MonkeyOCRv2_det_dptext

MonkeyOCRv2 Detection (DPText-DETR)

This repository provides the DPText-DETR text detection experiments from the MonkeyOCRv2 paper. The visual encoder from MonkeyOCRv2-AS (ViTAEv2-S, 21M parameters) is integrated into DPText-DETR as a drop-in detectron2 backbone. The last three ViTAEv2 stages (strides 8/16/32) are exposed as res3–res5 and feed the standard deformable-DETR input projections, so the transformer encoder/decoder and the detection head are unchanged.

Training and evaluation follow the official DPText-DETR protocols on Total-Text, CTW1500, ICDAR19-ArT, Rotated Total-Text and Inverse-Text.

Models and Results

For each benchmark, three visual backbones are compared under identical settings: the original ImageNet-pretrained ResNet-50, the text-specific oCLIP ResNet-50, and MonkeyOCRv2. MonkeyOCRv2 consistently improves F-score across all five benchmarks.

All models are trained directly on the target dataset (no SynthText/MLT pre-training), for 200k iterations with a total batch size of 8, using the positional label form and the rotated training images released with DPText-DETR (*_poly_train_rotate_pos).

Total-Text

CTW1500

ICDAR19-ArT

Rotated Total-Text

Inverse-Text

Rotated Total-Text and Inverse-Text are test-only benchmarks: they reuse the Total-Text model above and only change DATASETS.TEST.

Checkpoints

Download the checkpoints from HB16888/MonkeyOCRv2_det_dptext (HuggingFace) or WangXinhan/MonkeyOCRv2_det_dptext (ModelScope):

# run from this add-on directory; ./DPText-DETR is the repository root created
# by install.sh
# HuggingFace
hf download HB16888/MonkeyOCRv2_det_dptext --include "*.pth" --local-dir ./DPText-DETR/model_weight
# ModelScope
modelscope download --model WangXinhan/MonkeyOCRv2_det_dptext --local_dir ./DPText-DETR/model_weight

Environment

The reproduced environment uses Python 3.11, PyTorch 2.9.0, CUDA 12.8, torchvision 0.24.0, detectron2 0.6, NumPy 2.4.4, Transformers 4.57.1 and safetensors 0.7.0. The oCLIP baseline additionally needs MMOCR 1.0.1 (MMEngine 0.10.7, MMCV 2.0.1, MMDet 3.1.0). All models were trained on 8 GPUs (NVIDIA GeForce RTX 3090) with SOLVER.IMS_PER_BATCH: 8 for 200k iterations.

Installation

The code for these checkpoints is the detection/DPText-DETR add-on of MonkeyOCRv2, which sits on top of the official DPText-DETR release. Run it from that add-on directory (MonkeyOCRv2/detection/DPText-DETR):

bash install.sh            # clones DPText-DETR into ./DPText-DETR and patches it

install.sh creates a nested checkout, ./DPText-DETR, which is the DPText-DETR root referred to throughout this README:

MonkeyOCRv2/detection/DPText-DETR/     # this add-on directory
├── install.sh
├── configs/  patch/  tools/           # the add-on files, copied into ./DPText-DETR
└── DPText-DETR/                       # <- DPText-DETR root, created by install.sh
    ├── adet/  configs/  tools/
    ├── pretrained/monkeyocrv2_as/     # MonkeyOCRv2-AS visual encoder
    ├── ckpts/                         # ResNet-50 / oCLIP init weights
    ├── model_weight/                  # released checkpoints
    ├── datasets/                      # the benchmarks
    └── output/                        # training / evaluation output

Pretrained Backbones

cd DPText-DETR             # the DPText-DETR root created by install.sh

# MonkeyOCRv2-AS visual encoder (for the MonkeyOCRv2 rows)
hf download zenosai/MonkeyOCRv2-AS --local-dir ./pretrained/monkeyocrv2_as

# ImageNet ResNet-50 (for the baseline rows) - from the official DPText-DETR /
# AdelaiDet instructions
mkdir -p ckpts
wget -O ckpts/R-50.pkl https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-50.pkl

# oCLIP ResNet-50 (for the oCLIP rows)
wget -O ckpts/resnet50-oclip-7ba0c533.pth \
  https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth

Datasets

Download Total-Text (including rotated images), CTW1500 (including rotated images), ICDAR19-ArT (including rotated images), Inverse-Text, the polygon json files and the evaluation ground-truths from the official DPText-DETR data preparation links, and organize them under the DPText-DETR root as:

datasets/
├── totaltext/
│   ├── train_images_rotate/
│   ├── test_images_rotate/
│   ├── train_poly_rotate_pos.json
│   ├── test_poly.json
│   └── test_poly_rotate.json
├── ctw1500/
│   ├── train_images_rotate/
│   ├── test_images/
│   ├── train_poly_rotate_pos.json
│   └── test_poly.json
├── art/
│   ├── train_images_rotate/
│   ├── test_images/
│   ├── train_poly_rotate_pos.json
│   └── test_poly.json
├── inversetext/
│   ├── test_images/
│   └── test_poly.json
└── evaluation/
    ├── gt_totaltext.zip
    ├── gt_totaltext_rotate.zip
    ├── gt_ctw1500.zip
    └── gt_inversetext.zip

Training

All training and evaluation commands are run from the DPText-DETR root (detection/DPText-DETR/DPText-DETR), on 8 GPUs.

