More passion, more energy, more footwork

README.md

@@ -9,6 +9,10 @@
 [![status](https://joss.theoj.org/papers/447561ee2de4709eddb704e18bee846f/status.svg)](https://joss.theoj.org/papers/447561ee2de4709eddb704e18bee846f)
 <a href="https://ascl.net/2203.027"><img src="https://img.shields.io/badge/ascl-2203.027-blue.svg?colorB=262255" alt="ascl:2203.027" /></a>
 
+---
+### :tada: Zoobot 2.0 is now available. We added bigger and better models with streamlined finetuning. :tada:
+---
+
 Zoobot classifies galaxy morphology with deep learning.
 <!-- At Galaxy Zoo, we use Zoobot to help our volunteers classify the galaxies in all our recent catalogues: GZ DECaLS, GZ DESI, GZ Rings and GZ Cosmic Dawn. -->
 
@@ -110,27 +114,21 @@ Pretrained models are listed [here](https://zoobot.readthedocs.io/en/latest/pret
 
 <a name="worked_examples"></a>
 
-PyTorch (recommended):
-
 - [pytorch/examples/finetuning/finetune_binary_classification.py](https://github.com/mwalmsley/zoobot/blob/main/zoobot/pytorch/examples/finetuning/finetune_binary_classification.py)
 - [pytorch/examples/finetuning/finetune_counts_full_tree.py](https://github.com/mwalmsley/zoobot/blob/main/zoobot/pytorch/examples/finetuning/finetune_counts_full_tree.py)
 - [pytorch/examples/representations/get_representations.py](https://github.com/mwalmsley/zoobot/blob/main/zoobot/pytorch/examples/representations/get_representations.py)
 - [pytorch/examples/train_model_on_catalog.py](https://github.com/mwalmsley/zoobot/blob/main/zoobot/pytorch/examples/train_model_on_catalog.py) (only necessary to train from scratch)
 
 There is more explanation and an API reference on the [docs](https://zoobot.readthedocs.io/).
 
-I also [include](https://github.com/mwalmsley/zoobot/blob/main/benchmarks) the scripts used to create and benchmark our pretrained models. Many pretrained models are available [already](https://zoobot.readthedocs.io/en/latest/data_notes.html), but if you need one trained on e.g. different input image sizes or with a specific architecture, I can probably make it for you.
-
-When trained with a decision tree head (ZoobotTree, FinetuneableZoobotTree), Zoobot can learn from volunteer labels of varying confidence and predict posteriors for what the typical volunteer might say. Specifically, this Zoobot mode predicts the parameters for distributions, not simple class labels! For a demonstration of how to interpret these predictions, see the [gz_decals_data_release_analysis_demo.ipynb](https://github.com/mwalmsley/zoobot/blob/main/gz_decals_data_release_analysis_demo.ipynb).
-
 
 ### (Optional) Install PyTorch with CUDA
 
 <a name="install_cuda"></a>
 
 *If you're not using a GPU, skip this step. Use the pytorch-cpu option in the section below.*
 
-Install PyTorch 2.1.0 or Tensorflow 2.10.0 and compatible CUDA drivers. I highly recommend using [conda](https://docs.conda.io/en/latest/miniconda.html) to do this. Conda will handle both creating a new virtual environment (`conda create`) and installing CUDA (`cudatoolkit`, `cudnn`)
+Install PyTorch 2.1.0 and compatible CUDA drivers. I highly recommend using [conda](https://docs.conda.io/en/latest/miniconda.html) to do this. Conda will handle both creating a new virtual environment (`conda create`) and installing CUDA (`cudatoolkit`, `cudnn`)
 
 CUDA 12.1 for PyTorch 2.1.0:
 
@@ -157,11 +155,11 @@ Contributions are very welcome and will be credited in any future work. Please g
 
 ### Benchmarks and Replication - Training from Scratch
 
-The [benchmarks](https://github.com/mwalmsley/zoobot/blob/main/benchmarks) folder contains slurm and Python scripts to train Zoobot from scratch. We use these scripts to make sure new code versions work well, and that TensorFlow and PyTorch achieve similar performance.
+The [benchmarks](https://github.com/mwalmsley/zoobot/blob/main/benchmarks) folder contains slurm and Python scripts to train Zoobot 1.0 from scratch. 
 
 Training Zoobot using the GZ DECaLS dataset option will create models very similar to those used for the GZ DECaLS catalogue and shared with the early versions of this repo. The GZ DESI Zoobot model is trained on additional data (GZD-1, GZD-2), as the GZ Evo Zoobot model (GZD-1/2/5, Hubble, Candels, GZ2).
 
-**Pretraining is becoming increasingly complex and is now partially refactored out to a separate repository. We are gradually migrating this `zoobot` repository to focus on finetuning.**
+*Pretraining is becoming increasingly complex and is now partially refactored out to a separate repository. We are gradually migrating this `zoobot` repository to focus on finetuning.*
 
 ### Citing