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License: Other
Implementation of a classification framework from the paper Aggregated Residual Transformations for Deep Neural Networks
License: Other
Is there any chance we can get your 5k way pretrained models? I suspect these will work much better for pretraining.
Seems the Caffe third party repository no longer exists. Is there an alternative? I found https://github.com/firekong0909/ResNeXt but the download links appear to be broken.
Hi, where can we find the training scripts of ssd-resnet50, ssd-resnet-152 in caffe. I need that .
Kindly reply for this issue.
Thank yoyu
Dear author:
very great work for deep learning, yet I want to konw how can I cite your work as bibtex? the given cite seems not complete, could you please update the cite content?
thanks a lot.
@article{Xie2016, title={Aggregated Residual Transformations for Deep Neural Networks}, author={Saining Xie and Ross Girshick and Piotr Dollár and Zhuowen Tu and Kaiming He}, journal={arXiv preprint arXiv:1611.05431}, year={2016} }
What lr should I choose for imagenet training if I use 4 GPUs instead of 4 with batch size of 32 per gpu?
Hi, I have a question about license of Imagenet pre-trained model.
I want to do fine-tuning using Imagenet pre-trained model on my own dataset.
In this case, which license should I follow? Is it non-commercial?
Recently I am trying to reproduce your result in torch. And my command is
th main.lua -dataset cifar10 -bottleneckType resnext_C -depth 29 -baseWidth 64 -cardinality 16 -weightDecay 5e-6 -batchSize 32 -nGPU 2 -LR 0.025 -nThreads 8 -shareGradInput true | tee -a ./cifar10_2gpu_torch.log
I copied the command in README.md(CIFAR10 and 2GPUs).
The problem starts from #71 epoch.
Here is my log file(For readability I pick out part of them):
* Finished epoch # 60 top1: 6.610 top5: 0.140
* Finished epoch # 61 top1: 7.050 top5: 0.150
* Finished epoch # 62 top1: 7.680 top5: 0.240
* Finished epoch # 63 top1: 7.180 top5: 0.230
* Finished epoch # 64 top1: 7.100 top5: 0.220
* Finished epoch # 65 top1: 6.980 top5: 0.160
* Finished epoch # 66 top1: 6.850 top5: 0.170
* Finished epoch # 67 top1: 6.870 top5: 0.180
* Finished epoch # 68 top1: 7.010 top5: 0.270
* Finished epoch # 69 top1: 6.910 top5: 0.220
* Finished epoch # 70 top1: 6.290 top5: 0.130
* Finished epoch # 71 top1: 85.740 top5: 34.780
* Finished epoch # 72 top1: 81.790 top5: 33.700
* Finished epoch # 73 top1: 80.220 top5: 28.920
* Finished epoch # 74 top1: 79.200 top5: 31.640
* Finished epoch # 75 top1: 78.980 top5: 27.150
* Finished epoch # 76 top1: 79.540 top5: 30.260
* Finished epoch # 77 top1: 81.540 top5: 29.620
And the epoch output for a single batch:
| Epoch: [78][1158/1563] Time 1.024 Data 0.000 Err 1528913280.0000 top1 81.250 top5 28.125
| Epoch: [78][1159/1563] Time 0.881 Data 0.000 Err 1559899264.0000 top1 81.250 top5 15.625
| Epoch: [78][1160/1563] Time 0.975 Data 0.000 Err 8231911424.0000 top1 87.500 top5 40.625
| Epoch: [78][1161/1563] Time 0.928 Data 0.000 Err 554394944.0000 top1 78.125 top5 28.125
| Epoch: [78][1162/1563] Time 1.012 Data 0.000 Err 4567331328.0000 top1 93.750 top5 40.625
| Epoch: [78][1163/1563] Time 1.146 Data 0.000 Err 2310403584.0000 top1 78.125 top5 34.375
| Epoch: [78][1164/1563] Time 0.947 Data 0.000 Err 2803231744.0000 top1 81.250 top5 25.000
| Epoch: [78][1165/1563] Time 0.956 Data 0.000 Err 2265360896.0000 top1 87.500 top5 50.000
| Epoch: [78][1166/1563] Time 0.867 Data 0.000 Err 1953190016.0000 top1 84.375 top5 21.875
| Epoch: [78][1167/1563] Time 1.014 Data 0.000 Err 2912053760.0000 top1 93.750 top5 28.125
| Epoch: [78][1168/1563] Time 1.007 Data 0.000 Err 4222694656.0000 top1 84.375 top5 31.250
| Epoch: [78][1169/1563] Time 0.895 Data 0.000 Err 5509958144.0000 top1 81.250 top5 37.500
| Epoch: [78][1170/1563] Time 0.979 Data 0.000 Err 5301891584.0000 top1 84.375 top5 34.375
| Epoch: [78][1171/1563] Time 0.920 Data 0.000 Err 3593149184.0000 top1 87.500 top5 28.125
| Epoch: [78][1172/1563] Time 1.020 Data 0.000 Err 7279746560.0000 top1 90.625 top5 31.250
