Update README.md

Author: EiffL

README.md

@@ -16,25 +16,20 @@ https://astroclip.streamlit.app/
 The training and evaluation code requires PyTorch 2.0. Additionally, an up-to-date eventlet is required for wandb. Note that the code has only been tested with the specified versions and also expects a Linux environment. To install the AstroCLIP package and its corresponding dependencies, please follow the code below. The install procedure is unfortunately a bit complex, execute the following cell. Do not restart kernel when prompted, instead run the cell below.
 
 ```bash
-# Base install of required utilities
-apt install python3-dev libcairo2-dev pkg-config
-
 # Setting up proper torch version
 pip install --upgrade pip
-pip install lightning[extra]==2.3.3 boto3==1.28.17
+pip install lightning[extra]==2.3.3 boto3==1.28.17 
 pip install --upgrade pycairo datasets pyarrow
 pip install --extra-index-url https://pypi.nvidia.com cuml-cu11
 pip install --extra-index-url https://download.pytorch.org/whl/cu117 torch==2.0.0+cu117
-pip install torchvision==0.15.0 torchmetrics==0.10.3 dotenv
-pip install numpy==1.26.4 --force-reinstall
+pip install torchvision==0.15.0 torchmetrics==0.10.3 dotenv numpy==1.26.4
 
 # Installing DiNOv2
 pip install omegaconf fvcore iopath
 pip install --no-deps git+https://github.com/facebookresearch/dinov2.git@2302b6bf46953431b969155307b9bed152754069
 
 # Installing AstroCLIP
-pip install astropy datasets huggingface_hub jaxtyping wandb networkx pyvis
-pip uninstall -y transformers
+pip install astropy datasets huggingface_hub jaxtyping wandb
 pip install --no-deps git+https://github.com/PolymathicAI/AstroCLIP.git
 ```
 **NOTE** The package provides the three shortcuts: `astroclip_trainer` and `spectrum_trainer`, which link to `astroclip/trainer.py`, and `image_trainer`, which links to `astroclip/astrodino/trainer.py`, as long as it is installed. The shortcuts are defined in the `project.scripts` section of the `pyproject.toml` file.