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human-analysis / neural-architecture-transfer

Neural Architecture Transfer (Arxiv'20), PyTorch Implementation
http://hal.cse.msu.edu/papers/neural-architecture-transfer/
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* Acc@1 4.0 (95.982) Acc@5 10.9 (89.052) #6

Open NEUdeep opened 3 years ago

NEUdeep commented 3 years ago

sh run.sh M1 train val No horovod in environment Running validation on imagenet Data processing configuration for current model + dataset: input_size: (3, 224, 224) interpolation: bicubic mean: (0.485, 0.456, 0.406) std: (0.229, 0.224, 0.225) crop_pct: 0.875 Model created, param count: 6067408 Test: [ 0/782] Time: 0.926s (0.926s, 69.13/s) Loss: 6.8924 (6.8924) Acc@1: 4.688 ( 4.688) Acc@5: 12.500 ( 12.500) Test: [ 50/782] Time: 0.259s (0.106s, 601.80/s) Loss: 6.8926 (6.8912) Acc@1: 1.562 ( 5.453) Acc@5: 10.938 ( 14.614) Test: [ 100/782] Time: 0.040s (0.096s, 669.39/s) Loss: 6.8925 (6.8907) Acc@1: 7.812 ( 5.662) Acc@5: 15.625 ( 13.800) Test: [ 150/782] Time: 0.040s (0.092s, 698.14/s) Loss: 6.8940 (6.8904) Acc@1: 12.500 ( 5.702) Acc@5: 14.062 ( 13.742) Test: [ 200/782] Time: 0.152s (0.089s, 722.01/s) Loss: 6.8926 (6.8904) Acc@1: 4.688 ( 5.729) Acc@5: 14.062 ( 13.658) Test: [ 250/782] Time: 0.041s (0.089s, 717.89/s) Loss: 6.8923 (6.8905) Acc@1: 6.250 ( 5.671) Acc@5: 14.062 ( 13.515) Test: [ 300/782] Time: 0.043s (0.089s, 717.04/s) Loss: 6.8924 (6.8906) Acc@1: 4.688 ( 5.565) Acc@5: 9.375 ( 13.424) Test: [ 350/782] Time: 0.038s (0.089s, 716.55/s) Loss: 6.8927 (6.8905) Acc@1: 1.562 ( 5.235) Acc@5: 6.250 ( 12.985) Test: [ 400/782] Time: 0.052s (0.088s, 723.56/s) Loss: 6.8926 (6.8903) Acc@1: 1.562 ( 4.949) Acc@5: 3.125 ( 12.496) Test: [ 450/782] Time: 0.035s (0.089s, 719.58/s) Loss: 6.8925 (6.8902) Acc@1: 0.000 ( 4.691) Acc@5: 4.688 ( 12.088) Test: [ 500/782] Time: 0.337s (0.089s, 721.14/s) Loss: 6.8925 (6.8901) Acc@1: 1.562 ( 4.491) Acc@5: 6.250 ( 11.711) Test: [ 550/782] Time: 0.277s (0.088s, 723.97/s) Loss: 6.8925 (6.8900) Acc@1: 6.250 ( 4.356) Acc@5: 10.938 ( 11.496) Test: [ 600/782] Time: 0.041s (0.088s, 730.85/s) Loss: 6.8927 (6.8899) Acc@1: 1.562 ( 4.282) Acc@5: 6.250 ( 11.359) Test: [ 650/782] Time: 0.040s (0.088s, 730.81/s) Loss: 6.8924 (6.8899) Acc@1: 3.125 ( 4.179) Acc@5: 9.375 ( 11.245) Test: [ 700/782] Time: 0.041s (0.087s, 732.60/s) Loss: 6.8927 (6.8898) Acc@1: 3.125 ( 4.054) Acc@5: 4.688 ( 11.016) Test: [ 750/782] Time: 0.041s (0.087s, 731.90/s) Loss: 6.8929 (6.8898) Acc@1: 4.688 ( 4.026) Acc@5: 6.250 ( 10.952)

mikelzc1990 commented 3 years ago

Can you verify if the weights are properly downloaded ? They should be automatically downloaded, but sometimes you might have issues when you try to access them from outside US.

I quickly re-run the validation code, and everything work fine on my end.

NEUdeep commented 3 years ago

Thank you for your reply, I can visit the google website very well, but I’m not sure whether the downloaded weight file has lost part of the information. Below is my md5 value calculated for the downloaded weight by your imagenet config.net：

b7d0a136498ddad9d78b1795a98d5e15 M1/net.weights 9d15d7b75ce40fd77c64cadfaea02327 M2/net.weights a5ac8049e1ec9d1fb8c28d771d59464a M3/net.weights Look forward to your answer.

ealmenzar commented 3 years ago

Same problem here with pets dataset. Has anyone figured it out?