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detr-resnet-50-dc5-fashionpedia-finetuned

Object Detection
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detr
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metadata
library_name: transformers
license: apache-2.0
base_model: facebook/detr-resnet-50-dc5
tags:
  - generated_from_trainer
model-index:
  - name: detr-resnet-50-dc5-fashionpedia-finetuned
    results: []
datasets:
  - detection-datasets/fashionpedia

detr-resnet-50-dc5-fashionpedia-finetuned

This model is a fine-tuned version of facebook/detr-resnet-50-dc5 on a subset of fashionpedia dataset (10% of both train and test sets). It achieves the following results on the evaluation set:

  • Loss: 4.4538
  • Map: 0.0003
  • Map 50: 0.0015
  • Map 75: 0.0002
  • Map Small: 0.0
  • Map Medium: 0.0001
  • Map Large: 0.0013
  • Mar 1: 0.0013
  • Mar 10: 0.0052
  • Mar 100: 0.0071
  • Mar Small: 0.0007
  • Mar Medium: 0.0135
  • Mar Large: 0.0085
  • Map Shirt, blouse: 0.0
  • Mar 100 Shirt, blouse: 0.0
  • Map Top, t-shirt, sweatshirt: 0.0118
  • Mar 100 Top, t-shirt, sweatshirt: 0.1854
  • Map Sweater: 0.0
  • Mar 100 Sweater: 0.0
  • Map Cardigan: 0.0
  • Mar 100 Cardigan: 0.0
  • Map Jacket: 0.0
  • Mar 100 Jacket: 0.0
  • Map Vest: 0.0
  • Mar 100 Vest: 0.0
  • Map Pants: 0.0
  • Mar 100 Pants: 0.0
  • Map Shorts: 0.0
  • Mar 100 Shorts: 0.0
  • Map Skirt: 0.0
  • Mar 100 Skirt: 0.0
  • Map Coat: 0.0
  • Mar 100 Coat: 0.0
  • Map Dress: 0.0
  • Mar 100 Dress: 0.0
  • Map Glasses: 0.0
  • Mar 100 Glasses: 0.0
  • Map Hat: 0.0
  • Mar 100 Hat: 0.0
  • Map Headband, head covering, hair accessory: 0.0
  • Mar 100 Headband, head covering, hair accessory: 0.0
  • Map Tie: -1.0
  • Mar 100 Tie: -1.0
  • Map Glove: 0.0
  • Mar 100 Glove: 0.0
  • Map Watch: 0.0
  • Mar 100 Watch: 0.0
  • Map Belt: 0.0
  • Mar 100 Belt: 0.0
  • Map Tights, stockings: 0.0
  • Mar 100 Tights, stockings: 0.0
  • Map Sock: 0.0
  • Mar 100 Sock: 0.0
  • Map Shoe: 0.0002
  • Mar 100 Shoe: 0.064
  • Map Bag, wallet: 0.0
  • Mar 100 Bag, wallet: 0.0
  • Map Scarf: 0.0
  • Mar 100 Scarf: 0.0
  • Map Umbrella: 0.0
  • Mar 100 Umbrella: 0.0
  • Map Hood: 0.0
  • Mar 100 Hood: 0.0
  • Map Collar: 0.0
  • Mar 100 Collar: 0.0
  • Map Lapel: 0.0
  • Mar 100 Lapel: 0.0
  • Map Epaulette: 0.0
  • Mar 100 Epaulette: 0.0
  • Map Sleeve: 0.0
  • Mar 100 Sleeve: 0.0
  • Map Pocket: 0.0
  • Mar 100 Pocket: 0.0167
  • Map Neckline: 0.0
  • Mar 100 Neckline: 0.0044
  • Map Buckle: 0.0
  • Mar 100 Buckle: 0.0
  • Map Zipper: 0.0
  • Mar 100 Zipper: 0.0
  • Map Applique: 0.0
  • Mar 100 Applique: 0.0
  • Map Bead: 0.0
  • Mar 100 Bead: 0.0
  • Map Bow: 0.0
  • Mar 100 Bow: 0.0
  • Map Rivet: 0.0
  • Mar 100 Rivet: 0.0
  • Map Ruffle: 0.0
  • Mar 100 Ruffle: 0.0
  • Map Tassel: 0.0
  • Mar 100 Tassel: 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: 1e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • 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
  • training_steps: 500
  • mixed_precision_training: Native AMP

