readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - biglam/nls_chapbook_illustrations
  model-index:
  - name: detr-resnet-50_fine_tuned_nls_chapbooks
    results: []
  widget:
  - src: https://huggingface.co/davanstrien/detr-resnet-50_fine_tuned_nls_chapbooks/resolve/main/Chapbook_Jack_the_Giant_Killer.jpg
    example_title: Jack the Giant Killer
  - src: https://huggingface.co/davanstrien/detr-resnet-50_fine_tuned_nls_chapbooks/resolve/main/PN970_G6_V3_1846_DUP_0011.jpg
    example_title: History of Valentine and Orson
  ---
  
  <!-- 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_nls_chapbooks
  
  This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the `biglam/nls_chapbook_illustrations` dataset. This dataset contains images of chapbooks with bounding boxes for the illustrations contained on some of the pages. 
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  ### Using in a transformer pipeline 
  
  The easiest way to use this model is via a [Transformers pipeline](https://huggingface.co/docs/transformers/main/en/pipeline_tutorial#vision-pipeline). To do this, you should first load the model and feature extractor:
  
  ```python 
  from transformers import AutoFeatureExtractor, AutoModelForObjectDetection
  
  extractor = AutoFeatureExtractor.from_pretrained("davanstrien/detr-resnet-50_fine_tuned_nls_chapbooks")
  
  model = AutoModelForObjectDetection.from_pretrained("davanstrien/detr-resnet-50_fine_tuned_nls_chapbooks")
  ```
  
  Then you can create a pipeline for object detection using the model. 
  
  ```python
  from transformers import pipeline
  
  pipe = pipeline('object-detection',model=model, feature_extractor=extractor)
  ```
  
  To use this to make predictions pass in an image (or a file-path/URL for the image):
  
  ```python 
  >>> pipe("https://huggingface.co/davanstrien/detr-resnet-50_fine_tuned_nls_chapbooks/resolve/main/Chapbook_Jack_the_Giant_Killer.jpg")
  [{'box': {'xmax': 290, 'xmin': 70, 'ymax': 510, 'ymin': 261},
    'label': 'early_printed_illustration',
    'score': 0.998455286026001}]
   ```
  
  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: 10
  
  ### Training results
  
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  
  ### Example image credits 
  
  https://commons.wikimedia.org/wiki/File:Chapbook_Jack_the_Giant_Killer.jpg
  https://archive.org/details/McGillLibrary-PN970_G6_V3_1846-1180/