Comments (9)
Hi @antoniyaaboyanova ,
I checked our code for the different CORnet versions, and all models, including CORnet-s, correctly pull pretrained state dicts from the official AWS bucket and load their weights from them. This is more or less identical to the official CORnet repository. Model outputs also differ between pretrained and random weight initialization and resulting RDMs for some images I had lying around look significantly different.
For me, this suggests that on our side things should work as intended. Could you maybe post the code you used to do your feature extraction and analyses? Also, what version of thingsvision
do you use?
Best,
Johannes
from thingsvision.
Thanks for getting in touch!
I'm sorry, I didn't understand what problem you were referring to. What's the issue? Being specific is great, as it allows us to check the code and run tests and hopefully identify a problem.
from thingsvision.
from thingsvision.
Could this be for some reason related to #46 or is it a mistake on the end of @antoniyaaboyanova? Could you check this @antoniyaaboyanova? Posting the code you've used, as @andropar suggested, would definitely be helpful, and letting us know about your thingsvision
version.
from thingsvision.
from thingsvision.
@antoniyaaboyanova, you'll have to post the code into the Github issue, I don't think you can attach files when answering by mail! 🙂
from thingsvision.
Thanks for pointing that out! Sorry about that. I think the best way to show you what I have is by providing you with google colab links:
For the feature extraction: https://colab.research.google.com/drive/1BpBW_GBpDxm84oua5k-F3HPRV2Bg47uZ?usp=sharing
For the SVM on the features: https://colab.research.google.com/drive/1jzxS0LDaqNaG8ovgASPBBpnD1APA6FL_?usp=sharing
The links are set to viewers mode only. Let me know if this is not sufficient.
from thingsvision.
I think this is not an issue on our end. @andropar, can we close this?
from thingsvision.
Yes, I think so too.
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Related Issues (20)
- @andropar Please add documentation about this new feature in the `README.md` and our official docs. Looks good so far :) Default option should stay as what we have had before HOT 1
- TypeError from flatten_acts() when executing extractor.extract_features() HOT 1
- CLIP doesn't get installed properly
- further changes to the extraction pipeline
- Using pretrained weights from caffe with THINGSvision HOT 4
- Could not find a custom model HOT 5
- size mismatch error HOT 6
- Shape mismatch error while extracting layers from InceptionResNetV2 HOT 10
- Making `resize_dim` and `crop_dim` flexible arguments HOT 7
- Upgrade torch requirements HOT 1
- Fix error thrown when passing `None` as `model_parameters` for `get_extractor` HOT 1
- Tensorflow errors after import HOT 6
- Add more output options (i.e. txt, mat) HOT 3
- improve docs HOT 1
- Wrong layers (names?) when extracting all conv layers from AlexNet. HOT 4
- Feature extraction for a single image HOT 7
- small issues with CLI HOT 3
- Moving to torch 2.0 + HOT 1
- Add thingsvision.__version__ HOT 3
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