db_resnet50_20240430-025744

<p align="center"> <img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%"> </p>

Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch

Task: detection

https://github.com/mindee/doctr

Example usage:

>>> from doctr.io import DocumentFile
>>> from doctr.models import ocr_predictor, from_hub

>>> img = DocumentFile.from_images(['<image_path>'])
>>> # Load your model from the hub
>>> model = from_hub('mindee/my-model')

>>> # Pass it to the predictor
>>> # If your model is a recognition model:
>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
>>>                           reco_arch=model,
>>>                           pretrained=True)

>>> # If your model is a detection model:
>>> predictor = ocr_predictor(det_arch=model,
>>>                           reco_arch='crnn_mobilenet_v3_small',
>>>                           pretrained=True)

>>> # Get your predictions
>>> res = predictor(img)

Run Configuration

{ "train_path": "/workspace/donut_train/doctr/train/", "val_path": "/workspace/donut_train/doctr/val/", "arch": "db_resnet50", "name": null, "epochs": 5, "batch_size": 10, "device": 0, "save_interval_epoch": false, "input_size": 1024, "lr": 0.001, "weight_decay": 0, "workers": 16, "resume": null, "test_only": false, "freeze_backbone": false, "show_samples": false, "wb": false, "push_to_hub": true, "pretrained": false, "rotation": false, "eval_straight": false, "sched": "poly", "amp": false, "find_lr": false, "early_stop": false, "early_stop_epochs": 5, "early_stop_delta": 0.01 }