Result table

This table was generated on 2025-11-24 at 03:45. 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 172.73 8.64 62.5 70.27 70.78 141.05 None BestOf2024-1 profs upnquick Success 2603.47 130.17 52.5 51.86 63.57 115.43 None BestOfMiles profs upnquick Success 5188.41 259.42 70.0 51.27 63.15 114.42 None BestOf2024-2 profs coktailjet Success 1159.73 57.99 75.0 51.98 59.69 111.67 None troublemakers Master-IASD coktailjet Success 163.66 8.18 75.0 48.51 54.98 103.49 None exocet Master-IASD upnquick Success 737.57 36.88 90.0 41.33 58.2 99.53 None noeyedeer Master-IASD upnquick Success 884.12 44.21 68.75 39.66 55.05 94.71 None BestOf2023-2 profs coktailjet Success 113.04 5.65 81.25 40.99 53.54 94.53 None ciclose-10 Master-IASD coktailjet Success 69.74 3.49 50.0 28.94 35.07 64.01 None attack_mesonet Master-IASD upnquick Success 81.76 4.09 68.75 25.66 32.91 58.57 None attaquedestitans Master-IASD coktailjet Success 71.72 3.59 62.5 18.65 38.41 57.06 None invisible_attack Master-IASD coktailjet Success 77.81 3.89 43.75 27.3 29.71 57.01 None attackonpixels Master-IASD coktailjet Success 71.99 3.6 62.5 6.05 25.14 31.19 None jogabonito Master-IASD coktailjet Success 68.42 3.42 50.0 6.05 25.13 31.18 None attaqueoudefense Master-IASD coktailjet Success 74.32 3.72 43.75 6.03 25.14 31.17 None neural-nightmare Master-IASD upnquick Success 126.27 6.31 56.25 5.99 25.17 31.16 None harissa Master-IASD upnquick Success 94.57 4.73 43.75 6.03 25.12 31.15 None madraf Master-IASD coktailjet Success 68.96 3.45 37.5 6.02 25.13 31.15 None base_model profs coktailjet Success 112.99 5.65 50.0 6.03 25.12 31.15 None counter_attack Master-IASD upnquick Success 95.44 4.77 62.5 6.02 25.12 31.14 None team_joie Master-IASD coktailjet Success 75.98 3.8 43.75 6.0 25.13 31.13 None blast_attack Master-IASD coktailjet Success 73.05 3.65 62.5 6.01 25.11 31.12 None the-advengers Master-IASD coktailjet Success 70.11 3.51 43.75 5.98 25.14 31.12 None nyc Master-IASD coktailjet Success 72.99 3.65 43.75 6.01 25.11 31.12 None attackonnetworks Master-IASD upnquick Success 80.12 4.01 56.25 5.99 25.12 31.11 None attackus Master-IASD upnquick Success 79.08 3.95 56.25 5.98 25.12 31.1 None best_defense_is_attack Master-IASD coktailjet Success 70.18 3.51 62.5 6.0 25.08 31.08 None attack-of-babrumen Master-IASD coktailjet Error 0 0 0 0 0 0 RuntimeError: Error(s) in loading state_dict for Net: Missing key(s) in state_dict: "fc1_stoch.mu.weight", "fc1_stoch.mu.bias", "fc1_stoch.sigma_pre_softplus.weight", "fc1_stoch.sigma_pre_softplus.bias", "fc2_stoch.mu.weight", "fc2_stoch.mu.bias", "fc2_stoch.sigma_pre_softplus.weight", "fc2_stoch.sigma_pre_softplus.bias". jean-ponce Master-IASD coktailjet Error 0 0 0 0 0 0 ModuleNotFoundError: No module named 'kornia' the-taithon-canon Master-IASD coktailjet Error 0 0 0 0 0 0 RuntimeError: Error(s) in loading state_dict for Net: Missing key(s) in state_dict: "backbone.bn1.weight", "backbone.bn1.bias", "backbone.bn1.running_mean", "backbone.bn1.running_var", "backbone.layer2.0.downsample.0.weight", "backbone.layer2.0.downsample.1.weight", "backbone.layer2.0.downsample.1.bias", "backbone.layer2.0.downsample.1.running_mean", "backbone.layer2.0.downsample.1.running_var", "backbone.layer3.0.downsample.0.weight", "backbone.layer3.0.downsample.1.weight", "backbone.layer3.0.downsample.1.bias", "backbone.layer3.0.downsample.1.running_mean", "backbone.layer3.0.downsample.1.running_var", "backbone.layer4.0.downsample.0.weight", "backbone.layer4.0.downsample.1.weight", "backbone.layer4.0.downsample.1.bias", "backbone.layer4.0.downsample.1.running_mean", "backbone.layer4.0.downsample.1.running_var", "backbone.fc.weight", "backbone.fc.bias". Unexpected key(s) in state_dict: "backbone.linear.weight", "backbone.linear.bias", "backbone.layer2.0.shortcut.0.weight", "backbone.layer3.0.shortcut.0.weight", "backbone.layer4.0.shortcut.0.weight". size mismatch for backbone.layer2.0.bn1.weight: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for backbone.layer2.0.bn1.bias: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for backbone.layer2.0.bn1.running_mean: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for backbone.layer2.0.bn1.running_var: copying a param with shape torch.Size([64]) from checkpoint, the shape in current model is torch.Size([128]). size mismatch for backbone.layer3.0.bn1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone.layer3.0.bn1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone.layer3.0.bn1.running_mean: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone.layer3.0.bn1.running_var: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([256]). size mismatch for backbone.layer4.0.bn1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for backbone.layer4.0.bn1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for backbone.layer4.0.bn1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]). size mismatch for backbone.layer4.0.bn1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([512]).

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