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detr_finetuned_cppe5

Object Detection
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Safetensors
conditional_detr
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detr_finetuned_cppe5 / README.md
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metadata
library_name: transformers
license: apache-2.0
base_model: microsoft/conditional-detr-resnet-50
tags:
  - generated_from_trainer
model-index:
  - name: detr_finetuned_cppe5
    results: []

detr_finetuned_cppe5

This model is a fine-tuned version of microsoft/conditional-detr-resnet-50 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1983
  • Map: 0.0
  • Map 50: 0.0
  • Map 75: 0.0
  • Map Small: 0.0
  • Map Medium: -1.0
  • Map Large: -1.0
  • Mar 1: 0.0
  • Mar 10: 0.0
  • Mar 100: 0.0
  • Mar Small: 0.0
  • Mar Medium: -1.0
  • Mar Large: -1.0
  • Map Coverall: 0.0
  • Mar 100 Coverall: 0.0
  • Map Face Shield: 0.0
  • Mar 100 Face Shield: 0.0
  • Map Gloves: 0.0
  • Mar 100 Gloves: 0.0
  • Map Goggles: 0.0
  • Mar 100 Goggles: 0.0
  • Map Mask: 0.0
  • Mar 100 Mask: 0.0

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: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Map Map 50 Map 75 Map Small Map Medium Map Large Mar 1 Mar 10 Mar 100 Mar Small Mar Medium Mar Large Map Coverall Mar 100 Coverall Map Face Shield Mar 100 Face Shield Map Gloves Mar 100 Gloves Map Goggles Mar 100 Goggles Map Mask Mar 100 Mask
No log 1.0 213 1.8601 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
No log 2.0 426 1.7149 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2.6515 3.0 639 1.6423 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2.6515 4.0 852 1.5550 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.4249 5.0 1065 1.5036 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.4249 6.0 1278 1.4294 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.4249 7.0 1491 1.3962 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.274 8.0 1704 1.4138 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.274 9.0 1917 1.3613 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.1181 10.0 2130 1.3641 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.1181 11.0 2343 1.3022 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.0214 12.0 2556 1.2772 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.0214 13.0 2769 1.2671 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1.0214 14.0 2982 1.2379 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.9101 15.0 3195 1.2388 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.9101 16.0 3408 1.2240 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.8119 17.0 3621 1.2378 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.8119 18.0 3834 1.2233 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.7365 19.0 4047 1.2254 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.7365 20.0 4260 1.2334 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.7365 21.0 4473 1.2130 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.6639 22.0 4686 1.2187 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.6639 23.0 4899 1.2093 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.6065 24.0 5112 1.1955 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.6065 25.0 5325 1.1984 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.5682 26.0 5538 1.1981 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.5682 27.0 5751 1.1978 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.5682 28.0 5964 1.1991 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.5442 29.0 6177 1.1989 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.5442 30.0 6390 1.1983 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Framework versions

  • Transformers 4.46.0
  • Pytorch 2.2.2
  • Datasets 3.0.2
  • Tokenizers 0.20.1