diff --git a/docs/faq.rst b/docs/faq.rst index 7ef1e58d..d650a0e8 100644 --- a/docs/faq.rst +++ b/docs/faq.rst @@ -66,3 +66,49 @@ FAQ using `torch.set_num_threads `_ or through the environment variables ``OMP_NUM_THREADS`` or ``MKL_NUM_THREADS`` as described `here `_. + + +**Q: How does HITL work?** + + In cellpose HITL training always starts from a pretrained model but incorporates more training + data with each iteration. To start, only a single image is used as training data. + After an iteration another image is included in the training data. Since there is more + training data, the model should be more accurate on subsequent images. + + The goal of HITL training is to produce a model that is finetuned on your data and also generalist + enough to segment new images not in the training set. One of the problems with annotating + images is that it can be time-consuming to annotate your images to produce a finetuned model. + Cellpose also circumvents this tedium by using the already generalist-trained model to predict + your image segmentation. This prediction will be better than nothing, and it will get some + segmentation masks correct. That is helpful becuase you can accept the correct masks, and add + or edit the incorrect ones. Now you have a new image that can be used for training a new finetuned + model. This new finetuned model can then also predict segmentation for an image in your dataset, + and, since it's finetuned on your data, will do somewhat better than the 'base' cellpose model. + You can repeat these steps, (predict using the latest model, annotate the predictions, train, + and predict again) until you have a model that performs well enough on your data. + + +**Q: What is a 'model'?** + + A model is the neural network architecture and parameters (fitted numbers) in that architecture. + The CPSAM model we distribute is a 'model', and you can have another 'model' made from finetuning + on your data. These models are similar becuase they have the same architecture, but distinct + because they have different weights. + + +**Q: How can I do HITL without the GUI? (I don't have GPU hardware on my machine, but I want to use +colab/a cluster)** + + You can do the following steps: + + 1. Load the images onto the remote machine. + + 2. Use a script to segment the image using the pretrained model. + + 3. Download the segmented image masks and annotate it with the cellpose GUI. + + 4. Load the annotated masks onto the remote machine and train a model with all the images in the folder (only 1 at first) + + 5. Evaluate the trained model on the next image. + + 6. Repeat 3-5 until you have a working fine-tuned model. \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index 4239670f..2e115623 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -11,7 +11,7 @@ can install it as ``pip install cellpose[gui]``. - run Cellpose-SAM in the cloud (no install) at `Hugging Face `_. - `paper `_ on biorxiv -- talk +- `talk `_ diff --git a/docs/installation.rst b/docs/installation.rst index d49239ce..066b09ee 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -62,6 +62,7 @@ Common issues If you receive an issue with Qt "xcb", you may need to install xcb libraries, e.g.: :: + sudo apt install libxcb-cursor0 sudo apt install libxcb-xinerama0 @@ -90,6 +91,7 @@ If you are having other issues with the graphical interface and QT, see some adv If you have errors related to OpenMP and libiomp5, then try :: + conda install nomkl If you receive an error associated with **matplotlib**, try upgrading diff --git a/docs/settings.rst b/docs/settings.rst index eb09c62d..0acebfca 100644 --- a/docs/settings.rst +++ b/docs/settings.rst @@ -8,7 +8,7 @@ See the :ref:`cpmclass` for all run options. .. warning:: Cellpose 3 used ``models.Cellpose`` class which has been removed in Cellpose 4. Users should - now only use the ``models.CellposeModel``` class. + now only use the ``models.CellposeModel`` class. Here is an example of calling the ``CellposeModel`` class and running a list of images for reference: