Instructions to use jaxnwagner/detr-resnet-50-dc5-fashionpedia-finetuned-jw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaxnwagner/detr-resnet-50-dc5-fashionpedia-finetuned-jw with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="jaxnwagner/detr-resnet-50-dc5-fashionpedia-finetuned-jw")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("jaxnwagner/detr-resnet-50-dc5-fashionpedia-finetuned-jw") model = AutoModelForObjectDetection.from_pretrained("jaxnwagner/detr-resnet-50-dc5-fashionpedia-finetuned-jw") - Notebooks
- Google Colab
- Kaggle
| 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-jw | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # detr-resnet-50-dc5-fashionpedia-finetuned-jw | |
| This model is a fine-tuned version of [facebook/detr-resnet-50-dc5](https://huggingface.co/facebook/detr-resnet-50-dc5) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - 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 | |
| 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: 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 | |