00, 52.74it/s, bce_dice_loss - 1.415, iou_score - 0.6337]

Epoch: 30 train: 100%|████| 396/396 [00:56<00:00, 7.59it/s, bce_dice_loss - 1.489, iou_score - 0.5301] valid: 100%|████| 500/500 [00:09<00:00, 52.78it/s, bce_dice_loss - 1.415, iou_score - 0.6661] Model saved!

Epoch: 31 train: 100%|████| 396/396 [00:56<00:00, 7.62it/s, bce_dice_loss - 1.487, iou_score - 0.5318] valid: 100%|████| 500/500 [00:09<00:00, 52.71it/s, bce_dice_loss - 1.413, iou_score - 0.6402]

Epoch: 32 train: 100%|████| 396/396 [00:56<00:00, 7.62it/s, bce_dice_loss - 1.486, iou_score - 0.5344] valid: 100%|█████| 500/500 [00:09<00:00, 52.78it/s, bce_dice_loss - 1.414, iou_score - 0.638]

Epoch: 33 train: 100%|████| 396/396 [00:56<00:00, 7.62it/s, bce_dice_loss - 1.489, iou_score - 0.5308] valid: 100%|███████| 500/500 [00:09<00:00, 52.93it/s, bce_dice_loss - 1.413, iou_score - 0.63

Epoch: 34 train: 100%|███████| 396/396 [00:56<00:00, 7.62it/s, bce_dice_loss - 1.486, iou_score - 0.53 valid: 100%|███████| 500/500 [00:09<00:00, 53.03it/s, bce_dice_loss - 1.413, iou_score - 0.64

Epoch: 35 train: 100%|███████| 396/396 [00:56<00:00, 7.61it/s, bce_dice_loss - 1.487, iou_score - 0.53 valid: 100%|█████████| 500/500 [00:09<00:00, 52.59it/s, bce_dice_loss - 1.414, iou_score - 0.

Epoch: 36 train: 100%|███████| 396/396 [00:56<00:00, 7.59it/s, bce_dice_loss - 1.486, iou_score - 0.53 valid: 100%|███████| 500/500 [00:09<00:00, 52.71it/s, bce_dice_loss - 1.413, iou_score - 0.63

Epoch: 37 train: 100%|████████| 396/396 [00:56<00:00, 7.58it/s, bce_dice_loss - 1.487, iou_score - 0.5 valid: 100%|███████| 500/500 [00:09<00:00, 52.64it/s, bce_dice_loss - 1.413, iou_score - 0.63

Epoch: 38 train: 100%|███████| 396/396 [00:56<00:00, 7.61it/s, bce_dice_loss - 1.487, iou_score - 0.53 valid: 100%|███████| 500/500 [00:09<00:00, 52.55it/s, bce_dice_loss - 1.412, iou_score - 0.63

Epoch: 39 train: 100%|███████| 396/396 [00:56<00:00, 7.59it/s, bce_dice_loss - 1.486, iou_score - 0.53 valid: 100%|███████| 500/500 [00:09<00:00, 52.57it/s, bce_dice_loss - 1.412, iou_score - 0.63 Do you have any suggestions？

[sunxia233]

I re-executed the experiment, iou can reach 0.83

[sunxia233]

I ran the experiment on veri-776 according to your settings, Accuracy obtained for the first time mAP: 77.4% [I 210121 15:20:54 main:473] CMC curve, Rank-1 :95.89% [I 210121 15:20:54 main:473] CMC curve, Rank-5 :98.09% [I 210121 15:20:54 main:473] CMC curve, Rank-10 :98.81% [I 210121 15:20:54 main:338] Saving models in epoch 120 1.086038589477539

Accuracy obtained the second time: mAP: 77.5% [I 210116 11:03:52 main:475] CMC curve, Rank-1 :95.65% [I 210116 11:03:52 main:475] CMC curve, Rank-5 :97.85% [I 210116 11:03:52 main:475] CMC curve, Rank-10 :98.99% [I 210116 11:03:52 main:337] Saving models in epoch 120 1.3036835193634033

Compared with the map in your paper, there is a drop of 2 points. What should I do to improve the accuracy, or is it because of the settings?

[silverbulletmdc]

I have fixed a bug recently. Please run git pull origin master to get the latest code and retrain it.