pytorch tensorboard detr object-detection dataset:biglam/nls_chapbook_illustrations transformers generated_from_trainer license:apache-2.0 model-index
---
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
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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 nls_chapbook_illustrations dataset.

## 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/