updating readme

README.md

@@ -49,14 +49,14 @@ The model was trained with a resolution of **14.4 microns in XY** and **16 micro
 
 
 ```
-python large_scale_mask_prediction.py --input_path path/to/your/image.zarr
+python large_scale_mask_prediction.py --image_path path/to/your/image.zarr --model_path path/to/your/model.safetensors
 ```
 
 ## Input Parameters
 | Parameter | Description |
 |-----------|-------------|
 | `image_path` | Path to the OME-Zarr dataset (local or S3). Example: `s3://bucket/SmartSPIM_Dataset/Ex_639_Em_667.zarr` |
-| `model_path` | Path to the trained segmentation model. |
+| `model_path` | Path to the trained segmentation model. It can point to a .cpkt or .safetensors file. |
 | `output_folder` | Directory where results will be saved. |
 | `target_size_mb` | Memory allocation target for large-scale predictions. |
 | `n_workers` | Number of workers for PyTorch DataLoader. |

model.json

@@ -39,7 +39,7 @@
       "notes": null
    },
    "intended_use": "2D tissue segmentation from SmartSPIM datasets",
-   "limitations": "Only trained on 639 channel using a resolution",
+   "limitations": "Only trained on 639 channel",
    "pretrained_source_url": null,
    "training": [
       {