Image-to-Text
MLX
Safetensors
mlx-weights
paddlepaddle-ocr
ppocrv5
ppocrv6
ppdoclayoutv3
pp-structure
apple-silicon
Instructions to use plaincompute/ppocr-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use plaincompute/ppocr-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir ppocr-mlx plaincompute/ppocr-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 885 Bytes
1e1b9bd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | Global:
model_name: PP-OCRv6_small_det
Hpi:
backend_configs:
paddle_infer:
trt_dynamic_shapes: &id001
x:
- - 1
- 3
- 32
- 32
- - 1
- 3
- 736
- 736
- - 1
- 3
- 4000
- 4000
tensorrt:
dynamic_shapes: *id001
PostProcess:
box_thresh: 0.45
max_candidates: 3000
name: DBPostProcess
thresh: 0.2
unclip_ratio: 1.4
PreProcess:
transform_ops:
- DecodeImage:
channel_first: false
img_mode: BGR
- DetLabelEncode: null
- DetResizeForTest: null
- NormalizeImage:
mean:
- 0.485
- 0.456
- 0.406
order: hwc
scale: 1./255.
std:
- 0.229
- 0.224
- 0.225
- ToCHWImage: null
- KeepKeys:
keep_keys:
- image
- shape
- polys
- ignore_tags
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