# HiT
**Repository Path**: perceptive558/HiT
## Basic Information
- **Project Name**: HiT
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-02-27
- **Last Updated**: 2026-02-27
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
* # [IJCV'2025] - Exploiting Lightweight Hierarchical ViT and Dynamic Framework for Efficient Visual Tracking
> [**Exploiting Lightweight Hierarchical ViT and Dynamic Framework for Efficient Visual Tracking**](https://link.springer.com/article/10.1007/s11263-025-02500-9)
> accepted by IJCV
* # [ICCV'2023] - Exploring Lightweight Hierarchical Vision Transformers for Efficient Visual Tracking
> [**Exploring Lightweight Hierarchical Vision Transformers for Efficient Visual Tracking**](https://arxiv.org/abs/2308.06904)
> accepted by ICCV2023
This is an official pytorch implementation of the ICCV2023 paper **Exploring Lightweight Hierarchical Vision Transformers for Efficient Visual Tracking** and IJCV paper **Exploiting Lightweight Hierarchical ViT and Dynamic Framework for Efficient Visual Tracking** .
## Highlights
### Simple architecture of HiT
The architecture of HiT is very simple only contains three components: a lightweight hierarchical vision transformer, a bridge Module and a prediction head.
### Better Speed-Accuracy trade off
DyHiT adopts a divide-and-conquer strategy for handling complex and simple scenarios, achieving a better balance between speed and accuracy. By adjusting the partition threshold, DyHiT can adapt to various types of scenarios and attain an appropriate speed–accuracy trade-off. With a single DyHiT tracker, multiple speed–accuracy balance modes can be realized.
### Accelerating the base tracker
DyHiT can be integrated with any base tracker to improve its speed while preserving accuracy. This approach requires no additional training and works in a plug-and-play manner.
### Strong performance
HiT and DyHiT achieve promising speed with competitive performance, they are faster and perform better than previous efficient trackers, they significantly closes the gap between efficient trackers and mainstream trackers. It can be deployed in low-computing power scenarios.
| | HiT-Base | DyHiT | HiT-Small | HiT-Tiny | FEAR | TransT |
| :---------------------------------------- | :------: | -------- | :-------: | :------: | :--: | :----: |
| LaSOT (AUC) | **64.6** | **62.4** | **60.4** | **54.7** | 53.5 | 64.9 |
| GOT-10K (AO) | **64.0** | **62.9** | **62.6** | **52.6** | 61.9 | 72.3 |
| TrackingNet (AUC) | **80.0** | **77.9** | **77.7** | **74.6** | - | 81.4 |
| PyTorch Speed on 3090 GPU (FPS) | **175** | **299** | **192** | **204** | 105 | 63 |
| PyTorch Speed on i9-9900K CPU (FPS) | **33** | **63** | **72** | **76** | 60 | 5 |
| PyTorch Speed on Nvidia Jetson AGX (FPS) | **61** | **111** | **68** | **77** | 38 | 13 |
| PyTorch Speed on Nvidia Jetson NX (FPS) | **32** | **-** | **34** | **39** | 22 | 11 |
| ONNX Speed on 3090 GPU (FPS) | **274** | - | **400** | **455** | - | - |
| ONNX Speed on i9-9900K CPU (FPS) | **42** | - | **98** | **125** | - | - |
| ONNX Speed on Nvidia Jetson AGX (FPS) | **75** | - | **119** | **145** | - | - |
# Install the environment
**Option1**: Use the Anaconda
```
conda create -n HIT python=3.8
conda activate HIT
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
pip install -r requirements.txt
```
## Data Preparation
Put the tracking datasets in ./data. It should look like:
```
${STARK_ROOT}
-- data
-- lasot
|-- airplane
|-- basketball
|-- bear
...
-- got10k
|-- test
|-- train
|-- val
-- coco
|-- annotations
|-- images
-- trackingnet
|-- TRAIN_0
|-- TRAIN_1
...
|-- TRAIN_11
|-- TEST
```
## Set project paths
Run the following command to set paths for this project
```
python tracking/create_default_local_file.py --workspace_dir . --data_dir ./data --save_dir .
```
After running this command, you can also modify paths by editing these two files
```
lib/train/admin/local.py # paths about training
lib/test/evaluation/local.py # paths about testing
```
## Train HiT
```
python -m torch.distributed.launch --nproc_per_node 8 lib/train/run_training.py --script HiT --config HiT_Base --save_dir .
```
(Optionally) Debugging training with a single GPU
```
python lib/train/run_training.py --script HiT --config HiT_Base --save_dir .
```
## Train DyHiT
Before training DyHiT, you need to download the HiT_Base weights and modify the **WEIGHT** path in [experiments/DyHiT/stage1.yaml](experiments/DyHiT/stage1.yaml) to point to the location of the HiT_Base weights.
```
python lib/train/run_training.py --script DyHiT --config stage1 --save_dir .
```
After completing the first stage of training, you need to modify the **WEIGHT** path in [experiments/DyHiT/stage2.yaml](experiments/DyHiT/stage2.yaml) to the weights obtained from the first stage.
```
python lib/train/run_training.py --script DyHiT --config stage2 --save_dir .
```
## Test and evaluate on benchmark
**When evaluating DyHiT, you can adjust the THRESHOLD parameter (range 0.6–1) in [experiments/DyHiT/stage2.yaml](experiments/DyHiT/stage2.yaml) to achieve different speed–accuracy trade-offs.**
- LaSOT
```
python tracking/test.py HiT HiT_Base --dataset lasot --threads 2 --num_gpus 2 --debug 0
python tracking/analysis_results.py # need to modify tracker configs and names
```
- GOT10K-test
```
python tracking/test.py HiT HiT_Base --dataset got10k_test --threads 2 --num_gpus 2 --debug 0
python lib/test/utils/transform_got10k.py --tracker_name HiT --cfg_name HiT_Base
```
- TrackingNet
```
python tracking/test.py HiT HiT_Base --dataset trackingnet --threads 2 --num_gpus 2 --debug 0
python lib/test/utils/transform_trackingnet.py --tracker_name HiT --cfg_name HiT_Base
```
- NFS
```
python tracking/test.py HiT HiT_Base --dataset nfs --threads 2 --num_gpus 2 --debug 0
python tracking/analysis_results.py # need to modify tracker configs and names
```
- UAV123
```
python tracking/test.py HiT HiT_Base --dataset uav --threads 2 --num_gpus 2 --debug 0
python tracking/analysis_results.py # need to modify tracker configs and names
```
- LaSOText
```
python tracking/test.py HiT HiT_Base --dataset lasot_extension_subset --threads 2 --num_gpus 2 --debug 0
python tracking/analysis_results.py # need to modify tracker configs and names
```
## Accelerating the Base Tracker
We demonstrate how to use DyHiT to accelerate OSTrack (**DyOSTrack**). You can refer to this approach to accelerate other base trackers as well.
- First, you need to download the OSTrack weights and the DyHiT weights, and modify the **WEIGHT** path in [experiments/DyOSTrack/dyostrack.yaml](experiments/DyOSTrack/dyostrack.yaml) to point to the locations of these two weights.
- You can adjust the **THRESHOLD** parameter in [experiments/DyOSTrack/dyostrack.yaml](experiments/DyOSTrack/dyostrack.yaml) to achieve different speed–accuracy trade-offs.
```
python tracking/test.py DyOSTrack dyostrack --dataset --threads 2 --num_gpus 2 --debug 0
```
## Run Video demo
```
python tracking/video_demo.py