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logo-matching-base

Zero-Shot Image Classification
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metadata
library_name: transformers
base_model: ellabettison/logo-matching-base
tags:
  - generated_from_trainer
model-index:
  - name: logo-matching-base
    results: []

logo-matching-base

This model is a fine-tuned version of ellabettison/logo-matching-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Adjusted Rand Score: 0.1048
  • Adjusted Mutual Info Score: 0.1103
  • Homogeneity Score: 0.1072
  • Completeness Score: 0.6399
  • Fowlkes Mallows Score: 0.4934
  • Pair Confusion Matrix: [[13442, 34208], [1176, 12430]]
  • Loss: 0.1048

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 32
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Adjusted Rand Score Adjusted Mutual Info Score Homogeneity Score Completeness Score Fowlkes Mallows Score Pair Confusion Matrix Validation Loss
0.0988 1.0 34 0.0667 0.1257 0.1492 0.5530 0.4345 [[17354, 30296], [3408, 10198]] 0.0667
0.0918 2.0 68 0.0512 0.0801 0.1155 0.4762 0.4303 [[15840, 31810], [3306, 10300]] 0.0512
0.0972 3.0 102 0.0494 0.1485 0.2067 0.4977 0.3496 [[27372, 20278], [6908, 6698]] 0.0494
0.1027 4.0 136 0.1550 0.1639 0.2052 0.5160 0.4118 [[30918, 16732], [6132, 7474]] 0.1550
0.0944 5.0 170 0.1798 0.1948 0.2833 0.5017 0.3910 [[35784, 11866], [7490, 6116]] 0.1798
0.09 6.0 204 0.1186 0.1355 0.1836 0.5103 0.4414 [[23042, 24608], [4096, 9510]] 0.1186
0.0858 7.0 238 0.0756 0.1533 0.2163 0.4937 0.3570 [[29292, 18358], [7030, 6576]] 0.0756
0.0735 8.0 272 0.1107 0.1555 0.2280 0.4815 0.3651 [[31962, 15688], [7288, 6318]] 0.1107
0.0868 9.0 306 0.2523 0.2273 0.3022 0.5371 0.4428 [[36956, 10694], [6766, 6840]] 0.2523
0.0932 10.0 340 0.2318 0.2251 0.3703 0.4901 0.3838 [[41708, 5942], [9010, 4596]] 0.2318
0.0642 11.0 374 0.1840 0.1915 0.2284 0.5528 0.4331 [[31348, 16302], [5754, 7852]] 0.1840
0.0918 12.0 408 0.1472 0.1783 0.2400 0.5135 0.3946 [[32168, 15482], [6722, 6884]] 0.1472
0.0818 13.0 442 0.2861 0.2445 0.3090 0.5556 0.4658 [[37650, 10000], [6500, 7106]] 0.2861
0.0877 14.0 476 0.1159 0.1723 0.2474 0.4934 0.3621 [[32834, 14816], [7496, 6110]] 0.1159
0.0849 15.0 510 0.1053 0.1092 0.1086 0.6293 0.4935 [[13498, 34152], [1182, 12424]] 0.1053
0.0927 16.0 544 0.1119 0.1599 0.2309 0.4880 0.3674 [[31812, 15838], [7216, 6390]] 0.1119
0.0794 17.0 578 0.2050 0.2036 0.2405 0.5662 0.4462 [[31942, 15708], [5590, 8016]] 0.2050
0.0888 18.0 612 0.2215 0.2213 0.3139 0.5143 0.4002 [[38766, 8884], [7984, 5622]] 0.2215
0.0815 19.0 646 0.1725 0.1935 0.2815 0.4997 0.3833 [[35946, 11704], [7668, 5938]] 0.1725
0.0796 20.0 680 0.1048 0.1103 0.1072 0.6399 0.4934 [[13442, 34208], [1176, 12430]] 0.1048

Framework versions

  • Transformers 4.47.1
  • Pytorch 2.5.1+cu121
  • Datasets 3.2.0
  • Tokenizers 0.21.0