YOLOX专栏：内容详尽、实用易懂

释放双眼，带上耳机，听听看~！

本文详细介绍了YOLOX的环境搭建、COCO数据集准备、训练和测试过程，适合对目标检测感兴趣的AI技术爱好者阅读学习。

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YOLOX专栏：内容详尽、实用易懂

🍊 计算机视觉： Yolo专栏、一文读懂
🍊 YOLOX 系列推荐：YOLOX COCO训练复现
📆 最近更新：2022年2月22日
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📔 认识 YOLOX

🍊 github.com/Megvii-Base…

🍊 YOLOX: Exceeding YOLO Series in 2021 — 当前论文

YOLOX专栏：内容详尽、实用易懂

📕 环境搭建

服务器环境

## 服务器
cat /etc/issue
 
Ubuntu 18.04.5 LTS n l

## Cuda版本

nvcc -V

nvcc: NVIDIA (R) Cuda compiler driver
...
Cuda compilation tools, release 10.0, V10.0.130

# 显卡

Quadro RTX 5000 16G x 4

# 内存 128G

conda 环境独立搭建

git clone https://github.com/Megvii-BaseDetection/YOLOX.git

cd YOLOX

conda create -n torchYolo python=3.8.5

conda activate torchYolo 

pip3 install -U pip && pip3 install -r requirements.txt

python3 setup.py develop

pip3 install cython

pip install pycocotools

项目目录结构

YOLOX专栏：内容详尽、实用易懂

COCO 数据集准备【如果没有，可以参考该博文进行下载】

YOLOv5 COCO数据集训练

📗 测试

🟧 单图CPU测试

python tools/demo.py image -n yolox-s -c preModels/yolox_s.pth --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result

输出如下

2021-08-25 01:56:16.305 | INFO     | __main__:main:249 - Args: Namespace(camid=0, ckpt='preModels/yolox_s.pth', conf=0.25, demo='image', device='cpu', exp_file=None, experiment_name='yolox_s', fp16=False, fuse=False, legacy=False, name='yolox-s', nms=0.45, path='assets/dog.jpg', save_result=True, trt=False, tsize=640)


2021-08-25 01:56:16.507 | INFO     | __main__:main:259 - Model Summary: Params: 8.97M, Gflops: 26.81
2021-08-25 01:56:16.508 | INFO     | __main__:main:270 - loading checkpoint
2021-08-25 01:56:16.648 | INFO     | __main__:main:274 - loaded checkpoint done.
2021-08-25 01:56:16.839 | INFO     | __main__:inference:159 - Infer time: 0.1768s
2021-08-25 01:56:16.841 | INFO     | __main__:image_demo:196 - Saving detection result in 

./YOLOX_outputs/yolox_s/vis_res/2021_08_25_01_56_16/dog.jpg

🟨 单图GPU测试

python tools/demo.py image -n yolox-s -c preModels/yolox_s.pth --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result --device gpu

# 或者 用指定 文件的方式 specify your detector's config
# 区别： -n 指定 yolo 模型名字 | -f 指定  yolo  config 文件 ，这两种方式用其中一种即可

python tools/demo.py image -f exps/default/yolox_s.py -c preModels/yolox_s.pth --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result  --device gpu

输出如下

python tools/demo.py image -n yolox-s -c preModels/yolox_s.pth --path assets/dog.jpg --conf 0.25 --nms 0.45 --tsize 640 --save_result --device 0

2021-08-25 01:59:29.086 | INFO     | __main__:main:249 - Args: Namespace(camid=0, ckpt='preModels/yolox_s.pth', conf=0.25, demo='image', device='0', exp_file=None, experiment_name='yolox_s', fp16=False, fuse=False, legacy=False, name='yolox-s', nms=0.45, path='assets/dog.jpg', save_result=True, trt=False, tsize=640)

2021-08-25 01:59:29.285 | INFO     | __main__:main:259 - Model Summary: Params: 8.97M, Gflops: 26.81
2021-08-25 01:59:29.286 | INFO     | __main__:main:270 - loading checkpoint
2021-08-25 01:59:29.427 | INFO     | __main__:main:274 - loaded checkpoint done.

2021-08-25 01:59:29.619 | INFO     | __main__:inference:159 - Infer time: 0.1775s
2021-08-25 01:59:29.621 | INFO     | __main__:image_demo:196 - Saving detection result in ./YOLOX_outputs/yolox_s/vis_res/2021_08_25_01_59_29/dog.jpg

🟦 video GPU测试

-n yolox-s -c preModels/yolox_s.pth –device gpu 测试

time python tools/demo.py video -n yolox-s -c preModels/yolox_s.pth --path assets/obj.mp4 --conf 0.25 --nms 0.45 --tsize 640 --save_result --device gpu

# 输出如下

# GPU 占用 1199MiB

2021-08-25 03:28:35.486 | INFO     | __main__:main:249 - Args: Namespace(camid=0, ckpt='preModels/yolox_s.pth', conf=0.25, demo='video', device='gpu', exp_file=None, experiment_name='yolox_s', fp16=False, fuse=False, legacy=False, name='yolox-s', nms=0.45, path='assets/obj.mp4', save_result=True, trt=False, tsize=640)

2021-08-25 03:28:35.680 | INFO     | __main__:main:259 - Model Summary: Params: 8.97M, Gflops: 26.81
2021-08-25 03:28:39.340 | INFO     | __main__:imageflow_demo:216 - video save_path is ./YOLOX_outputs/yolox_s/vis_res/2021_08_25_03_28_39/obj.mp4
2021-08-25 03:28:39.418 | INFO     | __main__:inference:159 - Infer time: 0.0182s
2021-08-25 03:28:39.438 | INFO     | __main__:inference:159 - Infer time: 0.0112s
2021-08-25 03:28:39.457 | INFO     | __main__:inference:159 - Infer time: 0.0112s


2021-08-25 03:28:45.664 | INFO     | __main__:inference:159 - Infer time: 0.0138s
2021-08-25 03:28:45.684 | INFO     | __main__:inference:159 - Infer time: 0.0138s
2021-08-25 03:28:45.704 | INFO     | __main__:inference:159 - Infer time: 0.0139s

real    0m11.579s

-n yolox-x -c preModels/yolox_x.pth –device gpu 测试

time python tools/demo.py video -n yolox-x -c preModels/yolox_x.pth --path assets/objDetect.m4v --conf 0.25 --nms 0.45 --tsize 640 --save_result --device gpu


# 输出如下

# GPU 占用 1671MiB

2021-08-25 03:21:50.171 | INFO     | __main__:main:249 - Args: Namespace(camid=0, ckpt='preModels/yolox_x.pth', conf=0.25, demo='video', device='gpu', exp_file=None, experiment_name='yolox_x', fp16=False, fuse=False, legacy=False, name='yolox-x', nms=0.45, path='assets/obj.mp4', save_result=True, trt=False, tsize=640)


2021-08-25 03:21:51.248 | INFO     | __main__:main:259 - Model Summary: Params: 99.07M, Gflops: 281.93
2021-08-25 03:21:54.904 | INFO     | __main__:main:270 - loading checkpoint
2021-08-25 03:21:55.589 | INFO     | __main__:main:274 - loaded checkpoint done.
2021-08-25 03:21:55.595 | INFO     | __main__:imageflow_demo:216 - video save_path is ./YOLOX_outputs/yolox_x/vis_res/2021_08_25_03_21_55/obj.mp4

2021-08-25 03:21:55.712 | INFO     | __main__:inference:159 - Infer time: 0.0553s
2021-08-25 03:21:55.770 | INFO     | __main__:inference:159 - Infer time: 0.0485s
2021-08-25 03:21:55.826 | INFO     | __main__:inference:159 - Infer time: 0.0477s

real    0m22.646s

可以直接生成得到带检测框的 video

YOLOX专栏：内容详尽、实用易懂

📘 Reproduce results on COCO 【COCO训练】

🔴 COCO数据集安置

YOLOX专栏：内容详尽、实用易懂

🔵 vim tools/train.py 查看关键参数配置

YOLOX专栏：内容详尽、实用易懂

🟣 训练命令

python tools/train.py -n yolox-s -d 4 -b 64 --fp16 -o --cache

参数说明

d: number of gpu devices 【GPU个数】
b: total batch size, the recommended number for -b is num-gpu * 8 【建议 batch size = num-gpu * 8】
— fp16: mixed precision training
–cache: caching imgs into RAM to accelarate training, which need large system RAM.【128G内存可行】

效果如下

YOLOX专栏：内容详尽、实用易懂

看了下服务器内存，128 G 似乎也能这样玩

free -h
              total        used        free      shared  buff/cache   available
Mem:           125G         17G        2.0G        187M        106G        107G
Swap:          8.0G        154M        7.8G