| Epoch: [78][1173/1563] Time 1.002 Data 0.000 Err 10108009472.0000 top1 87.500 top5 31.250
| Epoch: [78][1174/1563] Time 0.861 Data 0.001 Err 2861270528.0000 top1 87.500 top5 28.125
| Epoch: [78][1175/1563] Time 0.862 Data 0.000 Err 4651573760.0000 top1 87.500 top5 31.250
| Epoch: [78][1176/1563] Time 1.051 Data 0.000 Err 92108896.0000 top1 75.000 top5 31.250
| Epoch: [78][1177/1563] Time 1.024 Data 0.000 Err 2649925888.0000 top1 87.500 top5 43.750
| Epoch: [78][1178/1563] Time 0.967 Data 0.000 Err 2876758784.0000 top1 71.875 top5 18.750
| Epoch: [78][1179/1563] Time 0.942 Data 0.000 Err 2976156928.0000 top1 71.875 top5 15.625
| Epoch: [78][1180/1563] Time 0.882 Data 0.000 Err 838116416.0000 top1 78.125 top5 43.750
| Epoch: [78][1181/1563] Time 1.028 Data 0.000 Err 6477106688.0000 top1 78.125 top5 37.500
| Epoch: [78][1182/1563] Time 1.004 Data 0.000 Err 5051654144.0000 top1 84.375 top5 31.250
| Epoch: [78][1183/1563] Time 0.859 Data 0.000 Err 5013932544.0000 top1 87.500 top5 34.375
| Epoch: [78][1184/1563] Time 0.848 Data 0.001 Err 2034009088.0000 top1 93.750 top5 25.000
| Epoch: [78][1185/1563] Time 1.060 Data 0.000 Err 3669680640.0000 top1 78.125 top5 25.000
| Epoch: [78][1186/1563] Time 1.028 Data 0.000 Err 4146675200.0000 top1 93.750 top5 28.125
| Epoch: [78][1187/1563] Time 0.966 Data 0.000 Err 2259935488.0000 top1 84.375 top5 34.375
| Epoch: [78][1188/1563] Time 0.956 Data 0.000 Err 1698448512.0000 top1 75.000 top5 25.000
| Epoch: [78][1189/1563] Time 0.864 Data 0.000 Err 4151320064.0000 top1 90.625 top5 56.250
| Epoch: [78][1190/1563] Time 1.035 Data 0.000 Err 1942320000.0000 top1 87.500 top5 31.250
| Epoch: [78][1191/1563] Time 1.026 Data 0.000 Err 1455451520.0000 top1 81.250 top5 31.250
| Epoch: [78][1192/1563] Time 0.867 Data 0.000 Err 2734585856.0000 top1 90.625 top5 40.625
| Epoch: [78][1193/1563] Time 0.965 Data 0.000 Err 36324916.0000 top1 81.250 top5 18.750
| Epoch: [78][1194/1563] Time 0.913 Data 0.000 Err 6873055744.0000 top1 90.625 top5 50.000
| Epoch: [78][1195/1563] Time 1.004 Data 0.000 Err 1242362112.0000 top1 84.375 top5 31.250
So anyone has the same problem or how to solve it? I have run this for nearly two days, but it really disappointed me.
Thanks a lot.
Hi,can u provide the pretrained model of ResNeXt.The model link provided seems broken!
Hi,
I'm trying to compute GFLOPs by using this code: https://github.com/apaszke/torch-opCounter
The input data is [1, 3, 224, 224]
, and the computed GFLOPs is about 22.84 GFLOPs for ResNeXt-50, 32x4d, which is different with the reported 4.1 GFLOPs.
So is there anything wrong with my approach? How do you compute GFLOPs?
May I ask why ResNeXt don't use Pre activation as mentioned in Identity Mappings in Deep Residual Networks. I didn't see the reason in Aggregated Residual Transformations for Deep Neural Networks.
I trained ResNeXt-64x8 and ResNeXt-64x16 on Cifar-10. But the result is slight worse than the results released in the paper. I trained these models by three gpus:
th main.lua -dataset cifar10 -bottleneckType resnext_C -depth 29 -baseWidth 64 -cardinality 16/8 -weightDecay 5e-4 -batchSize 128 -nGPU 3 -nThreads 8 -shareGradInput true
the best model results I got for each run is:
ResNeXt-64x8: 3.68%, 3.8%, 3.86% 3.90% 3.76% 3.84%
ResNeXt-64x16: 3.84% 3.60%
The average results in the paper is 3.65% and 3.58% for these two net.
Does someone have the same question? Any suggestions?
Thanks a lot.
Hi,
In appendix "A. Implementation Details: CIFAR" of paper "Aggregated Residual Transformations for Deep Neural Networks", it is written that: "We adopt the pre-activation style block as in [14]", where [14] is the paper "Identity mappings in deep residual networks.". However, when I look at the resnext_bottleneck_B, I see it has the original style block with BN before the sum and ReLU after. Did you get your results with the original style block or with the pre-activation style block ?
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