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docs: update readme

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myhloli
2024-07-13 20:05:53 +08:00
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@@ -94,9 +94,9 @@ Alternatively, for built-in high-precision model parsing capabilities, use:
```bash
pip install magic-pdf[full-cpu]
```
The high-precision models depend on detectron2, which requires a compiled installation.
If you need to compile it yourself, refer to https://github.com/facebookresearch/detectron2/issues/5114
Or directly use our pre-compiled wheel packages (limited to python 3.10):
The high-precision models depend on detectron2, which requires a compiled installation.
If you need to compile it yourself, refer to https://github.com/facebookresearch/detectron2/issues/5114
Or directly use our pre-compiled wheel packages (limited to python 3.10):
```bash
pip install detectron2 --extra-index-url https://myhloli.github.io/wheels/
```
@@ -104,7 +104,7 @@ pip install detectron2 --extra-index-url https://myhloli.github.io/wheels/
#### 2. Downloading model weights files
For detailed references, please see below[how_to_download_models](docs/how_to_download_models_en.md)
For detailed references, please see below [how_to_download_models](docs/how_to_download_models_en.md)
After downloading the model weights, move the 'models' directory to a directory on a larger disk space, preferably an SSD.
@@ -130,9 +130,9 @@ In magic-pdf.json, configure "models-dir" to point to the directory where the mo
```bash
magic-pdf pdf-command --pdf "pdf_path" --inside_model true
```
After the program has finished, you can find the generated markdown files under the directory "/tmp/magic-pdf".
You can find the corresponding xxx_model.json file in the markdown directory.
If you intend to do secondary development on the post-processing pipeline, you can use the command:
After the program has finished, you can find the generated markdown files under the directory "/tmp/magic-pdf".
You can find the corresponding xxx_model.json file in the markdown directory.
If you intend to do secondary development on the post-processing pipeline, you can use the command:
```bash
magic-pdf pdf-command --pdf "pdf_path" --model "model_json_path"
```
@@ -150,12 +150,12 @@ magic-pdf --help
##### CUDA
You need to install the corresponding PyTorch version according to your CUDA version.
You need to install the corresponding PyTorch version according to your CUDA version.
This example installs the CUDA 11.8 version.More information https://pytorch.org/get-started/locally/
```bash
# When using the GPU solution, you need to reinstall PyTorch for the corresponding CUDA version. This example installs the CUDA 11.8 version.
pip install --force-reinstall torch==2.3.1 torchvision==0.18.1 --index-url https://download.pytorch.org/whl/cu118
```
Also, you need to modify the value of "device-mode" in the configuration file magic-pdf.json.
Also, you need to modify the value of "device-mode" in the configuration file magic-pdf.json.
```json
{
"device-mode":"cuda"
@@ -164,9 +164,8 @@ Also, you need to modify the value of "device-mode" in the configuration file ma
##### MPS
For macOS users with M-series chip devices, you can use MPS for inference acceleration.
You also need to modify the value of "device-mode" in the configuration file magic-pdf.json.
For macOS users with M-series chip devices, you can use MPS for inference acceleration.
You also need to modify the value of "device-mode" in the configuration file magic-pdf.json.
```json
{
"device-mode":"mps"