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# Hierarchical Object-to-Zone Graph for Object Navigation | ||
## Setup | ||
---- | ||
Sixian Zhang, Xinhang Song, Yubing Bai, Weijie Li, Yakui Chu, Shuqiang Jiang (Accepted by ICCV 2021) | ||
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||
[ICCV 2021 Paper]() | | ||
[Arxiv Paper](https://arxiv.org/abs/2109.02066) | | ||
[Video demo](https://drive.google.com/file/d/1UtTcFRhFZLkqgalKom6_9GpQmsJfXAZC/view) | ||
## Setup | ||
- Clone the repository `git clone https://github.com/sx-zhang/HOZ.git` and move into the top-level directory `cd HOZ` | ||
- Install the dependencies. `pip install -r requirements.txt` | ||
- We provide pre-trained model of [hoz](https://drive.google.com/file/d/11L-ejoWgLHPBe_F-gQ7dJ5gQZB0dzNjr/view?usp=sharing) and [hoztpn](). For evaluation and fine-tuning training, you can download them to the `trained_models` directory. | ||
- Download the [dataset](https://drive.google.com/file/d/1kvYvutjqc6SLEO65yQjo8AuU85voT5sC/view), which refers to [ECCV-VN](https://github.com/xiaobaishu0097/ECCV-VN). The offline data is discretized from [AI2THOR](https://ai2thor.allenai.org/) simulator. | ||
Your `data` folder should look like this | ||
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`Data | ||
└── AI2thor_offline_data/ | ||
├── FloorPlan1 | ||
│ ├── resnet18_featuremap.hdf5 | ||
│ ├── graph.json | ||
│ ├── visible_object_map_1.5.json | ||
│ ├── det_feature_categories.hdf5 | ||
│ ├── grid.json | ||
│ └── optimal_action.json | ||
├── FloorPlan2 | ||
└── ...` | ||
## HOZ graph Construction | ||
## Training and Evaluation | ||
### Train the baseline model | ||
`python main.py --title Basemodel --model BaseModel --workers 12 –gpu-ids 0` | ||
### Train our HOZ model | ||
`python main.py --title HOZ --model HOZ --workers 12 –gpu-ids 0` | ||
### Evaluate our HOZ model | ||
`python python full_eval.py --title HOZ --model HOZ --results_json HOZ.json --gpu-ids 0` | ||
### Evaluate our HOZ-TPN model | ||
`python python full_eval.py --title HOZTPN --model MetaMemoryHOZ --results_json HOZTPN.json --gpu-ids 0` | ||
## Citing |
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