| model | mAP | eval method | Training set | PreTrain set | Training Log | Eval Log | Base Module | config |
|---|---|---|---|---|---|---|---|---|
| 【ALL】= resnet101 (multiscale) + resnet101 ratio1:4,4:1 (multiscale) + resnet101 scale4 (multiscale) + resnet152 (multiscale) + inceptionv3 (multiscale) + rcnn_dcn (multiscale) | 0.5295 | nms+box voting | Imagenet all | - | - | - | - | NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1 |
| 【ENS1】=【ALL】 - dcn_rcnn (scale1000, mAP=0.4231) | 0.5301 | nms+box voting | Imagenet all | - | - | - | - | NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1 |
| 【ENS2】=【ENS1】 - dcn_rcnn (scale400, mAP=0.4249) | 0.5297 | nms+box voting | Imagenet all | - | - | - | - | NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1 |
| 【ENS3】=【ENS2】 - inceptionv3 (scale400, mAP=0.4257) | 0.5289 | nms+box voting | Imagenet all | - | - | - | - | NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1 |
| 【ENS4】=【ENS3】 - resnet152 (scale400, mAP=0.4318) - resnet101_ratio4 (scale400, mAP=0.4350) - resnet101_scale4 (scale400, mAP=0.4375) | 0.5289 | nms+box voting | Imagenet all | - | - | - | - | NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1 |
| 【ENS5】=【ALL】 - resnet152 (scale400, mAP=0.4318) - resnet101_ratio4 (scale400, mAP=0.4350) - resnet101_scale4 (scale400, mAP=0.4375) | 0.5298 | nms+box voting | Imagenet all | - | - | - | - | NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1 |
| 【ENS6】=【ENS1】 + dcn_rfcn (scale600, mAP=0.4695) | 0.5305 | nms+box voting | Imagenet all | - | - | - | - | NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1 |
| 【ENS7】=【ENS6】 - resnet101 (scale400, mAP=0.4456) | 0.5295 | nms+box voting | Imagenet all | - | - | - | - | NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1 |
| 【ENS8】=【ENS7】 - resnet101 (scale1000, mAP=0.4532) | 0.5301 | nms+box voting | Imagenet all | - | - | - | - | NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1 |
| 【ENS9】=【ENS8】 - resnet101_scale4 (scale1000, mAP=0.4555) | 0.5296 | nms+box voting | Imagenet all | - | - | - | - | NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1 |
| 【ENS10】=【ENS9】 - dcn_rfcn (scale800, mAP=0.4597) | 0.5292 | nms+box voting | Imagenet all | - | - | - | - | NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1 |
| resnet101(multiscale) + resnet152 + inceptionv3 + rcnn_dcn | 0.5238 | nms+box voting | Imagenet all | - | - | - | - | NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1 |
| resnet101(multiscale) +resnet152 +inceptionv3 | 0.5222 | nms+box voting | Imagenet all | - | - | - | - | NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1 |
| resnet101(multiscale) +deepmask +resnet152 +inceptionv3 | 0.5213 | nms+box voting | Imagenet all | - | - | - | - | NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1 |
| resnet101(multiscale) +deepmask +resnet152 | 0.5141 | nms+box voting | Imagenet all | - | - | - | - | NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1 |
| resnet101(multiscale) +deepmask | 0.5090 | nms+box voting | Imagenet all | - | - | - | - | NMS＝0.5 IoU_Thresh=0.5 score_Thresh=0.1 |
| Resnet-101 std | 0.4874 | test.py | ImageNet all | None | Train | Test | Resnet-101 param Resnet 101 modeljson | config |
| ResNet-101 smalldb | 0.3958 | test.py | ImageNet train_0 | None | Train | Eval | Resnet-101 param Resnet 101 modeljson | config |
- 7 * BN-Inception (32 Layers)
- 2 * MSRA-Net (22 Layers)
- ResNet, Identity Map
- random crop
- multi-scale
- contrast jittering
- color jittering
- Pretrain on LOC !!
- Objectness loss
- Negative categories
- BBox Voting
- Balanced Sampling
- Multi-Scale Training
- Online Hard Sample Mining
- Cascade RPN
- Constrained Neg/Pos Anchor Ratio
- Pretrained Global Context
- Multi-Scale Testing
- HFlip
- Box Votinng
Tricks的实现划分在以下5个文件夹中：
- dataprocess
- regionproposal
- fastrcnn
- postprocess
- ensumble
上述代码尝试做成平台无关，与计算框架相关的代码都在PlatformRelated文件夹中