license: mit
tags:
- cad
- template-fitting
- pytorch
TempFit checkpoint
Model weights for template-based industrial CAD scene reconstruction.
Code and installation · Dataset
The tempfit-scratch directory contains model.safetensors and its companion model.json.
The model weights occupy approximately 508.49 MB. The accompanying metadata
provides the configuration and file checksums used by TempFit.
Usage
Install TempFit and place the dataset under its data/ and splits/ directories.
Download the weights from the repository root:
pip install -U huggingface_hub
hf download Daisy-1227/TempFit --include "tempfit-scratch/*" --local-dir checkpoints
Run autoregressive inference:
python inference.py --config configs/inference.yaml --checkpoint checkpoints/tempfit-scratch/model.safetensors --output_dir outputs/tempfit-scratch
Initialize a new training run from these weights:
accelerate launch --multi_gpu --num_processes 8 --mixed_precision bf16 train.py --config configs/train.yaml --pretrained checkpoints/tempfit-scratch/model.safetensors --output_dir outputs/tempfit-scratch-finetune
For a single GPU:
python train.py --config configs/train.yaml --pretrained checkpoints/tempfit-scratch/model.safetensors --output_dir outputs/tempfit-scratch-finetune
Gradient accumulation adjusts to preserve the default global batch size of 128.
Keep model.json alongside its weight file. The package supports inference and
pretrained initialization; optimizer and scheduler states are excluded.
License
The weights are released under the MIT License. Dataset access and use are governed separately by the dataset license.