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license: apache-2.0
- generated_from_trainer
- biglam/nls_chapbook_illustrations
- name: detr-resnet-50_fine_tuned_nls_chapbooks
results: []
- 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
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# 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:
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.
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):
>>> 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