# ---------- Total-Text (also used for Rot.Total-Text and Inverse-Text) ----------
python tools/train_net.py --config-file configs/DPText_DETR/TotalText_Direct_Rotate/R_50_poly.yaml             --num-gpus 8
python tools/train_net.py --config-file configs/DPText_DETR/TotalText_Direct_Rotate/R_50_oclip_poly_lr1e4.yaml --num-gpus 8
python tools/train_net.py --config-file configs/DPText_DETR/TotalText_Direct_Rotate/mkv2vitae_align.yaml       --num-gpus 8

# ---------- CTW1500 ----------
python tools/train_net.py --config-file configs/DPText_DETR/CTW_Rotate/R_50_poly.yaml             --num-gpus 8
python tools/train_net.py --config-file configs/DPText_DETR/CTW_Rotate/R_50_oclip_poly_lr1e4.yaml --num-gpus 8
python tools/train_net.py --config-file configs/DPText_DETR/CTW_Rotate/mkv2vitae_align.yaml       --num-gpus 8

# ---------- ICDAR19-ArT ----------
python tools/train_net.py --config-file configs/DPText_DETR/ArT_Rotate/R_50_poly.yaml             --num-gpus 8
python tools/train_net.py --config-file configs/DPText_DETR/ArT_Rotate/R_50_oclip_poly_lr1e4.yaml --num-gpus 8
python tools/train_net.py --config-file configs/DPText_DETR/ArT_Rotate/mkv2vitae_align.yaml       --num-gpus 8

Evaluation

Each config already carries the MODEL.TRANSFORMER.INFERENCE_TH_TEST value that reproduces the corresponding row of the tables above, so evaluating on the dataset a model was trained on needs no extra flags:

# Total-Text
python tools/train_net.py --num-gpus 8 --eval-only \
  --config-file configs/DPText_DETR/TotalText_Direct_Rotate/mkv2vitae_align.yaml \
  MODEL.WEIGHTS model_weight/dptext_mkv2vitae_totaltext.pth

# CTW1500
python tools/train_net.py --num-gpus 8 --eval-only \
  --config-file configs/DPText_DETR/CTW_Rotate/mkv2vitae_align.yaml \
  MODEL.WEIGHTS model_weight/dptext_mkv2vitae_ctw1500.pth

Evaluation prints precision / recall / hmean on the copypaste: line, matching the tables above.

Rotated Total-Text and Inverse-Text

These reuse the Total-Text checkpoints and override the test set and the threshold:

# Rotated Total-Text
python tools/train_net.py --num-gpus 8 --eval-only \
  --config-file configs/DPText_DETR/TotalText_Direct_Rotate/mkv2vitae_align.yaml \
  MODEL.WEIGHTS model_weight/dptext_mkv2vitae_totaltext.pth \
  MODEL.TRANSFORMER.INFERENCE_TH_TEST 0.395 \
  DATASETS.TEST '("totaltext_poly_test_rotate",)'

# Inverse-Text
python tools/train_net.py --num-gpus 8 --eval-only \
  --config-file configs/DPText_DETR/TotalText_Direct_Rotate/mkv2vitae_align.yaml \
  MODEL.WEIGHTS model_weight/dptext_mkv2vitae_totaltext.pth \
  MODEL.TRANSFORMER.INFERENCE_TH_TEST 0.37 \
  DATASETS.TEST '("inversetext_test",)'

The full set of thresholds used for the tables:

tools/search_th.py sweeps INFERENCE_TH_TEST for a trained model and reports the best F-score:

python tools/search_th.py \
  --output-dir output/mkv2vitae_align/totaltext/direct_rotate \
  --test-dataset totaltext_poly_test --start 0.1 --end 0.5 --num-gpus 8

ICDAR19-ArT

ArT has no public test ground-truth. Evaluating an ArT config writes <OUTPUT_DIR>/inference/art_submit.json, which has to be uploaded to the ICDAR19-ArT evaluation server to obtain the P / R / F numbers reported above:

python tools/train_net.py --num-gpus 8 --eval-only \
  --config-file configs/DPText_DETR/ArT_Rotate/mkv2vitae_align.yaml \
  MODEL.WEIGHTS model_weight/dptext_mkv2vitae_art.pth

Acknowledgements

This project builds on DPText-DETR, AdelaiDet, detectron2, MMOCR, oCLIP, and MonkeyOCRv2.

License

The DPText-DETR / AdelaiDet sources this add-on patches are released for non-commercial use only (see the DPText-DETR license); the same restriction applies to the add-on and to the released checkpoints.