Detr Resnet 50 Fine Tuned Loc 2023

--- license: apache-2.0 library_name: transformers tags: - lam - newspapers datasets: - biglam/loc_beyond_words pipeline_tag: object-detection base_model: facebook/detr-resnet-50 model-index: - name: detr-resnet-50_fine_tuned_loc-2023 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_fine_tuned_loc-2023 > **For new projects**, consider [`small-models-for-glam/historic-newspaper-illustrations-yolov11`](https://huggingface.co/small-models-for-glam/historic-newspaper-illustrations-yolov11) — a more recent YOLO-based detector for illustrations in historic newspaper pages, faster at inference. This DETR model is kept for reproducibility. This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the loc_beyond_words dataset. It achieves the following results on the evaluation set: - Loss: 0.8784 ## 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: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.731 | 0.16 | 50 | 2.6356 | | 2.4875 | 0.31 | 100 | 2.2348 | | 2.1786 | 0.47 | 150 | 2.1148 | | 1.9845 | 0.62 | 200 | 1.8847 | | 1.8507 | 0.78 | 250 | 1.8331 | | 1.6813 | 0.94 | 300 | 1.5620 | | 1.5613 | 1.09 | 350 | 1.5898 | | 1.4966 | 1.25 | 400 | 1.4161 | | 1.4831 | 1.41 | 450 | 1.4831 | | 1.4587 | 1.56 | 500 | 1.3218 | | 1.433 | 1.72 | 550 | 1.3529 | | 1.33 | 1.88 | 600 | 1.2453 | | 1.2842 | 2.03 | 650 | 1.2956 | | 1.2807 | 2.19 | 700 | 1.1993 | | 1.1767 | 2.34 | 750 | 1.1557 | | 1.2134 | 2.5 | 800 | 1.1393 | | 1.1897 | 2.66 | 850 | 1.2016 | | 1.1784 | 2.81 | 900 | 1.1235 | | 1.2016 | 2.97 | 950 | 1.1378 | | 1.06 | 3.12 | 1000 | 1.0803 | | 1.1124 | 3.28 | 1050 | 1.1145 | | 1.1191 | 3.44 | 1100 | 1.0523 | | 1.0819 | 3.59 | 1150 | 1.0165 | | 1.1196 | 3.75 | 1200 | 1.0...