Openlrm Obj Small 1.1

--- license: cc-by-nc-4.0 datasets: - allenai/objaverse pipeline_tag: image-to-3d --- # Model Card for OpenLRM V1.1 ## Overview - This model card is for the [OpenLRM](https://github.com/3DTopia/OpenLRM) project, which is an open-source implementation of the paper [LRM](https://arxiv.org/abs/2311.04400). - Information contained in this model card corresponds to [Version 1.1](https://github.com/3DTopia/OpenLRM/releases). ## Model Details - Training data | Model | Training Data | | :---: | :---: | | [openlrm-obj-small-1.1](https://huggingface.co/zxhezexin/openlrm-obj-small-1.1) | Objaverse | | [openlrm-obj-base-1.1](https://huggingface.co/zxhezexin/openlrm-obj-base-1.1) | Objaverse | | [openlrm-obj-large-1.1](https://huggingface.co/zxhezexin/openlrm-obj-large-1.1) | Objaverse | | [openlrm-mix-small-1.1](https://huggingface.co/zxhezexin/openlrm-mix-small-1.1) | Objaverse + MVImgNet | | [openlrm-mix-base-1.1](https://huggingface.co/zxhezexin/openlrm-mix-base-1.1) | Objaverse + MVImgNet | | [openlrm-mix-large-1.1](https://huggingface.co/zxhezexin/openlrm-mix-large-1.1) | Objaverse + MVImgNet | - Model architecture (version==1.1) | Type | Layers | Feat. Dim | Attn. Heads | Triplane Dim. | Input Res. | Image Encoder | Size | | :---: | :----: | :-------: | :---------: | :-----------: | :--------: | :---------------: | :---: | | small | 12 | 512 | 8 | 32 | 224 | dinov2_vits14_reg | 446M | | base | 12 | 768 | 12 | 48 | 336 | dinov2_vitb14_reg | 1.04G | | large | 16 | 1024 | 16 | 80 | 448 | dinov2_vitb14_reg | 1.81G | - Training settings | Type | Rend. Res. | Rend. Patch | Ray Samples | | :---: | :--------: | :---------: | :---------: | | small | 192 | 64 | 96 | | base | 288 | 96 | 96 | | large | 384 | 128 | 128 | ## Notable Differences from the Original Paper - We do not use the deferred back-propagation technique in the original paper. - We used random background colors during training. - The image encoder is based on the [DINOv2](https://github.com/facebookresearch/dinov2) model with register tokens. - The triplane decoder contains 4 layers in our implementation. ## License - The model weights are released under the [Creative Commons Attribution-NonCommercial 4.0 International License](LICENSE_WEIGHT). - They are provided for research purposes only, and CANNOT be used commercially. ## Disclaimer This model is an open-source implem...