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metadata
library_name: transformers
license: apache-2.0
base_model: PekingU/rtdetr_r50vd_coco_o365
tags:
  - generated_from_trainer
model-index:
  - name: rtdetr-r50-fruits2-finetune
    results: []

rtdetr-r50-fruits2-finetune

This model is a fine-tuned version of PekingU/rtdetr_r50vd_coco_o365 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 12.5005
  • Map: 0.4374
  • Map 50: 0.5647
  • Map 75: 0.4601
  • Map Small: 0.1357
  • Map Medium: 0.3572
  • Map Large: 0.617
  • Mar 1: 0.2516
  • Mar 10: 0.5739
  • Mar 100: 0.7003
  • Mar Small: 0.3578
  • Mar Medium: 0.6855
  • Mar Large: 0.8642
  • Map Apple: 0.4334
  • Mar 100 Apple: 0.6864
  • Map Banana: 0.4636
  • Mar 100 Banana: 0.7153
  • Map Grapes: 0.4037
  • Mar 100 Grapes: 0.6071
  • Map Orange: 0.305
  • Mar 100 Orange: 0.6301
  • Map Pineapple: 0.5309
  • Mar 100 Pineapple: 0.7449
  • Map Watermelon: 0.4881
  • Mar 100 Watermelon: 0.8182

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: 8
  • eval_batch_size: 8
  • 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
  • lr_scheduler_warmup_steps: 300
  • num_epochs: 1

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 Apple Mar 100 Apple Map Banana Mar 100 Banana Map Grapes Mar 100 Grapes Map Orange Mar 100 Orange Map Pineapple Mar 100 Pineapple Map Watermelon Mar 100 Watermelon
35.0679 1.0 750 12.5005 0.4374 0.5647 0.4601 0.1357 0.3572 0.617 0.2516 0.5739 0.7003 0.3578 0.6855 0.8642 0.4334 0.6864 0.4636 0.7153 0.4037 0.6071 0.305 0.6301 0.5309 0.7449 0.4881 0.8182

Framework versions

  • Transformers 4.53.0.dev0
  • Pytorch 2.6.0+cu124
  • Datasets 3.6.0
  • Tokenizers 0.21.1