It’s not just the show method, for example the
to_fp16() callback will also fail, as it tries to call
to_float on the predictions and it doesn’t expect a list in there (only tensors). I feel either fastai needs to have some global support for the model outputting things which are not part of the actual predictions (similar like
n_inp is used to tell the model what part of the data is input and what target), or a callback would just store these things
EDIT: I posted before seeing sguggers reply, I’ll check out the loss function decode and check if I can get it to work this way
@muellerzr I finally got a working mAP evaluation for the output of your RetinaNet using this library, so I can soon start experimenting with dilated xresnet.
As a solution to the problems above (model output not matching prediction target) I have 2 ideas which I will try and code up today:
sizes should be stored in the RetinaNet class in both cases, and can be accessed by the loss function via a reference passed to the constructor.
And then either:
- Have a callback to do score thresholding and non max suppression in the
after_loss stage (modifying
- do score thresholding and non max suppression directly in the
model.forward and store the
raw_predictions in the model as well and access them via the reference in the loss function.
I think 1. is better as it should work with the
fp16 callback for example
I’ll share a notebook here when I’m done
- got an non-max suppression callback working
- using the object detection metrics library above I got fastai2 metrics for mean AP (Average Precision) and per class AP working (unfortunately the evaluation can be really slow if there are a lot of box detection and images)
- Testing with the dataset sample (2501 images total) in the notebook, after ca. 30 epochs I only get around an mAP of ~12, which is pretty bad. I think this is due to the small dataset size
show_results is also working (though it doesn’t visually distinguish between the predicted boxes and the actual ones)
I will be training on a bigger dataset (either coco or pascal voc) overnight and see if it can reach reasonable results, then I’ll share the notebook and make a PR to @muellerzr course github project.
(The code is really hacky and awful in some places and I hope we can make concise and clear going forward )
Hello. Just want to be notified about any progress in object detection inference.
Sorry for the delay, had a slow few days…
Here is the link to my fork with the notebook “06_Object_Detection_changed” being the modified notebook. I removed the setup/installation code as I run this locally. In the
object_detection_metrics folder the code is copied from rafaelpadilla/Object-Detection-Metrics.
There are 3 modifications to @muellerzr code:
RetinaNet only returns the box and class predictions (in this order), and the dynamically computed sizes are stored in
RetinaNetFocalLoss takes a reference to the model, from which it gets the computed sizes
RetinaNetFocalLoss has a
decodes method, that applies the argmax over the predicted scores over all classes. (We need the scores for the non-max-suppression, but want the argmax for displaying the result)
These changes make it possible to use
show_results as the output of the model now is similar to the dependent variables of the dataloaders (boxes and classes).
Then the notebook contains a non-max-suppression callback which uses the torchvision operator, and a mAP metric using the github repo linked above.
If you have any questions, ideas for improvement, or see find any bugs, please share them
DISCLAIMER: THIS IS NOT FINISHED CODE,
I don’t have much time to work on it at the moment and it is not getting results anywhere near good. I’m sharing this so others can have a look and improve the approach so we can get closer to good object detection in fastai2
@j.laute, nice notebook but I still cannot use the trained model for inference. Your codes look good but it might error out if the data is small or the initial model is bad. I made some small changes and am not sure they are correct.
def accumulate(self, learn):
# add predictions and ground truths
pred_boxes, pred_scores = learn.pred
# 0 means no prediction
# when the model is bad, no predictions are generated
if pred_scores.shape == 0:
# return -1 if no predictions are generated
if self.reference_metric.res is None:
_ = self.reference_metric.value
if self.lookup_idx < len(self.reference_metric.res):
One more issue: learn.export() does not work and I got this error:
PicklingError: Can’t pickle <function at 0x12a2ef050>: attribute lookup on main failed
If we only have one reference image say a retail board of ‘Toysrus’ and detect that in an image of an area which has ‘Toysrus’ among other retailer boards, how do you proceed using fastai2?
We will not have a lot of images of the board i.e. its a standard board which needs to be detected.
Any approach will be helpful.
Has anyone taken a look at the new End-to-End Object Detection with Transformers by Facebook? The advantage being having to do way less hyperparameter tuning as the architecture is more straightforward compared to other approaches.
Might be a candidate to port to fastai2…
I had a look at the paper, models are also available pretrained in pytorch
This video explains it very nicely if anyone is interested: https://www.youtube.com/watch?v=T35ba_VXkMY
We still need the metrics (Average Precision) to have a nice object detection in fastai2 if I’m not wrong. Hope to work on that very soon (there is some preliminary work on the forums.
Has anybody managed to work out how to plot a confusion matrix on the Retinanet multi-object detection notebook ? I’ve been laying about with it but not managed to get anything working.
DETR is fantastic (the end to end transformer). I’m working with it extensively now and it is way smarter than your usual detector b/c of the transformer.
No ports to fastai yet that I know of but I have a basic colab in progress here:
I have a PR in for making it a bit easier to train custom datasets (there is no num_classes as a param in DETR repo) but I will probably just proceed with showing the code changes to do your own training this weekend.
Note that there’s also another tutorial that provides a more fast-ai (better abstraction) like way to run DETR as well here:
In my colab I’m trying to stick as close to raw DETR codebase so it’s pretty low level, vs the above is quite elegant in it’s own purpose of providing a friendlier set of abstractions, so two different aspects on working with DETR.
Thanks for sharing @LessW2020
We also just finished a self contained colab notebook
This one install all the dependencies and automatically downloads the dataset, just like fastai does
I hope it’s helpful for anyone starting to explore Detr
I am unable to acces your notebooks/codes are they still available? I am planning on implementing a DETR for image sequences.
The github.io and the colab notebook are unavailable right now. Have the locations changed? Thanks for your help!
@tcapelle and @shimsan, here is the repo and here a quickstart
Feel free to ask any questions
Thank you for sharing this. Is it resource hungry ? Will it work on CPU’s ?