[doc] add INSTALL.md as an installation FAQ page

README.md

@@ -14,7 +14,7 @@
 
 ## Installation
 
-Please install SAM 2 on a GPU machine using:
+SAM 2 needs to be installed first before use. The code requires `python>=3.10`, as well as `torch>=2.3.1` and `torchvision>=0.18.1`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. You can install SAM 2 on a GPU machine using:
 
 ```bash
 git clone https://github.com/facebookresearch/segment-anything-2.git
@@ -28,6 +28,12 @@ To use the SAM 2 predictor and run the example notebooks, `jupyter` and `matplot
 pip install -e ".[demo]"
 ```
 
+Note:
+1. It's recommended to create a new Python environment for this installation and install PyTorch 2.3.1 (or higher) via `pip` following https://pytorch.org/. If you have a PyTorch version lower than 2.3.1 in your current environment, the installation command above will try to upgrade it to the latest PyTorch version using `pip`.
+2. The step above requires compiling a custom CUDA kernel with the `nvcc` compiler. If it isn't already available on your machine, please install the [CUDA toolkits](https://developer.nvidia.com/cuda-toolkit-archive) with a version that matches your PyTorch CUDA version.
+
+Please see [`INSTALL.md`](./INSTALL.md) for FAQs on potential issues and solutions.
+
 ## Getting Started
 
 ### Download Checkpoints
@@ -86,7 +92,7 @@ with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
     state = predictor.init_state(<your_video>)
 
     # add new prompts and instantly get the output on the same frame
-    frame_idx, object_ids, masks = predictor.add_new_points(state, <your prompts>):
+    frame_idx, object_ids, masks = predictor.add_new_points(state, <your_prompts>):
 
     # propagate the prompts to get masklets throughout the video
     for frame_idx, object_ids, masks in predictor.propagate_in_video(state):