显卡勉强够用

YOLOX专栏：内容详尽、实用易懂

🟡 训练输出


(torchYolo) yoloDir/YOLOX$ python tools/train.py -n yolox-s -d 4 -b 64 --fp16 -o --cache

# 输出如下
...

***************************************************************
We set `OMP_NUM_THREADS` for each process to 1 to speed up.
please further tune the variable for optimal performance.
***************************************************************
2021-08-25 03:46:59 | INFO     | yolox.core.trainer:126 - args: Namespace(batch_size=64, cache=True, ckpt=None, devices=4, dist_backend='nccl', dist_url=None, exp_file=None, experiment_name='yolox_s', fp16=True, machine_rank=0, name='yolox-s', num_machines=1, occupy=True, opts=[], resume=False, start_epoch=None)
2021-08-25 03:46:59 | INFO     | yolox.core.trainer:127 - exp value:

╒══════════════════╤════════════════════════════╕
│ keys             │ values                     │
╞══════════════════╪════════════════════════════╡
│ seed             │ None                       │
├──────────────────┼────────────────────────────┤
│ output_dir       │ './YOLOX_outputs'          │
├──────────────────┼────────────────────────────┤
│ print_interval   │ 10                         │
├──────────────────┼────────────────────────────┤
│ eval_interval    │ 10                         │
├──────────────────┼────────────────────────────┤
│ num_classes      │ 80                         │
├──────────────────┼────────────────────────────┤
│ depth            │ 0.33                       │
├──────────────────┼────────────────────────────┤
│ width            │ 0.5                        │
├──────────────────┼────────────────────────────┤
│ data_num_workers │ 4                          │
├──────────────────┼────────────────────────────┤
│ input_size       │ (640, 640)                 │
├──────────────────┼────────────────────────────┤
│ multiscale_range │ 5                          │
├──────────────────┼────────────────────────────┤
│ data_dir         │ None                       │
├──────────────────┼────────────────────────────┤
│ train_ann        │ 'instances_train2017.json' │
├──────────────────┼────────────────────────────┤
│ val_ann          │ 'instances_val2017.json'   │
├──────────────────┼────────────────────────────┤
│ mosaic_prob      │ 1.0                        │
├──────────────────┼────────────────────────────┤
│ mixup_prob       │ 1.0                        │
├──────────────────┼────────────────────────────┤
│ degrees          │ 10.0                       │
├──────────────────┼────────────────────────────┤
│ translate        │ 0.1                        │
├──────────────────┼────────────────────────────┤
│ mosaic_scale     │ (0.1, 2)                   │
├──────────────────┼────────────────────────────┤
│ mixup_scale      │ (0.5, 1.5)                 │
├──────────────────┼────────────────────────────┤
│ shear            │ 2.0                        │
├──────────────────┼────────────────────────────┤
│ perspective      │ 0.0                        │
├──────────────────┼────────────────────────────┤
│ enable_mixup     │ True                       │
├──────────────────┼────────────────────────────┤
│ warmup_epochs    │ 5                          │
├──────────────────┼────────────────────────────┤
│ max_epoch        │ 300                        │
├──────────────────┼────────────────────────────┤
│ warmup_lr        │ 0                          │
├──────────────────┼────────────────────────────┤
│ basic_lr_per_img │ 0.00015625                 │
├──────────────────┼────────────────────────────┤
│ scheduler        │ 'yoloxwarmcos'             │
├──────────────────┼────────────────────────────┤
│ no_aug_epochs    │ 15                         │
├──────────────────┼────────────────────────────┤
│ min_lr_ratio     │ 0.05                       │
├──────────────────┼────────────────────────────┤
│ ema              │ True                       │
├──────────────────┼────────────────────────────┤
│ weight_decay     │ 0.0005                     │
├──────────────────┼────────────────────────────┤
│ momentum         │ 0.9                        │
├──────────────────┼────────────────────────────┤
│ exp_name         │ 'yolox_s'                  │
├──────────────────┼────────────────────────────┤
│ test_size        │ (640, 640)                 │
├──────────────────┼────────────────────────────┤
│ test_conf        │ 0.01                       │
├──────────────────┼────────────────────────────┤
│ nmsthre          │ 0.65                       │
╘══════════════════╧════════════════════════════╛


