---
library_name: transformers
language:
- en
- es
base_model:
- facebook/detr-resnet-101
---

# Model Card for Model ID

DETR allows to detect and generate the bounding boxes for handwritten and cursive text. This model was finetuned using the base model facebook/detr-resnet-101.
The dataset used is still under development and possible released in future versions.
Mainly, the model detects spanish text.
Note: The default value of generated bounding boxes was used (num_queries: 100). Modifying this value when using the model could lead to unexpected behavior.

## Model Details

### Model Description

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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:** Rodrigo Alvarez
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** Text Detection / Bounding Box generation
- **Language(s) (NLP):** en (default), es-MX (finetuned)
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** facebook/detr-resnet-101

### Model Sources [optional]

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- **Repository:** [https://github.com/rodrigoalvarez-20/detr_trocr_handwritten_text/development](DETR TROCR Lab)
- **Paper [optional]:** *Work in progress*
- **Demo [optional]:** [https://github.com/rodrigoalvarez-20/detr_trocr_handwritten_text/blob/development/detr_lab.ipynb](Demo)

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

```python
from transformers import DetrForObjectDetection, DetrImageProcessor
import torch
import cv2
import supervision as sv
# User defined constants
MODEL_CHECKPOINT = "Rodr16020/detr_handwriten_cursive_text_detection"
DEVICE = "cuda"
CONFIDENCE_TRESHOLD = 0.5 # This parameter allows to filter the generated boxes with a confidence score >= to this value
IOU_TRESHOLD = 0.5
TEST_IMAGE = "demo.jpeg" # Path to the test image
#Load the model and preprocessor
img_proc = DetrImageProcessor.from_pretrained(MODEL_CHECKPOINT)
detr_model = DetrForObjectDetection.from_pretrained(
    pretrained_model_name_or_path=MODEL_CHECKPOINT,
    ignore_mismatched_sizes=True
).to(DEVICE)
# Get the pixel values of the image (matrix)
image = cv2.imread(TEST_IMAGE)
# inference
with torch.no_grad():
    # load image and predict
    inputs = img_proc(images=image, return_tensors='pt').to(DEVICE)
    outputs = detr_model(**inputs)
    # post-process
    # Resize the generated Bounding Boxes coords to the image original size
    target_sizes = torch.tensor([image.shape[:2]]).to(DEVICE)
    results = img_proc.post_process_object_detection(
        outputs=outputs, 
        threshold=CONFIDENCE_TRESHOLD, 
        target_sizes=target_sizes
    )[0]

# To extract all the generated bboxes
boxes = results["boxes"].tolist()[0]
# With supervision lib, use the generated coords to annotate the image and preview the boxes
box_annotator = sv.BoxAnnotator()
detections = sv.Detections.from_transformers(transformers_results=results).with_nms(threshold=0.1)
labels = [f"{confidence:.2f}" for _,_, confidence, class_id, _ in detections]
frame = box_annotator.annotate(scene=image.copy(), detections=detections, labels=labels)
sv.plot_image(frame, (16, 16))
```

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- Dataset Format: COCO
- Device: CUDA
- WEIGHT_DECAY = 3e-3
- CLIP_GRAD = 1e-4 #0.001
- BATCH_SIZE = 8
- ACC_BATCH = BATCH_SIZE * 4
- MODEL_LR = 5e-4 # In some articles, they set the value to 5e-4, but, in my case, it doesn't work, so I try with this and works "well"
- BB_LR = 5e-4 # Same as above
- MAX_EPOCHS = 300 # Use >= 50 . But it stops learning near the step 70

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

A simple and a tiny computer at CIC research lab.

When finetuning, the model and data used a total of 

#### Hardware

- ASRock-placa base Z370/OEM
- Gabinete Corsair 4000D Airflow
- Procesador Intel Core i7 i7-8700K
- Memoria RAM XPG Spectrix DDR4, 3200MHz, 16GB (x4)
- SSD Externo Western Digital WD My Passport, 1TB
- NVIDIA GeForce RTX 4090 24GB
- Corsair Serie RMX, RM1000x, 1000 W

#### Software

- transformers
- pytorch
- tensorboard
- cv2
- supervision

And possibly others

## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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