Rodr16020/detr_handwriten_cursive_text_detection

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

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]

• 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

Uses

Direct Use

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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Recommendations

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

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

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

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
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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]

BibTeX:

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APA:

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Glossary [optional]

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Model Card Authors [optional]

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Model Card Contact

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