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STRLite

STR-Lite is an ultra-lightweight scene text recognition model that combines Masked Autoencoder (MAE) pretraining with an autoregressive decoder for text generation. With only 6M parameters, it achieves competitive...

Fuentehuggingface.co/balaboom123/STRLite/tree/f937a4e180e3acd5fd03de8c3cbc0e27e1fb5c7c
TipoOtherVersiónCheckpoint bestCcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
63,3–69,3 MB
Versión
vCheckpoint best
Actualizado
2 nov 2026
Fuente
Hugging Face

Descripción de la fuente

STR-Lite

STR-Lite is an ultra-lightweight scene text recognition model that combines Masked Autoencoder (MAE) pretraining with an autoregressive decoder for text generation. With only 6M parameters, it achieves competitive accuracy while remaining highly efficient for real-world deployment.

Model Architecture

ComponentDetails
BackboneViT-Tiny (embed=192, depth=12, heads=12)
Decoder1-layer autoregressive transformer (embed=192, heads=12)
Input size32 × 128 (H × W)
Patch size4 × 8
Parameters~6M
Precisionbfloat16

Training

Stage 1 — MAE Pretraining

  • Dataset: U14M-Unlabeled
  • Epochs: 40

Stage 2 — Fine-tuning

  • Dataset: U14M-L-Filtered
  • Epochs: 20, Batch: 256, LR: 1e-3, Weight decay: 0.01

Checkpoints

ModelDescriptionEpochsAccDownload
MAE ViT-TinyPretrained encoder only40—pretrain/checkpoint-last.pth
STRLiteFull fine-tuned model2093.82%finetune/checkpoint-best.pth

Results

Common STR Benchmarks

Subsetw/ pretrainw/o pretrain
CUTE8095.8394.79
IC1396.8596.50
IC1586.8086.25
IIIT5k96.9796.47
SVT95.3694.90
SVTP92.4089.77
Weighted avg.93.8293.12

U14M Benchmarks

Subsetw/ pretrainw/o pretrain
artistic67.7862.11
contextless78.9577.43
curve82.1978.97
general81.0779.96
multi oriented82.9178.57
multi words76.7274.31
salient78.1775.33
Weighted avg.81.0379.88

Usage

Download and evaluate:

git clone https://github.com/balaboom123/STR-Lite
cd STR-Lite

# Download checkpoint
from huggingface_hub import hf_hub_download
path = hf_hub_download("balaboom123/STRLite", "finetune/checkpoint-best.pth")

# Evaluate
python eval.py \
  resume=$path \
  test_data_path='[/path/to/lmdb_test]'

Fine-tune from MAE pretrained weights:

path = hf_hub_download("balaboom123/STRLite", "pretrain/checkpoint-last.pth")

python main_finetune.py \
  train_data_path='[/path/to/lmdb_train]' \
  val_data_path='[/path/to/lmdb_val]' \
  pretrained_mae=$path

See the GitHub repo for full installation and dataset preparation instructions.

Fuentes

1 fuente
Hugging FacePrincipalVersión f937a4e180e3acd5fd03de8c3cbc0e27e1fb5c7c
huggingface.co/balaboom123/STRLite/tree/f937a4e180e3acd5fd03de8c3cbc0e27e1fb5c7c

Verificado 2 nov

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Artefactos del modelo

2 artefactos

finetune/checkpoint-best.pth

pth · 69,3 MB · SHA-256 2d509b5d8635…95f4 · Hugging Face

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pretrain/checkpoint-last.pth

pth · 63,3 MB · SHA-256 41234d486ae4…cdde · Hugging Face

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Extractos de fuentes

2 extractos
Readme20 agohuggingface.co/balaboom123/STRLite

--- license: mit language: - en tags: - scene-text-recognition - ocr - vision-transformer - mae - image-to-text - pytorch library_name: pytorch --- # STR-Lite STR-Lite is an ultra-lightweight scene text recognition model that combines **Masked Autoencoder (MAE) pretraining** with an **autoregressive decoder** for text generation. With only **6M parameters**, it achieves competitive accuracy while remaining highly efficient for real-world deployment. - **GitHub:** [balaboom123/STR-Lite](https://github.com/balaboom123/STR-Lite) - **Author:** Kuanwei Chen - **License:** MIT ## Model Architecture | Component | Details | | --------- | ------- | | Backbone | ViT-Tiny (embed=192, depth=12, heads=12) | | Decoder | 1-layer autoregressive transformer (embed=192, heads=12) | | Input size | 32 × 128 (H × W) | | Patch size | 4 × 8 | | Parameters | ~6M | | Precision | bfloat16 | ## Training **Stage 1 — MAE Pretraining** - Dataset: U14M-Unlabeled - Epochs: 40 **Stage 2 — Fine-tuning** - Dataset: U14M-L-Filtered - Epochs: 20, Batch: 256, LR: 1e-3, Weight decay: 0.01 ## Checkpoints | Model | Description | Epochs | Acc | Download | | ----- | ----------- | :----: | :-: | :------: | | MAE ViT-Tiny | Pretrained encoder only | 40 | — | [pretrain/checkpoint-last.pth](https://huggingface.co/balaboom123/STRLite/resolve/main/pretrain/checkpoint-last.pth) | | STRLite | Full fine-tuned model | 20 | 93.82% | [finetune/checkpoint-best.pth](https://huggingface.co/balaboom123/STRLite/resolve/main/finetune/checkpoint-best.pth) | ## Results **Common STR Benchmarks** | Subset | w/ pretrain | w/o pretrain | | ------ | :---------: | :----------: | | CUTE80 | 95.83 | 94.79 | | IC13 | 96.85 | 96.50 | | IC15 | 86.80 | 86.25 | | IIIT5k | 96.97 | 96.47 | | SVT | 95.36 | 94.90 | | SVTP | 92.40 | 89.77 | | **Weighted avg.** | **93.82** | **93.12** | **U14M Benchmarks** | Subset | w/ pretrain | w/o pretrain | | --------------- | :---------: | :----------: | | artistic | 67.78 | 62.11 | | contextless | 78.95 | 77.43 | | curve | 82.19 | 78.97 | | general | 81.07 | 79.96 | | multi oriented | 82.91 | 78.57 | | multi words | 76.72 | 74.31 | | salient | 78.17 | 75.33 | | **Weighted avg.** | **81.03** | **79.88** | ## Usage **Download and evaluate:** ```bash git clone https://github.com/balaboom123/STR-Lite cd STR-Lite # Download checkpoint from huggingface_hub import hf_hub_download pat...

Note20 agohuggingface.co/balaboom123/STRLite

Source context: 0 downloads · 0 likes · Pipeline image-to-text · Library pytorch · Repo balaboom123/STRLite