README.md

---
library_name: transformers
tags: []
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Direct Use

```python
from transformers import AutoImageProcessor
from transformers.models.detr import DetrForSegmentation

img_proc = AutoImageProcessor.from_pretrained(
    "ArkeaIAF/detr-layout-detection"
)
model = DetrForSegmentation.from_pretrained(
    "ArkeaIAF/detr-layout-detection"
)

with torch.inference_mode():
    input_ids = img_proc(img, return_tensors='pt')
    output = model(**input_ids)

threshold=0.4

segmentation_mask = img_proc.post_process_segmentation(
    out_seg,
    threshold=threshold,
    target_sizes=[img.size[::-1]]
)

bbox_pred = img_proc.post_process_object_detection(
    output,
    threshold=threshold,
    target_sizes=[img.size[::-1]]
)
```

### Citation

```
@online{DeDetrLay,
  AUTHOR = {Cyrile Delestre},
  URL = {https://huggingface.co/cmarkea/detr-base-layout-detection},
  YEAR = {2024},
  KEYWORDS = {Image Processing ; Transformers ; Layout},
}
```