Model Zoo¶
Results are presented in the format of <Rank-1 (mAP)>.
When computing model size and FLOPs, only layers that are used at test time are considered (see
torchreid.utils.compute_model_complexity).Asterisk (*) means the model is trained from scratch.
combineall=Truemeans all images in the dataset are used for model training.Why not use heavy data augmentation like random erasing for model training? It’s because heavy data augmentation might harm the cross-dataset generalization performance (see this paper).
ImageNet pretrained models¶
| Model | Download |
|---|---|
| shufflenet | model |
| mobilenetv2_x1_0 | model |
| mobilenetv2_x1_4 | model |
| mlfn | model |
| osnet_x1_0 | model |
| osnet_x0_75 | model |
| osnet_x0_5 | model |
| osnet_x0_25 | model |
| osnet_ibn_x1_0 | model |
| osnet_ain_x1_0 | model |
| osnet_ain_x0_75 | model |
| osnet_ain_x0_5 | model |
| osnet_ain_x0_25 | model |
Same-domain ReID¶
| Model | # Param (10^6) | GFLOPs | Loss | Input | Transforms | Distance | market1501 | dukemtmcreid | msmt17 |
|---|---|---|---|---|---|---|---|---|---|
| resnet50 | 23.5 | 2.7 | softmax | (256, 128) | random_flip, random_crop |
euclidean |
87.9 (70.4) | 78.3 (58.9) | 63.2 (33.9) |
| resnet50_fc512 | 24.6 | 4.1 | softmax | (256, 128) | random_flip, random_crop |
euclidean |
90.8 (75.3) | 81.0 (64.0) | 69.6 (38.4) |
| mlfn | 32.5 | 2.8 | softmax | (256, 128) | random_flip, random_crop |
euclidean |
90.1 (74.3) | 81.1 (63.2) | 66.4 (37.2) |
| hacnn* | 4.5 | 0.5 | softmax | (160, 64) | random_flip, random_crop |
euclidean |
90.9 (75.6) | 80.1 (63.2) | 64.7 (37.2) |
| mobilenetv2_x1_0 | 2.2 | 0.2 | softmax | (256, 128) | random_flip, random_crop |
euclidean |
85.6 (67.3) | 74.2 (54.7) | 57.4 (29.3) |
| mobilenetv2_x1_4 | 4.3 | 0.4 | softmax | (256, 128) | random_flip, random_crop |
euclidean |
87.0 (68.5) | 76.2 (55.8) | 60.1 (31.5) |
| osnet_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | random_flip |
euclidean |
94.2 (82.6) | 87.0 (70.2) | 74.9 (43.8) |
| osnet_x0_75 | 1.3 | 0.57 | softmax | (256, 128) | random_flip |
euclidean |
93.7 (81.2) | 85.8 (69.8) | 72.8 (41.4) |
| osnet_x0_5 | 0.6 | 0.27 | softmax | (256, 128) | random_flip |
euclidean |
92.5 (79.8) | 85.1 (67.4) | 69.7 (37.5) |
| osnet_x0_25 | 0.2 | 0.08 | softmax | (256, 128) | random_flip |
euclidean |
91.2 (75.0) | 82.0 (61.4) | 61.4 (29.5) |
Cross-domain ReID¶
Market1501 -> DukeMTMC-reID¶
| Model | # Param (10^6) | GFLOPs | Loss | Input | Transforms | Distance | Rank-1 | Rank-5 | Rank-10 | mAP | Download |
|---|---|---|---|---|---|---|---|---|---|---|---|
| osnet_ibn_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | random_flip, color_jitter |
euclidean |
48.5 | 62.3 | 67.4 | 26.7 | model |
| osnet_ain_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | random_flip, color_jitter |
cosine |
52.4 | 66.1 | 71.2 | 30.5 | model |
DukeMTMC-reID -> Market1501¶
| Model | # Param (10^6) | GFLOPs | Loss | Input | Transforms | Distance | Rank-1 | Rank-5 | Rank-10 | mAP | Download |
|---|---|---|---|---|---|---|---|---|---|---|---|
| osnet_ibn_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | random_flip, color_jitter |
euclidean |
57.7 | 73.7 | 80.0 | 26.1 | model |
| osnet_ain_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | random_flip, color_jitter |
cosine |
61.0 | 77.0 | 82.5 | 30.6 | model |
MSMT17 (combineall=True) -> Market1501 & DukeMTMC-reID¶
| Model | # Param (10^6) | GFLOPs | Loss | Input | Transforms | Distance | msmt17 -> market1501 | msmt17 -> dukemtmcreid | Download |
|---|---|---|---|---|---|---|---|---|---|
| resnet50 | 23.5 | 2.7 | softmax | (256, 128) | random_flip, color_jitter |
euclidean |
46.3 (22.8) | 52.3 (32.1) | model |
| osnet_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | random_flip, color_jitter |
euclidean |
66.6 (37.5) | 66.0 (45.3) | model |
| osnet_x0_75 | 1.3 | 0.57 | softmax | (256, 128) | random_flip, color_jitter |
euclidean |
63.6 (35.5) | 65.3 (44.5) | model |
| osnet_x0_5 | 0.6 | 0.27 | softmax | (256, 128) | random_flip, color_jitter |
euclidean |
64.3 (34.9) | 65.2 (43.3) | model |
| osnet_x0_25 | 0.2 | 0.08 | softmax | (256, 128) | random_flip, color_jitter |
euclidean |
59.9 (31.0) | 61.5 (39.6) | model |
| osnet_ibn_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | random_flip, color_jitter |
euclidean |
66.5 (37.2) | 67.4 (45.6) | model |
| osnet_ain_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | random_flip, color_jitter |
cosine |
70.1 (43.3) | 71.1 (52.7) | model |
Multi-source domain generalization¶
The models below are trained using multiple source datasets, as described in Zhou et al. TPAMI’21.
Regarding the abbreviations, MS is MSMT17; M is Market1501; D is DukeMTMC-reID; and C is CUHK03.
All models were trained with im_osnet_ain_x1_0_softmax_256x128_amsgrad_cosine.yaml and max_epoch=50.
| Model | # Param (10^6) | GFLOPs | Loss | Input | Transforms | Distance | MS+D+C->M | MS+M+C->D | MS+D+M->C | D+M+C->MS |
|---|---|---|---|---|---|---|---|---|---|---|
| osnet_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | random_flip, color_jitter |
cosine |
72.5 (44.2) | 65.2 (47.0) | 23.9 (23.3) | 33.2 (12.6) |
| osnet_ibn_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | random_flip, color_jitter |
cosine |
73.0 (44.9) | 64.6 (45.7) | 25.7 (25.4) | 39.8 (16.2) |
| osnet_ain_x1_0 | 2.2 | 0.98 | softmax | (256, 128) | random_flip, color_jitter |
cosine |
73.3 (45.8) | 65.6 (47.2) | 27.4 (27.1) | 40.2 (16.2) |