Downloads · 30 days
0
CLPRNet-PARSeq
theakhilshukla/CLPRNet-PARSeq
CLPRNet-PARSeq is a machine learning model from theakhilshukla. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This is a modified version of CLPRNet where the original CNN-based recognition branch has been replaced with PARSeq Tiny (Scene Text Recognition with Permuted Autoregressive Sequence Models, ECCV 2022).
Access
Public
Updated May 8, 2026
Repo size
—
Likes
0
Public
Repo makeup
Click a slice to open those files.
.py55.9 KB · 90%
At a glance
- Access
- Public
- Created
- May 8, 2026
- Updated
- May 8, 2026
- SHA
- bef897d6
Model card
From the Hugging Face model README
CLPRNet with PARSeq Tiny OCR Backbone
This is a modified version of CLPRNet where the original CNN-based recognition branch has been replaced with PARSeq Tiny (Scene Text Recognition with Permuted Autoregressive Sequence Models, ECCV 2022).
Architecture
Input Image (1024×1024)
│
▼
┌─────────────────────────────────┐
│ Shared FPN Backbone (unchanged) │
│ BasicBlock stack → multi-scale │
│ features → FPN upsampling │
└──────┬──────────────┬────────────┘
│ │
▼ ▼
┌──────────────┐ ┌────────────┐
│ at_head (1ch)│ │ Detection │
│ LP attention │ │ SEBasicBlock│
└──────────────┘ │ → 5ch head │
└─────┬──────┘
│
▼
Bounding Boxes (NMS)
│
▼
┌─────────────────┐
│ Plate Cropping │
│ (grid_sample) │
│ → (32, 128) RGB │
└────────┬────────┘
│
▼
┌─────────────────┐
│ PARSeq Tiny │
│ ViT Encoder │
│ (192d, 12 layers)│
│ + Transformer │
│ Decoder (1 layer)│
└────────┬────────┘
│
▼
Character Logits
(B, 9, 74)
What Changed vs Original CLPRNet
| Component | Original | Modified |
|---|---|---|
| Recognition backbone | 4× SEBasicBlock CNN | PARSeq Tiny (ViT) |
| Recognition head | Conv2d 256→73 (dense spatial) | Linear 192→74 (sequence) |
| Character attention | 8-channel learned spatial masks | Internal Transformer attention |
| at_head output | 9 channels (1 LP + 8 char) | 1 channel (LP only) |
| Recognition output | (B, 16, 16, 584) dense grid | (B, 9, 74) sequence logits |
| Decoding | Argmax per grid cell | Autoregressive/parallel sequence |
| Plate cropping | Not needed (attention-based) | Differentiable grid_sample |
Parameters
Total: 8,134,550 params
PARSeq Tiny: 6,007,178 params (recognition)
Detection: 2,127,372 params (backbone + FPN + detection head)
Files
| File | Purpose |
|---|---|
model_parseq.py | Main model with PARSeq Tiny integrated |
train_parseq.py | Training script (updated losses) |
inference_parseq.py | Inference script (two-stage) |
Usage
from model_parseq import create_clprnet_parseq
# Create model
model = create_clprnet_parseq(max_label_length=8)
# Training forward pass (with GT boxes + labels)
y_det, y_rec, at_lp, plate_counts = model(
images, # (B, 3, 1024, 1024)
boxes_lurd=gt_boxes, # list of (N_i, 4) tensors [l,t,r,b]
plate_labels=gt_labels # list of plate strings
)
# Inference (detection only)
y_det, _, at_lp, _ = model(images)
# Full inference (detect + recognize)
plates, confs = model.recognize_plates(images, detected_boxes)
PARSeq Tiny Specs
Based on DeiT-Ti configuration from the PARSeq paper:
| Parameter | Value |
|---|---|
| embed_dim | 192 |
| Encoder heads | 3 |
| Encoder depth | 12 |
| Decoder heads | 6 |
| Decoder depth | 1 |
| MLP ratio | 4 |
| Patch size | (4, 8) |
| Input size | (32, 128) |
| Max label length | 8 |
| Charset | 73 Chinese LP chars + EOS |
Requirements
torch >= 2.0
torchvision
numpy
opencv-python
Pillow
Training
Uses the same CCPD + CRPD datasets as the original CLPRNet. See train_parseq.py.
Credits
- Original CLPRNet: https://github.com/wulb97/CLPRNet
- PARSeq: https://github.com/baudm/parseq (ECCV 2022)
Generated by ML Intern
This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.
- Try ML Intern: https://smolagents-ml-intern.hf.space
- Source code: https://github.com/huggingface/ml-intern
- Created
- May 8, 2026
- Updated
- May 8, 2026