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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- biglam/nls_chapbook_illustrations |
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widget: |
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- src: >- |
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https://huggingface.co/davanstrien/detr-resnet-50_fine_tuned_nls_chapbooks/resolve/main/Chapbook_Jack_the_Giant_Killer.jpg |
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example_title: Jack the Giant Killer |
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- src: >- |
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https://huggingface.co/davanstrien/detr-resnet-50_fine_tuned_nls_chapbooks/resolve/main/PN970_G6_V3_1846_DUP_0011.jpg |
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example_title: History of Valentine and Orson |
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base_model: facebook/detr-resnet-50 |
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model-index: |
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- name: detr-resnet-50_fine_tuned_nls_chapbooks |
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results: [] |
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library_name: transformers |
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pipeline_tag: object-detection |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# detr-resnet-50_fine_tuned_nls_chapbooks |
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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. |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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### Using in a transformer pipeline |
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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: |
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```python |
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from transformers import AutoFeatureExtractor, AutoModelForObjectDetection |
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extractor = AutoFeatureExtractor.from_pretrained("davanstrien/detr-resnet-50_fine_tuned_nls_chapbooks") |
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model = AutoModelForObjectDetection.from_pretrained("davanstrien/detr-resnet-50_fine_tuned_nls_chapbooks") |
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``` |
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Then you can create a pipeline for object detection using the model. |
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```python |
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from transformers import pipeline |
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pipe = pipeline('object-detection',model=model, feature_extractor=extractor) |
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``` |
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To use this to make predictions pass in an image (or a file-path/URL for the image): |
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```python |
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>>> pipe("https://huggingface.co/davanstrien/detr-resnet-50_fine_tuned_nls_chapbooks/resolve/main/Chapbook_Jack_the_Giant_Killer.jpg") |
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[{'box': {'xmax': 290, 'xmin': 70, 'ymax': 510, 'ymin': 261}, |
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'label': 'early_printed_illustration', |
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'score': 0.998455286026001}] |
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``` |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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### Example image credits |
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https://commons.wikimedia.org/wiki/File:Chapbook_Jack_the_Giant_Killer.jpg |
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https://archive.org/details/McGillLibrary-PN970_G6_V3_1846-1180/ |
