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detr-finetuned-cppe-5-10k-steps

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metadata
library_name: transformers
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
  - object-detection
  - vision
  - generated_from_trainer
model-index:
  - name: detr-finetuned-cppe-5-10k-steps
    results: []

detr-finetuned-cppe-5-10k-steps

This model is a fine-tuned version of facebook/detr-resnet-50 on the cppe-5 dataset. It achieves the following results on the evaluation set:

  • Loss: 1.6238
  • Map: 0.1619
  • Map 50: 0.3226
  • Map 75: 0.1429
  • Map Small: 0.0501
  • Map Medium: 0.129
  • Map Large: 0.227
  • Mar 1: 0.1773
  • Mar 10: 0.3167
  • Mar 100: 0.3392
  • Mar Small: 0.128
  • Mar Medium: 0.2626
  • Mar Large: 0.4711
  • Map Coverall: 0.4278
  • Mar 100 Coverall: 0.6532
  • Map Face Shield: 0.1078
  • Mar 100 Face Shield: 0.2937
  • Map Gloves: 0.0679
  • Mar 100 Gloves: 0.2991
  • Map Goggles: 0.0102
  • Mar 100 Goggles: 0.0985
  • Map Mask: 0.1959
  • Mar 100 Mask: 0.3516

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: 5e-05
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 1337
  • 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: 10.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Map Map 50 Map 75 Map Coverall Map Face Shield Map Gloves Map Goggles Map Mask Map Large Map Medium Map Small Mar 1 Mar 10 Mar 100 Mar 100 Coverall Mar 100 Face Shield Mar 100 Gloves Mar 100 Goggles Mar 100 Mask Mar Large Mar Medium Mar Small
2.5499 1.0 213 2.3248 0.0367 0.0798 0.0325 0.1697 0.0 0.0078 0.0 0.0058 0.0417 0.0114 0.0018 0.0555 0.1278 0.1648 0.5104 0.0 0.1813 0.0 0.1324 0.2026 0.1044 0.0416
2.1119 2.0 426 2.0867 0.0493 0.1064 0.0379 0.1978 0.0 0.0253 0.0 0.0236 0.0574 0.0304 0.0072 0.0805 0.1667 0.2069 0.5874 0.0 0.204 0.0 0.2431 0.2325 0.1572 0.073
2.0052 3.0 639 2.1689 0.0563 0.1279 0.0441 0.0154 0.0414 0.0688 0.0818 0.1611 0.1793 0.0616 0.131 0.2199 0.2039 0.4757 0.0 0.0 0.0158 0.1942 0.0 0.0 0.0618 0.2267
1.9373 4.0 852 1.9264 0.0813 0.1755 0.0679 0.0125 0.056 0.0953 0.0916 0.1816 0.206 0.0662 0.1446 0.2549 0.3464 0.6302 0.0 0.0 0.0245 0.2004 0.0 0.0 0.0357 0.1996
1.8396 5.0 1065 1.8418 0.0958 0.208 0.0731 0.0194 0.0751 0.1163 0.116 0.2195 0.2399 0.095 0.1802 0.2939 0.3278 0.6054 0.0228 0.1127 0.031 0.2071 0.0 0.0 0.0975 0.2742
1.7659 6.0 1278 1.8737 0.1004 0.2399 0.0791 0.0377 0.0913 0.1207 0.1226 0.2204 0.2382 0.0808 0.1912 0.295 0.2906 0.5856 0.0439 0.1165 0.0497 0.2281 0.0 0.0 0.1178 0.2609
1.6415 7.0 1491 1.7200 0.1305 0.2822 0.105 0.044 0.1026 0.172 0.1505 0.2689 0.2891 0.1047 0.2239 0.386 0.3916 0.6257 0.0516 0.1962 0.0526 0.2554 0.0059 0.0523 0.1508 0.316
1.6405 8.0 1704 1.6820 0.1411 0.2866 0.1303 0.0542 0.1162 0.1854 0.1572 0.2867 0.3088 0.1204 0.2432 0.4042 0.4147 0.6347 0.0549 0.2468 0.0592 0.271 0.0053 0.0631 0.1713 0.3284
1.5513 9.0 1917 1.6380 0.1546 0.3144 0.1307 0.0569 0.1215 0.2131 0.17 0.3014 0.323 0.112 0.2563 0.4431 0.4352 0.6414 0.0804 0.2544 0.0657 0.2871 0.0062 0.0846 0.1853 0.3476
1.5564 10.0 2130 1.6238 0.1619 0.3226 0.1429 0.0501 0.129 0.227 0.1773 0.3167 0.3392 0.128 0.2626 0.4711 0.4278 0.6532 0.1078 0.2937 0.0679 0.2991 0.0102 0.0985 0.1959 0.3516

Framework versions

  • Transformers 4.52.0.dev0
  • Pytorch 2.7.0+cu118
  • Datasets 3.6.0
  • Tokenizers 0.21.1