NPID¶
Unsupervised Feature Learning via Non-Parametric Instance Discrimination¶
Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so. We study whether this observation can be extended beyond the conventional domain of supervised learning: Can we learn a good feature representation that captures apparent similar- ity among instances, instead of classes, by merely asking the feature to be discriminative of individual instances?
We formulate this intuition as a non-parametric classification problem at the instance-level, and use noise-contrastive estimation to tackle the computational challenges imposed by the large number of instance classes. Our experimental results demonstrate that, under unsupervised learning settings, our method surpasses the state-of-the-art on ImageNet classification by a large margin.
Our method is also remarkable for consistently improving test performance with more training data and better network architectures. By fine-tuning the learned feature, we further obtain competitive results for semi-supervised learning and object detection tasks. Our non-parametric model is highly compact: With 128 features per image, our method requires only 600MB storage for a million images, enabling fast nearest neighbour retrieval at the run time.
Citation¶
@inproceedings{wu2018unsupervised,
title={Unsupervised feature learning via non-parametric instance discrimination},
author={Wu, Zhirong and Xiong, Yuanjun and Yu, Stella X and Lin, Dahua},
booktitle={CVPR},
year={2018}
}
Models and Benchmarks¶
Back to model_zoo.md
In this page, we provide benchmarks as much as possible to evaluate our pre-trained models. If not mentioned, all models were trained on ImageNet1k dataset.
VOC SVM / Low-shot SVM¶
The Best Layer indicates that the best results are obtained from which layers feature map. For example, if the Best Layer is feature3, its best result is obtained from the second stage of ResNet (1 for stem layer, 2-5 for 4 stage layers).
Besides, k=1 to 96 indicates the hyper-parameter of Low-shot SVM.
| Model | Config | Best Layer | SVM | k=1 | k=2 | k=4 | k=8 | k=16 | k=32 | k=64 | k=96 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| model | resnet50_8xb32-steplr-200e |
ImageNet Linear Evaluation¶
The Feature1 - Feature5 don’t have the GlobalAveragePooling, the feature map is pooled to the specific dimensions and then follows a Linear layer to do the classification. Please refer to resnet50_mhead_8xb32-steplr-90e.py for details of config.
The AvgPool result is obtained from Linear Evaluation with GlobalAveragePooling. Please refer to file name for details of config.
| Model | Config | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
|---|---|---|---|---|---|---|---|
| model | resnet50_8xb32-steplr-200e |
iNaturalist2018 Linear Evaluation¶
Please refer to resnet50_mhead_8xb32-steplr-84e_inat18.py and file name for details of config.
| Model | Config | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
|---|---|---|---|---|---|---|---|
| model | resnet50_8xb32-steplr-200e |
Places205 Linear Evaluation¶
Please refer to resnet50_mhead_8xb32-steplr-28e_places205.py and file name for details of config.
| Model | Config | Feature1 | Feature2 | Feature3 | Feature4 | Feature5 | AvgPool |
|---|---|---|---|---|---|---|---|
| model | resnet50_8xb32-steplr-200e |
Semi-Supervised Classification¶
In this benchmark, the necks or heads are removed and only the backbone CNN is evaluated by appending a linear classification head. All parameters are fine-tuned.
When training with 1% ImageNet, we find hyper-parameters especially the learning rate greatly influence the performance. Hence, we prepare a list of settings with the base learning rate from
{0.001, 0.01, 0.1}and the learning rate multiplier for the head from{1, 10, 100}. We choose the best performing setting for each method. The setting of parameters are indicated in the file name. The learning rate is indicated like1e-1,1e-2,1e-3and the learning rate multiplier is indicated likehead1,head10,head100.Please use –deterministic in this benchmark.
Please refer to the directories configs/benchmarks/classification/imagenet/imagenet_1percent/ of 1% data and configs/benchmarks/classification/imagenet/imagenet_10percent/ 10% data for details.
| Model | Pretrain Config | Fine-tuned Config | Top-1 (%) | Top-5 (%) |
|---|---|---|---|---|
| model | resnet50_8xb32-steplr-200e |
Detection¶
The detection benchmarks includes 2 downstream task datasets, Pascal VOC 2007 + 2012 and COCO2017. This benchmark follows the evluation protocols set up by MoCo.
Pascal VOC 2007 + 2012¶
Please refer to faster_rcnn_r50_c4_mstrain_24k.py for details of config.
| Model | Config | mAP | AP50 |
|---|---|---|---|
| model | resnet50_8xb32-steplr-200e |
COCO2017¶
Please refer to mask_rcnn_r50_fpn_mstrain_1x.py for details of config.
| Model | Config | mAP(Box) | AP50(Box) | AP75(Box) | mAP(Mask) | AP50(Mask) | AP75(Mask) |
|---|---|---|---|---|---|---|---|
| model | resnet50_8xb32-steplr-200e |
Segmentation¶
The segmentation benchmarks includes 2 downstream task datasets, Cityscapes and Pascal VOC 2012 + Aug. It follows the evluation protocols set up by MMSegmentation.
Pascal VOC 2012 + Aug¶
Please refer to file for details of config.
| Model | Config | mIOU |
|---|---|---|
| model | resnet50_8xb32-steplr-200e |
Cityscapes¶
Please refer to file for details of config.
| Model | Config | mIOU |
|---|---|---|
| model | resnet50_8xb32-steplr-200e |