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After running the multi-preprocess, I met thist problems:
Platform::WindowlessEglApplication::tryCreateContext(): cannot get default EGL display: EGL_BAD_PARAMETER
WindowlessContext: Unable to create windowless context

Could you specify which versions of the LibriSpeech, FMA, and HMD3D datasets I should download?

example) LibriSpeech: original-mp3, FMA: fma-full, hmd3d: hm3d_train_full

[11:11:54:265809]:[Metadata] AssetAttributesManager.cpp(110)::AssetAttributesManager : Built default primitive asset templates : 12
[11:11:54:266126]:[Metadata] SceneDatasetAttributesManager.cpp(308)::readDatasetJSONCell : "stages.default_attributes" set in Attributes Manager from JSON.
[11:11:54:266182]:[Metadata] AttributesManagerBase.h(357)::buildAttrSrcPathsFromJSONAndLoad : No Glob path result for /root/sound-spaces/data/scene_da tasets/data/versioned_data/hm3d-1.0/hm3d/example/00861-GLAQ4DNUx5U/.basis.glb
[11:11:54:266212]:[Metadata] AttributesManagerBase.h(357)::buildAttrSrcPathsFromJSONAndLoad : No Glob path result for /root/sound-spaces/data/scene_da tasets/data/versioned_data/hm3d-1.0/hm3d/minival/00800-TEEsavR23oF/.basis.glb
[11:11:54:266240]:[Metadata] AttributesManagerBase.h(357)::buildAttrSrcPathsFromJSONAndLoad : No Glob path result for /root/sound-spaces/data/scene_da tasets/data/versioned_data/hm3d-1.0/hm3d/minival/00802-wcojb4TFT35/*.basis.glb

Hello, I trained the model the slfm_pretext and then slfm_geometric. However, when I evaluate the geometric model, the graph shows that only from 200-1000 are accurately predicted. However, from 0-200 and 1000-1200, are every inaccurately predicted. I was wondering if this is something wrong with the evaluate_angle code. Please look at the attachment

Hello, https://ificl.github.io/SLfM/ the real - world inside the realization of the demo code can be sent to me have a look

Hello, I was trying the preset training with resnet-50. However, the error shows that there's a key not found error, and a size mismatch error. The only thing I changed was in slfm-pretext.py. I've attached the screenshots below. Please take a look. Thank you so much!