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 Shirt, blouse Mar 100 Shirt, blouse Map Top, t-shirt, sweatshirt Mar 100 Top, t-shirt, sweatshirt Map Sweater Mar 100 Sweater Map Cardigan Mar 100 Cardigan Map Jacket Mar 100 Jacket Map Vest Mar 100 Vest Map Pants Mar 100 Pants Map Shorts Mar 100 Shorts Map Skirt Mar 100 Skirt Map Coat Mar 100 Coat Map Dress Mar 100 Dress Map Jumpsuit Mar 100 Jumpsuit Map Cape Mar 100 Cape Map Glasses Mar 100 Glasses Map Hat Mar 100 Hat Map Headband, head covering, hair accessory Mar 100 Headband, head covering, hair accessory Map Tie Mar 100 Tie Map Glove Mar 100 Glove Map Watch Mar 100 Watch Map Belt Mar 100 Belt Map Leg warmer Mar 100 Leg warmer Map Tights, stockings Mar 100 Tights, stockings Map Sock Mar 100 Sock Map Shoe Mar 100 Shoe Map Bag, wallet Mar 100 Bag, wallet Map Scarf Mar 100 Scarf Map Umbrella Mar 100 Umbrella Map Hood Mar 100 Hood Map Collar Mar 100 Collar Map Lapel Mar 100 Lapel Map Epaulette Mar 100 Epaulette Map Sleeve Mar 100 Sleeve Map Pocket Mar 100 Pocket Map Neckline Mar 100 Neckline Map Buckle Mar 100 Buckle Map Zipper Mar 100 Zipper Map Applique Mar 100 Applique Map Bead Mar 100 Bead Map Bow Mar 100 Bow Map Flower Mar 100 Flower Map Fringe Mar 100 Fringe Map Ribbon Mar 100 Ribbon Map Rivet Mar 100 Rivet Map Ruffle Mar 100 Ruffle Map Sequin Mar 100 Sequin Map Tassel Mar 100 Tassel
5.6895 0.0438 50 6.3890 0.0 0.0001 0.0 0.0 0.0 0.0001 0.0001 0.0009 0.001 0.0 0.0013 0.0019 0.0 0.0 0.0004 0.039 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0
6.3331 0.0876 100 6.1659 0.0 0.0001 0.0 0.0 0.0 0.0002 0.0004 0.0016 0.0018 0.0 0.0019 0.0037 0.0 0.0 0.0014 0.0683 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0007 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0
5.5579 0.1315 150 5.7278 0.0003 0.002 0.0 0.0005 0.0001 0.0053 0.0008 0.0042 0.0057 0.0005 0.0064 0.0203 0.0 0.0 0.0039 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0086 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0059 0.0056 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0021 0.0002 0.0022 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0
5.2219 0.1753 200 5.3784 0.0003 0.002 0.0 0.0005 0.0001 0.0388 0.0011 0.0043 0.0058 0.0007 0.0074 0.0473 0.0 0.0 0.0048 0.1805 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0158 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0059 0.0056 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0008 0.0167 0.0 0.0 0.0 0.0 0.0 0.0 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
4.7244 0.2191 250 5.0133 0.0001 0.0005 0.0 0.0 0.0 0.0022 0.0008 0.0045 0.0059 0.0003 0.0069 0.0249 0.0 0.0 0.0046 0.1951 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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 0.0 0.0187 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0021 0.0001 0.0078 0.0 0.0 0.0 0.0 0.0 0.0 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
4.4979 0.2629 300 4.8280 0.0002 0.0006 0.0 0.0 0.0001 0.0006 0.0011 0.0047 0.0063 0.0002 0.0097 0.0099 0.0 0.0 0.0059 0.2049 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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 0.0001 0.0273 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0042 0.0001 0.0022 0.0 0.0 0.0 0.0 0.0 0.0 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
4.7632 0.3067 350 4.6091 0.0003 0.0009 0.0002 0.0 0.0001 0.0015 0.0014 0.0044 0.0062 0.0008 0.0089 0.0092 0.0 0.0 0.0114 0.1732 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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 0.0002 0.0532 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0021 0.0002 0.0067 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0
4.9522 0.3506 400 4.5602 0.0002 0.0012 0.0 0.0 0.0001 0.0007 0.0006 0.0048 0.0067 0.0005 0.0135 0.0076 0.0 0.0 0.0069 0.1732 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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 0.0002 0.0547 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0001 0.0229 0.0001 0.0044 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 0.0 0.0
4.0437 0.3944 450 4.4765 0.0004 0.0014 0.0002 0.0 0.0001 0.0016 0.0014 0.005 0.0069 0.0007 0.0118 0.0092 0.0 0.0 0.014 0.1829 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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 0.0002 0.0597 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0001 0.0167 0.0 0.0033 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4.1306 0.4382 500 4.4538 0.0003 0.0015 0.0002 0.0 0.0001 0.0013 0.0013 0.0052 0.0071 0.0007 0.0135 0.0085 0.0 0.0 0.0118 0.1854 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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 0.0002 0.064 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0167 0.0 0.0044 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Framework versions

  • Transformers 4.53.0
  • Pytorch 2.7.1+cu126
  • Datasets 3.6.0
  • Tokenizers 0.21.2