2021-08-25 04:15:43 | INFO     | yolox.core.trainer:238 - epoch: 1/300, iter: 1830/1849, mem: 13400Mb, iter_time: 0.534s, data_time: 0.002s, total_loss: 11.3, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 5.1, cls_loss: 2.8, lr: 3.918e-04, size: 672, ETA: 4 days, 13:10:57
2021-08-25 04:15:53 | INFO     | yolox.core.trainer:238 - epoch: 1/300, iter: 1840/1849, mem: 13400Mb, iter_time: 1.007s, data_time: 0.034s, total_loss: 11.4, iou_loss: 3.4, l1_loss: 0.0, conf_loss: 5.2, cls_loss: 2.7, lr: 3.961e-04, size: 672, ETA: 4 days, 13:25:39
2021-08-25 04:16:02 | INFO     | yolox.core.trainer:318 - Save weights to ./YOLOX_outputs/yolox_s
2021-08-25 04:16:03 | INFO     | yolox.core.trainer:188 - ---> start train epoch2

🔴 训练 10 个 epoch 统计训练时长

 time python tools/train.py -n yolox-s -d 4 -b 64 --fp16 -o --cache

训练输出如下 【10 个 epoch 训练时长 140 分钟】

Average forward time: 5.46 ms, Average NMS time: 3.18 ms, Average inference time: 8.65 ms
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.145
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.258
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.145
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.054
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.153
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.204
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.176
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.288
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.309
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.110
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.336
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.418

2021-08-25 06:43:48 | INFO     | yolox.core.trainer:318 - Save weights to ./YOLOX_outputs/yolox_s
2021-08-25 06:43:49 | INFO     | yolox.core.trainer:183 - Training of experiment is done and the best AP is 14.46

# 10 个 epoch 训练时长 140 分钟

real    140m41.991s
user    668m9.676s
sys     519m4.609s

训练生成目录内容如下：

YOLOX专栏：内容详尽、实用易懂

🔵 tensorboard 可视化查看 loss 曲线

cd YOLOX/YOLOX_outputs

# 运行如下命令
tensorboard --logdir yolox_s/ --bind_all

# 输出大致如下

TensorFlow installation not found - running with reduced feature set.
TensorBoard 2.6.0 at http://wave:6006/ (Press CTRL+C to quit)

浏览器中使用 IP : 6006 即可成功查看

YOLOX专栏：内容详尽、实用易懂

🟣 Resume 训练

比如遇到断电、网络类似突发情况，模型训练突然终止，这时，就需要用到

首先，我们可能想要设定最大 epoch 次数【如下图所示】

YOLOX专栏：内容详尽、实用易懂

查阅 tools/train.py 文件，了解参数含义，-e 和 –start_epoch 都可以指定 resume training start epoch

YOLOX专栏：内容详尽、实用易懂

例如我的 resume 训练命令如下

python tools/train.py -n yolox-s -d 4 -b 64 --fp16 -o --cache --resume  -e 10

# 可以看到输出， 继续 之前的 epoch10 开始训练了

2021-08-xx | INFO     | yolox.core.trainer:188 - ---> start train epoch10

2021-08-xx | INFO     | yolox.core.trainer:238 - epoch: 10/300, iter: 10/1849, mem: 13335Mb, iter_time: 6.747s, data_time: 0.003s, total_loss: 8.3, iou_loss: 2.7, l1_loss: 0.0, conf_loss: 3.6, cls_loss: 2.0, lr: 9.995e-03, size: 512, ETA: 42 days, 0:25:32

...

🟡 多机器联合训练

YOLOX专栏：内容详尽、实用易懂

📙 Evaluation

🟧 batch testing for fast evaluation: 【批量快速测试】

这里是对 datasets/COCO/val2017/ 目录下 5000 张 xx.jpg 图片进行推理评估

yolox-s 评估

python tools/eval.py -n  yolox-s -c preModels/yolox_s.pth -b 64 -d 4 --conf 0.001 --fp16 --fuse

4卡测试输出如下 GPU占用 1337MiB

...

2021-08-25 06:58:42.078 | INFO     | yolox.core.launch:_distributed_worker:116 - Rank 0 initialization finished.
2021-08-25 06:58:42.134 | INFO     | yolox.core.launch:_distributed_worker:116 - Rank 2 initialization finished.
2021-08-25 06:58:42.136 | INFO     | yolox.core.launch:_distributed_worker:116 - Rank 1 initialization finished.
2021-08-25 06:58:42.155 | INFO     | yolox.core.launch:_distributed_worker:116 - Rank 3 initialization finished.

...
...

