Result table

This table was generated on 2025-12-01 at 09:03. 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 jean-ponce Master-IASD upnquick Success 10960.33 548.02 100.0 97.84 99.36 197.2 None BestOf2023-1 profs upnquick Success 255.75 12.79 50.0 70.27 70.84 141.11 None exocet Master-IASD coktailjet Success 1794.76 89.74 86.25 51.06 66.25 117.31 None BestOf2024-1 profs upnquick Success 3865.44 193.27 83.75 51.86 63.58 115.44 None the-taithon-canon Master-IASD coktailjet Success 4860.27 243.01 81.25 49.41 65.8 115.21 None BestOfMiles profs upnquick Success 9489.7 474.49 71.25 51.47 63.27 114.74 None BestOf2024-2 profs coktailjet Success 1158.27 57.91 62.5 52.14 60.17 112.31 None best_defense_is_attack Master-IASD coktailjet Success 2886.8 144.34 81.25 50.99 59.23 110.22 None counter_attack Master-IASD coktailjet Success 2765.91 138.3 75.0 46.29 62.94 109.23 None invisible_attack Master-IASD upnquick Success 15524.17 776.21 77.5 50.8 57.93 108.73 None attackonpixels Master-IASD upnquick Success 2148.66 107.43 75.0 43.83 52.57 96.4 None neural-nightmare Master-IASD upnquick Success 1317.63 65.88 68.75 41.82 53.1 94.92 None BestOf2023-2 profs coktailjet Success 112.69 5.63 68.75 41.08 53.47 94.55 None jogabonito Master-IASD coktailjet Success 263.15 13.16 75.0 24.63 46.41 71.04 None gradient-hackers Master-IASD upnquick Success 222.55 11.13 31.25 29.17 34.91 64.08 None the-advengers Master-IASD coktailjet Success 109.12 5.46 50.0 28.62 34.61 63.23 None attaquedestitans Master-IASD upnquick Success 195.8 9.79 56.25 18.58 38.36 56.94 None attack_mesonet Master-IASD coktailjet Success 73.2 3.66 62.5 22.38 33.28 55.66 None attack-of-babrumen Master-IASD upnquick Success 198.54 9.93 62.5 13.44 31.38 44.82 None troublemakers Master-IASD coktailjet Success 9006.35 450.32 77.5 11.12 21.46 32.58 None attackus Master-IASD coktailjet Success 68.64 3.43 50.0 6.04 25.14 31.18 None attackonnetworks Master-IASD coktailjet Success 72.56 3.63 37.5 6.02 25.16 31.18 None blast_attack Master-IASD coktailjet Success 110.23 5.51 62.5 6.04 25.13 31.17 None attaqueoudefense Master-IASD coktailjet Success 110.16 5.51 50.0 6.0 25.15 31.15 None nyc Master-IASD coktailjet Success 84.02 4.2 68.75 6.01 25.14 31.15 None base_model profs coktailjet Success 110.91 5.55 62.5 6.01 25.12 31.13 None rattataque Master-IASD coktailjet Success 83.86 4.19 62.5 5.99 25.13 31.12 None noeyedeer Master-IASD coktailjet Success 1940.39 97.02 12.5 12.78 14.01 26.79 None ciclose-10 Master-IASD coktailjet Error 0 0 0 0 0 0 FileNotFoundError: [Errno 2] No such file or directory: '/home/lamsade/testplatform/test-platform-a3/repos/Master-IASD/ciclose-10/models/default_model.pth' harissa Master-IASD upnquick Error 0 0 0 0 0 0 TypeError: stat: path should be string, bytes, os.PathLike or integer, not NoneType madraf Master-IASD upnquick Error 0 0 0 0 0 0 TypeError: Net.load() missing 1 required positional argument: 'device' team_joie 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: "conv1.weight", "conv1.bias", "conv2.weight", "conv2.bias", "conv3.weight", "conv3.bias", "conv4.weight", "conv4.bias", "fc1.weight", "fc1.bias", "fc2.weight", "fc2.bias", "fc3.weight", "fc3.bias". Unexpected key(s) in state_dict: "model.conv1.weight", "model.bn1.weight", "model.bn1.bias", "model.bn1.running_mean", "model.bn1.running_var", "model.bn1.num_batches_tracked", "model.layer1.0.conv1.weight", "model.layer1.0.bn1.weight", "model.layer1.0.bn1.bias", "model.layer1.0.bn1.running_mean", "model.layer1.0.bn1.running_var", "model.layer1.0.bn1.num_batches_tracked", "model.layer1.0.conv2.weight", "model.layer1.0.bn2.weight", "model.layer1.0.bn2.bias", "model.layer1.0.bn2.running_mean", "model.layer1.0.bn2.running_var", "model.layer1.0.bn2.num_batches_tracked", "model.layer1.1.conv1.weight", "model.layer1.1.bn1.weight", "model.layer1.1.bn1.bias", "model.layer1.1.bn1.running_mean", "model.layer1.1.bn1.running_var", "model.layer1.1.bn1.num_batches_tracked", "model.layer1.1.conv2.weight", "model.layer1.1.bn2.weight", "model.layer1.1.bn2.bias", "model.layer1.1.bn2.running_mean", "model.layer1.1.bn2.running_var", "model.layer1.1.bn2.num_batches_tracked", "model.layer2.0.conv1.weight", "model.layer2.0.bn1.weight", "model.layer2.0.bn1.bias", "model.layer2.0.bn1.running_mean", "model.layer2.0.bn1.running_var", "model.layer2.0.bn1.num_batches_tracked", "model.layer2.0.conv2.weight", "model.layer2.0.bn2.weight", "model.layer2.0.bn2.bias", "model.layer2.0.bn2.running_mean", "model.layer2.0.bn2.running_var", "model.layer2.0.bn2.num_batches_tracked", "model.layer2.0.downsample.0.weight", "model.layer2.0.downsample.1.weight", "model.layer2.0.downsample.1.bias", "model.layer2.0.downsample.1.running_mean", "model.layer2.0.downsample.1.running_var", "model.layer2.0.downsample.1.num_batches_tracked", "model.layer2.1.conv1.weight", "model.layer2.1.bn1.weight", "model.layer2.1.bn1.bias", "model.layer2.1.bn1.running_mean", "model.layer2.1.bn1.running_var", "model.layer2.1.bn1.num_batches_tracked", "model.layer2.1.conv2.weight", "model.layer2.1.bn2.weight", "model.layer2.1.bn2.bias", "model.layer2.1.bn2.running_mean", "model.layer2.1.bn2.running_var", "model.layer2.1.bn2.num_batches_tracked", "model.layer3.0.conv1.weight", "model.layer3.0.bn1.weight", "model.layer3.0.bn1.bias", "model.layer3.0.bn1.running_mean", "model.layer3.0.bn1.running_var", "model.layer3.0.bn1.num_batches_tracked", "model.layer3.0.conv2.weight", "model.layer3.0.bn2.weight", "model.layer3.0.bn2.bias", "model.layer3.0.bn2.running_mean", "model.layer3.0.bn2.running_var", "model.layer3.0.bn2.num_batches_tracked", "model.layer3.0.downsample.0.weight", "model.layer3.0.downsample.1.weight", "model.layer3.0.downsample.1.bias", "model.layer3.0.downsample.1.running_mean", "model.layer3.0.downsample.1.running_var", "model.layer3.0.downsample.1.num_batches_tracked", "model.layer3.1.conv1.weight", "model.layer3.1.bn1.weight", "model.layer3.1.bn1.bias", "model.layer3.1.bn1.running_mean", "model.layer3.1.bn1.running_var", "model.layer3.1.bn1.num_batches_tracked", "model.layer3.1.conv2.weight", "model.layer3.1.bn2.weight", "model.layer3.1.bn2.bias", "model.layer3.1.bn2.running_mean", "model.layer3.1.bn2.running_var", "model.layer3.1.bn2.num_batches_tracked", "model.layer4.0.conv1.weight", "model.layer4.0.bn1.weight", "model.layer4.0.bn1.bias", "model.layer4.0.bn1.running_mean", "model.layer4.0.bn1.running_var", "model.layer4.0.bn1.num_batches_tracked", "model.layer4.0.conv2.weight", "model.layer4.0.bn2.weight", "model.layer4.0.bn2.bias", "model.layer4.0.bn2.running_mean", "model.layer4.0.bn2.running_var", "model.layer4.0.bn2.num_batches_tracked", "model.layer4.0.downsample.0.weight", "model.layer4.0.downsample.1.weight", "model.layer4.0.downsample.1.bias", "model.layer4.0.downsample.1.running_mean", "model.layer4.0.downsample.1.running_var", "model.layer4.0.downsample.1.num_batches_tracked", "model.layer4.1.conv1.weight", "model.layer4.1.bn1.weight", "model.layer4.1.bn1.bias", "model.layer4.1.bn1.running_mean", "model.layer4.1.bn1.running_var", "model.layer4.1.bn1.num_batches_tracked", "model.layer4.1.conv2.weight", "model.layer4.1.bn2.weight", "model.layer4.1.bn2.bias", "model.layer4.1.bn2.running_mean", "model.layer4.1.bn2.running_var", "model.layer4.1.bn2.num_batches_tracked", "model.fc.weight", "model.fc.bias".

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