update README: add model_type to sam registry

README.md

@@ -44,7 +44,8 @@ First download a [model checkpoint](#model-checkpoints). Then the model can be u
 
 ```
 from segment_anything import build_sam, SamPredictor 
-predictor = SamPredictor(build_sam(checkpoint="</path/to/model.pth>"))
+sam = sam_model_registry["<model_type>"](checkpoint="<path/to/checkpoint>")
+predictor = SamPredictor(sam)
 predictor.set_image(<your_image>)
 masks, _, _ = predictor.predict(<input_prompts>)
 ```
@@ -53,14 +54,15 @@ or generate masks for an entire image:
 
 ```
 from segment_anything import build_sam, SamAutomaticMaskGenerator
-mask_generator = SamAutomaticMaskGenerator(build_sam(checkpoint="</path/to/model.pth>"))
+sam = sam_model_registry["<model_type>"](checkpoint="<path/to/checkpoint>")
+mask_generator = SamAutomaticMaskGenerator(sam)
 masks = mask_generator.generate(<your_image>)
 ```
 
 Additionally, masks can be generated for images from the command line:
 
 ```
-python scripts/amg.py --checkpoint <path/to/sam/checkpoint> --input <image_or_folder> --output <output_directory>
+python scripts/amg.py --checkpoint <path/to/checkpoint> --input <image_or_folder> --output <path/to/output>
 ```
 
 See the examples notebooks on [using SAM with prompts](/notebooks/predictor_example.ipynb) and [automatically generating masks](/notebooks/automatic_mask_generator_example.ipynb) for more details.
@@ -85,9 +87,9 @@ See the [example notebook](https://github.com/facebookresearch/segment-anything/
 Three model versions of the model are available with different backbone sizes. These models can be instantiated by running 
 ```
 from segment_anything import sam_model_registry
-sam = sam_model_registry["<name>"](checkpoint="<path/to/checkpoint>")
+sam = sam_model_registry["<model_type>"](checkpoint="<path/to/checkpoint>")
 ```
-Click the links below to download the checkpoint for the corresponding model name. The default model in bold can also be instantiated with `build_sam`, as in the examples in [Getting Started](#getting-started).
+Click the links below to download the checkpoint for the corresponding model type. The default model in bold can also be instantiated with `build_sam`, as in the examples in [Getting Started](#getting-started).
 
 * **`default` or `vit_h`: [ViT-H SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth)**
 * `vit_l`: [ViT-L SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth)