Comments (2)
Thanks a lot for your interest.
Ideally, we can preprocess all accuracies and save top-1 in the benchmark files. In practice, as there are some conditions to obtain the top-1%, for example, top-1% on CIFAR-10 w.r.t. the mean accuracy, top-1% on CIFAR-10 w.r.t. the mean test loss.
There could be multiple top-1%, and I cannot give an assumption for what the users want. Therefore, we did not provide such top-1% models, and it is better to do on the user side.
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Gotta, Thank your suggestions :)
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Related Issues (20)
- The reasults of test accuracy HOT 2
- get_cost_info(hp="200") returns weird value for some models in topology search space HOT 4
- Problem recreating api in fastmode HOT 2
- The results of validation/test accuracy in NATS-Bench paper HOT 4
- best accuracy find HOT 3
- The code for ImageNet16 is 404 error HOT 4
- How to generate a architecute model with torch HOT 4
- Question regarding the design space for the topology search space in NATS-Bench HOT 1
- api_utils.py invoking get_latency method without passing "hp" parameter HOT 3
- No testset in ImageNet16 HOT 1
- Details about the data structures more_info and cost_info HOT 2
- Question about ImageNet16120 HOT 1
- Is NATS Extension of NAS_201 bench HOT 4
- get_net_param returns empty dictionary HOT 1
- Regarding the checkpoints HOT 3
- What is the seed of subnet training on cifar10, 111 or 777? HOT 3
- Further training of the network obtained from NATS bench
- Regarding inference with pretrained weights.
- get_more_info(): different seed but same 'train-accuracy', 'train-all-time', 'train-all-time'
- Question about the different init model weights of NATS-Bench-tss and NASbench201
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