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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:

```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/
{"license": "apache-2.0", "library_name": "transformers", "tags": ["generated_from_trainer"], "datasets": ["biglam/nls_chapbook_illustrations"], "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"}], "base_model": "facebook/detr-resnet-50", "pipeline_tag": "object-detection", "model-index": [{"name": "detr-resnet-50_fine_tuned_nls_chapbooks", "results": []}]}
transformers pytorch tensorboard safetensors detr object-detection generated_from_trainer dataset:biglam/nls_chapbook_illustrations base_model:facebook/detr-resnet-50 license:apache-2.0 endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #detr #object-detection #generated_from_trainer #dataset-biglam/nls_chapbook_illustrations #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #has_space #region-us 




# detr-resnet-50_fine_tuned_nls_chapbooks

This model is a fine-tuned version of 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. To do this, you should first load the model and feature extractor:



Then you can create a pipeline for object detection using the model. 



To use this to make predictions pass in an image (or a file-path/URL for the image):



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 

URL
URL
# detr-resnet-50_fine_tuned_nls_chapbooks

This model is a fine-tuned version of 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. To do this, you should first load the model and feature extractor:



Then you can create a pipeline for object detection using the model. 



To use this to make predictions pass in an image (or a file-path/URL for the image):



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 

URL
URL
TAGS
#transformers #pytorch #tensorboard #safetensors #detr #object-detection #generated_from_trainer #dataset-biglam/nls_chapbook_illustrations #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
 # detr-resnet-50_fine_tuned_nls_chapbooks

This model is a fine-tuned version of 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. To do this, you should first load the model and feature extractor:



Then you can create a pipeline for object detection using the model. 



To use this to make predictions pass in an image (or a file-path/URL for the image):



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 

URL
URL
passage: TAGS
#transformers #pytorch #tensorboard #safetensors #detr #object-detection #generated_from_trainer #dataset-biglam/nls_chapbook_illustrations #base_model-facebook/detr-resnet-50 #license-apache-2.0 #endpoints_compatible #has_space #region-us 
# detr-resnet-50_fine_tuned_nls_chapbooks

This model is a fine-tuned version of 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. To do this, you should first load the model and feature extractor:



Then you can create a pipeline for object detection using the model. 



To use this to make predictions pass in an image (or a file-path/URL for the image):



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 

URL
URL