detr-resnet-50-dc5-fashionpedia-finetuned-jw

• eval_loss: 3.4234
• eval_map: 0.0022
• eval_map_50: 0.0063
• eval_map_75: 0.0008
• eval_map_small: 0.0022
• eval_map_medium: 0.003
• eval_map_large: 0.0007
• eval_mar_1: 0.0034
• eval_mar_10: 0.0093
• eval_mar_100: 0.0131
• eval_mar_small: 0.009
• eval_mar_medium: 0.0171
• eval_mar_large: 0.0144
• eval_map_shirt, blouse: 0.0
• eval_mar_100_shirt, blouse: 0.0
• eval_map_top, t-shirt, sweatshirt: 0.0
• eval_mar_100_top, t-shirt, sweatshirt: 0.0
• eval_map_sweater: 0.0
• eval_mar_100_sweater: 0.0
• eval_map_cardigan: 0.0
• eval_mar_100_cardigan: 0.0
• eval_map_jacket: 0.0
• eval_mar_100_jacket: 0.0
• eval_map_vest: 0.0
• eval_mar_100_vest: 0.0
• eval_map_pants: 0.0
• eval_mar_100_pants: 0.0
• eval_map_shorts: 0.0
• eval_mar_100_shorts: 0.0
• eval_map_skirt: 0.0
• eval_mar_100_skirt: 0.0
• eval_map_coat: 0.0
• eval_mar_100_coat: 0.0
• eval_map_dress: 0.0
• eval_mar_100_dress: 0.0
• eval_map_jumpsuit: 0.0
• eval_mar_100_jumpsuit: 0.0
• eval_map_cape: 0.0
• eval_mar_100_cape: 0.0
• eval_map_glasses: 0.0
• eval_mar_100_glasses: 0.0
• eval_map_hat: 0.0
• eval_mar_100_hat: 0.0
• eval_map_headband, head covering, hair accessory: 0.0
• eval_mar_100_headband, head covering, hair accessory: 0.0
• eval_map_tie: 0.0
• eval_mar_100_tie: 0.0
• eval_map_glove: 0.0
• eval_mar_100_glove: 0.0
• eval_map_watch: 0.0
• eval_mar_100_watch: 0.0
• eval_map_belt: 0.0
• eval_mar_100_belt: 0.0
• eval_map_leg warmer: 0.0
• eval_mar_100_leg warmer: 0.0
• eval_map_tights, stockings: 0.0
• eval_mar_100_tights, stockings: 0.0
• eval_map_sock: 0.0
• eval_mar_100_sock: 0.0
• eval_map_shoe: 0.0939
• eval_mar_100_shoe: 0.3148
• eval_map_bag, wallet: 0.0
• eval_mar_100_bag, wallet: 0.0
• eval_map_scarf: 0.0
• eval_mar_100_scarf: 0.0
• eval_map_umbrella: 0.0
• eval_mar_100_umbrella: 0.0
• eval_map_hood: 0.0
• eval_mar_100_hood: 0.0
• eval_map_collar: 0.0
• eval_mar_100_collar: 0.0
• eval_map_lapel: 0.0
• eval_mar_100_lapel: 0.0
• eval_map_epaulette: 0.0
• eval_mar_100_epaulette: 0.0
• eval_map_sleeve: 0.0076
• eval_mar_100_sleeve: 0.2888
• eval_map_pocket: 0.0
• eval_mar_100_pocket: 0.0
• eval_map_neckline: 0.0
• eval_mar_100_neckline: 0.0
• eval_map_buckle: 0.0
• eval_mar_100_buckle: 0.0
• eval_map_zipper: 0.0
• eval_mar_100_zipper: 0.0
• eval_map_applique: 0.0
• eval_mar_100_applique: 0.0
• eval_map_bead: 0.0
• eval_mar_100_bead: 0.0
• eval_map_bow: 0.0
• eval_mar_100_bow: 0.0
• eval_map_flower: 0.0
• eval_mar_100_flower: 0.0
• eval_map_fringe: 0.0
• eval_mar_100_fringe: 0.0
• eval_map_ribbon: 0.0
• eval_mar_100_ribbon: 0.0
• eval_map_rivet: 0.0
• eval_mar_100_rivet: 0.0
• eval_map_ruffle: 0.0
• eval_mar_100_ruffle: 0.0
• eval_map_sequin: 0.0
• eval_mar_100_sequin: 0.0
• eval_map_tassel: 0.0
• eval_mar_100_tassel: 0.0
• eval_runtime: 202.1296
• eval_samples_per_second: 5.729
• eval_steps_per_second: 1.435
• epoch: 0.0482
• step: 550

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

• learning_rate: 1e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 10000
• mixed_precision_training: Native AMP

Framework versions

• Transformers 4.45.2
• Pytorch 2.4.1+cu121
• Datasets 3.0.1
• Tokenizers 0.20.1