Running per image evaluation...
Evaluate annotation type *bbox*
COCOeval_opt.evaluate() finished in 15.68 seconds.
Accumulating evaluation results...
COCOeval_opt.accumulate() finished in 2.10 seconds.
2021-08-25 06:59:53 | INFO     | __mp_main__:187 - 
Average forward time: 3.49 ms, Average NMS time: 2.27 ms, Average inference time: 5.76 ms
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.405
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.593
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.437
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.232
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.448
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.541
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.326
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.531
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.574
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.365
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.634
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.723

yolox-x 评估

python tools/eval.py -n  yolox-x -c preModels/yolox_x.pth -b 64 -d 4 --conf 0.001 --fp16 --fuse

4卡测试输出如下推理时，GPU占用 3527MiB

...
Running per image evaluation...
Evaluate annotation type *bbox*
COCOeval_opt.evaluate() finished in 12.79 seconds.
Accumulating evaluation results...
COCOeval_opt.accumulate() finished in 1.45 seconds.
2021-08-25 07:06:44 | INFO     | __mp_main__:187 - 

Average forward time: 15.65 ms, Average NMS time: 1.01 ms, Average inference time: 16.65 ms

Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.511
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.691
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.554
Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.347
Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.562
Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.671
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.381
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.621
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.659
Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.486
Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.710
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.819

也可以对刚刚训练得到的模型进行评估

python tools/eval.py -n  yolox-s -c YOLOX_outputs/yolox_s/best_ckpt.pth -b 64 -d 4 --conf 0.001 --fp16 --fuse

🟨 单卡 batch_size = 1 进行评估

目的是：To reproduce speed test 【复现官方测试速度，当然显卡一致才有的比】

python tools/eval.py -n  yolox-s -c preModels/yolox_s.pth -b 1 -d 1 --conf 0.001 --fp16 --fuse

输出如下
单卡 GPU 占用 1319MiB

...
100%|##########| 5000/5000 [01:26<00:00, 57.98it/s]

Running per image evaluation...
Evaluate annotation type *bbox*
COCOeval_opt.evaluate() finished in 15.67 seconds.
Accumulating evaluation results...
COCOeval_opt.accumulate() finished in 2.04 seconds.
2021-08-25 07:49:54 | INFO     | __main__:187 - 
Average forward time: 12.25 ms, Average NMS time: 1.83 ms, Average inference time: 14.08 ms
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.405
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.593
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.437
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.232
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.448
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.541
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.326
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.531
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.574
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.365
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.634
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.724

未完，本专栏持续更新，感谢三连

YOLOX专栏：内容详尽、实用易懂

🚀🚀 墨理学AI

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YOLOX专栏：内容详尽、实用易懂

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YOLOX专栏：内容详尽、实用易懂

📔 认识 YOLOX

📕 环境搭建

📗 测试

🟧 单图CPU测试

🟨 单图GPU测试

🟦 video GPU测试

📘 Reproduce results on COCO 【COCO训练】

🔴 COCO数据集安置

🔵 vim tools/train.py 查看关键参数配置

🟣 训练命令

🟡 训练输出

🔴 训练 10 个 epoch 统计训练时长

🔵 tensorboard 可视化查看 loss 曲线

🟣 Resume 训练

🟡 多机器联合训练

📙 Evaluation

🟧 batch testing for fast evaluation: 【批量快速测试】

🟨 单卡 batch_size = 1 进行评估

🚀🚀 墨理学AI

Gradio：多输入输出简单示例和块的灵活性和控制

昇腾 auto tune 操作指南

GeoSpy.ai

即梦Dreamina

Globe Explorer

Luma Dream Machine

抖音即创

Motionshop

归档

{{userData.name}}已认证

📔 认识 YOLOX

📕 环境搭建

📗 测试

🟧 单图CPU测试

🟨 单图GPU测试

🟦 video GPU测试

📘 Reproduce results on COCO 【COCO训练】

🔴 COCO数据集安置

🔵 vim tools/train.py 查看关键参数配置

🟣 训练命令

🟡 训练输出

🔴 训练 10 个 epoch 统计训练时长

🔵 tensorboard 可视化查看 loss 曲线

🟣 Resume 训练

🟡 多机器联合训练

📙 Evaluation

🟧 batch testing for fast evaluation: 【批量快速测试】

🟨 单卡 batch_size = 1 进行评估

🚀🚀 墨理学AI

Gradio：多输入输出简单示例和块的灵活性和控制

昇腾 auto tune 操作指南

使用YOLOv5进行实时车辆识别

如何在yolox中添加车型分类和单双轮检测

LabVIEW实现YOLOX目标检测教程

YOLOX: 2021年超越YOLO系列的新一代目标检测算法