273 lines
4.9 KiB
Markdown
Executable File
273 lines
4.9 KiB
Markdown
Executable File
# ByteTrack 训练与评估
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> 完整训练测试流程
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---
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## 1. 数据准备
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### 转换数据集格式
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```bash
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# 转换 MOT17
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python3 tools/convert_mot17_to_coco.py
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# 转换 MOT20
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python3 tools/convert_mot20_to_coco.py
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# 转换 CrowdHuman
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python3 tools/convert_crowdhuman_to_coco.py
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# 转换 Cityperson
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python3 tools/convert_cityperson_to_coco.py
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# 转换 ETHZ
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python3 tools/convert_ethz_to_coco.py
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```
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### 混合训练数据
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```bash
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# 基础混合
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python3 tools/mix_data_ablation.py
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# 生成测试数据
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python3 tools/mix_data_test_mot17.py
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python3 tools/mix_data_test_mot20.py
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```
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---
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## 2. 模型训练
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### 消融实验
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训练数据：CrowdHuman + MOT17 Half Train
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```bash
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python3 tools/train.py \
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-f exps/example/mot/yolox_x_ablation.py \
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-d 8 -b 48 --fp16 -o \
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-c pretrained/yolox_x.pth
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```
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### MOT17 完整训练
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训练数据：CrowdHuman + MOT17 + Cityperson + ETHZ
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```bash
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python3 tools/train.py \
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-f exps/example/mot/yolox_x_mix_det.py \
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-d 8 -b 48 --fp16 -o \
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-c pretrained/yolox_x.pth
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```
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### MOT20 训练
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训练数据：CrowdHuman + MOT20
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> ⚠️ **注意**：MOT20 需要裁剪边界框
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在 `data_augment.py` 第 134-135 行添加裁剪操作：
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```python
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# clip border
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x1 = max(0, x1)
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y1 = max(0, y1)
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```
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```bash
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python3 tools/train.py \
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-f exps/example/mot/yolox_x_mix_mot20_ch.py \
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-d 8 -b 48 --fp16 -o \
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-c pretrained/yolox_x.pth
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```
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---
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## 3. 模型评估
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### MOT17 Half Val
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```bash
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# ByteTrack
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python3 tools/track.py \
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-f exps/example/mot/yolox_x_ablation.py \
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-c pretrained/bytetrack_ablation.pth.tar \
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-b 1 -d 1 --fp16 --fuse
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# 其他 Tracker 对比
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python3 tools/track_sort.py -f exps/example/mot/yolox_x_ablation.py \
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-c pretrained/bytetrack_ablation.pth.tar -b 1 -d 1 --fp16 --fuse
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python3 tools/track_deepsort.py -f exps/example/mot/yolox_x_ablation.py \
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-c pretrained/bytetrack_ablation.pth.tar -b 1 -d 1 --fp16 --fuse
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python3 tools/track_motdt.py -f exps/example/mot/yolox_x_ablation.py \
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-c pretrained/bytetrack_ablation.pth.tar -b 1 -d 1 --fp16 --fuse
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```
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### MOT17 测试集
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```bash
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# 追踪
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python3 tools/track.py \
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-f exps/example/mot/yolox_x_mix_det.py \
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-c pretrained/bytetrack_x_mot17.pth.tar \
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-b 1 -d 1 --fp16 --fuse
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# 轨迹插值（提升性能）
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python3 tools/interpolation.py
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```
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提交 txt 文件到 [MOTChallenge](https://motchallenge.net/) 可获得 **79+ MOTA**。
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### MOT20 测试集
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```bash
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# 编辑输入尺寸 (yolox_x_mix_mot20_ch.py)
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# MOT20-04, MOT20-07: 1600 x 896
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# MOT20-06, MOT20-08: 1920 x 736
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# 追踪
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python3 tools/track.py \
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-f exps/example/mot/yolox_x_mix_mot20_ch.py \
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-c pretrained/bytetrack_x_mot20.pth.tar \
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-b 1 -d 1 --fp16 --fuse \
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--match_thresh 0.7 --mot20
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# 插值
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python3 tools/interpolation.py
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```
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---
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## 4. 使用自己的检测器
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### 传入检测结果
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```python
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from yolox.tracker.byte_tracker import BYTETracker
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# 初始化
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args = # 你的参数
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tracker = BYTETracker(args)
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# 逐帧处理
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for image in images:
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# 你的检测器
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dets = your_detector(image) # 格式: (N, 5) [x1, y1, x2, y2, score]
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# 追踪更新
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online_targets = tracker.update(dets, info_imgs, img_size)
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# 获取结果
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for target in online_targets:
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tlwh = target.tlwh # 轨迹框
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track_id = target.track_id # ID
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score = target.score # 置信度
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```
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### 参考代码
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详见 `yolox/evaluators/mot_evaluator.py`
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---
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## 5. 视频演示
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```bash
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# 视频追踪
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python3 tools/demo_track.py video \
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-f exps/example/mot/yolox_x_mix_det.py \
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-c pretrained/bytetrack_x_mot17.pth.tar \
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--fp16 --fuse --save_result
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```
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结果保存在 `YOLOX_outputs/`
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---
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## 6. 参数调优
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### 关键参数
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| 参数 | 说明 | 调优建议 |
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|------|------|----------|
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| `track_thresh` | 检测阈值 | 0.5-0.6 效果较好 |
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| `match_thresh` | IoU 匹配阈值 | 0.8 常用 |
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| `match_thresh` (MOT20) | MOT20 阈值 | 0.7 |
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| `track_buffer` | 轨迹缓冲帧数 | 30 |
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| `frame_rate` | 目标帧率 | 30 |
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### MOT17 测试集优化
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获得 **80+ MOTA** 的技巧：
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1. 仔细调整每个序列的测试图像尺寸
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2. 调整每个序列的高分检测阈值
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3. 使用轨迹插值
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---
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## 7. 自定义数据集
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### 1. 准备数据（COCO 格式）
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```bash
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# 转换为 COCO 格式
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python3 tools/convert_mot17_to_coco.py
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```
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### 2. 创建 Exp 文件
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参考 `exps/example/mot/yolox_x_ch.py`
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### 3. 修改配置
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```python
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class MyExp(YOLOXExp):
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def get_data_loader(self):
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# 返回训练数据加载器
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...
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def get_eval_loader(self):
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# 返回评估数据加载器
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...
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```
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### 4. 训练
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```bash
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python3 tools/train.py \
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-f exps/example/mot/my_exp.py \
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-d 8 -b 48 --fp16 -o \
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-c pretrained/yolox_x.pth
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```
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---
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## 8. 部署推理
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### ONNX 导出
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详见 `deploy/ONNXRuntime/`
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### TensorRT 部署
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```bash
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# Python
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deploy/TensorRT/python/
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# C++
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deploy/TensorRT/cpp/
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```
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### DeepStream
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```bash
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deploy/DeepStream/
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```
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---
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> 参考：[ByteTrack GitHub](https://github.com/FoundationVision/ByteTrack)
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