Result table

This table was generated on 2025-11-20 at 11:23. See more results here. See last results here.

results

project_name group_name hostname status time time_per_image_ms acc_nat acc_pgdlinf acc_pgdl2 agg error_msg BestOf2023-1 profs upnquick Success 128.84 6.44 62.5 70.19 70.81 141.0 None BestOf2024-1 profs upnquick Success 1397.73 69.89 62.5 51.35 63.4 114.74 None BestOf2024-2 profs coktailjet Success 1154.0 57.7 81.25 52.49 59.51 112.0 None exocet Master-IASD coktailjet Success 478.79 23.94 98.75 41.91 58.32 100.23 None BestOf2023-2 profs coktailjet Success 109.3 5.46 56.25 41.02 53.42 94.44 None attack_mesonet Master-IASD coktailjet Success 72.69 3.63 50.0 25.67 32.9 58.57 None attack-of-babrumen Master-IASD upnquick Success 84.04 4.2 37.5 25.99 27.43 53.42 None invisible_attack Master-IASD coktailjet Success 98.43 4.92 62.5 6.05 25.14 31.19 None ciclose-10 Master-IASD upnquick Success 86.4 4.32 56.25 6.02 25.16 31.18 None counter_attack Master-IASD coktailjet Success 97.17 4.86 56.25 6.02 25.16 31.18 None blast_attack Master-IASD upnquick Success 85.69 4.28 43.75 6.05 25.12 31.17 None base_model profs coktailjet Success 108.08 5.4 43.75 6.03 25.12 31.15 None attackonnetworks Master-IASD coktailjet Success 72.31 3.62 56.25 6.0 25.14 31.14 None attackus Master-IASD coktailjet Success 73.04 3.65 62.5 6.03 25.11 31.14 None jogabonito Master-IASD upnquick Success 86.38 4.32 68.75 6.01 25.11 31.12 None attaqueoudefense Master-IASD upnquick Success 82.27 4.11 62.5 5.98 25.13 31.11 None attackonpixels Master-IASD upnquick Success 82.67 4.13 68.75 6.0 25.08 31.08 None the-taithon-canon Master-IASD upnquick Success 51.05 2.55 6.25 10.0 10.0 20.0 None BestOfMiles profs upnquick Error 0 0 0 0 0 0 OutOfMemoryError: CUDA out of memory. Tried to allocate 248.00 MiB. GPU 0 has a total capacity of 79.14 GiB of which 220.06 MiB is free. Process 567211 has 73.54 GiB memory in use. Process 662892 has 600.00 MiB memory in use. Including non-PyTorch memory, this process has 3.20 GiB memory in use. Process 662894 has 1.58 GiB memory in use. Of the allocated memory 2.56 GiB is allocated by PyTorch, and 152.30 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

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