【深度学习】yolov5以及yolov8的微调后的模型精度对比
文章目录
- 前言
- 1. 前置先解决报错 todo
- 2. 训练
-
- 2.1 yolov5 的 yolov5m6
- 2.2 yolov5 的 yolov5l6
- 2.3 yolov8 的
前言
做了一个烟火识别，用了2W张图片，标注包括：fire，smoke 。在coco80类的模型上进行ft， 借此机会进行比较一下。
- yolov5 yolov5m6
- yolov5 yolov5l6
- yolov8 模型待定
图片共：20113 张 按8:1:1 区分train，val 和 test
数据集：
train: Scanning ‘/data_share/data_share/fire_smoke_iter20230720/firesmoketaobao/train.cache’ images and labels… 16090 found, 0 missing, 1019 empty, 0 corrupt: 100%|██████████| 16090/16090
val: Scanning ‘/data_share/data_share/fire_smoke_iter20230720/firesmoketaobao/val.cache’ images and labels… 2011 found, 0 missing, 123 empty, 0 corrupt: 100%|██████████| 2011/2011 [00:00
1. 前置先解决报错 todo
2. 训练
2.1 yolov5 的 yolov5m6
所用命令：
python -m torch.distributed.launch --nproc_per_node=2 train.py --weights weights/yolov5m6_coco.pt --img 640 --epoch 500 --data fire_smoke.yaml --batch-size 24 --workers 8 --save-period 20
资源占用情况：
最终结果：
00%|██████████| 671/671 [02:05<00:00, 4.87it/s] 173/499 7.22G 0.02312 0.01991 0.005421 26 640: 1 00%|██████████| 671/671 [02:05<00:00, 5.35it/s]
Class Images Instances P R mAP50
all 2011 3236 0.81 0.76 0.825 0.556
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
174/499 7.22G 0.02303 0.01993 0.005404 16 640: 1
Class Images Instances P R mAP50
all 2011 3236 0.809 0.757 0.824 0.556
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
175/499 7.22G 0.02297 0.01974 0.005403 14 640: 1
Class Images Instances P R mAP50
all 2011 3236 0.808 0.758 0.823 0.556
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
176/499 7.22G 0.02281 0.02 0.005223 17 640: 1
Class Images Instances P R mAP50
all 2011 3236 0.807 0.759 0.823 0.555
Stopping training early as no improvement observed in last 100 epochs. Best resu lts observed at epoch 76, best model saved as best.pt.
To update EarlyStopping(patience=100) pass a new patience value, i.e. `python tr ain.py --patience 300` or use `--patience 0` to disable EarlyStopping.
177 epochs completed in 6.973 hours.
Optimizer stripped from runs/train/exp5/weights/last.pt, 71.1MB
Optimizer stripped from runs/train/exp5/weights/best.pt, 71.1MB
Validating runs/train/exp5/weights/best.pt...
Fusing layers...
Model summary: 276 layers, 35254692 parameters, 0 gradients, 49.0 GFLOPs
Class Images Instances P R mAP50
all 2011 3236 0.815 0.75 0.829 0.559
fire 2011 1791 0.792 0.724 0.801 0.527
smoke 2011 1445 0.839 0.777 0.857 0.59
Results saved to runs/train/exp5
2.2 yolov5 的 yolov5l6
所用命令
nohup python -m torch.distributed.launch --nproc_per_node=2 train.py --weights weights/yolov5l6.pt --img 640 --epoch 500 --data fire_smoke.yaml --batch-size 24 --workers 8 --save-period 20 >yolov5l6.log 2>&1 &
相同的参数，large模型是比较吃显存的
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 26007 C ...39_torch1.10.1/bin/python 9325MiB |
| 1 N/A N/A 26008 C ...39_torch1.10.1/bin/python 8835MiB |
+-----------------------------------------------------------------------------+
Fri Jul 21 17:28:53 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.63.01 Driver Version: 470.63.01 CUDA Version: 11.4 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:0B:00.0 Off | N/A |
| 96% 69C P2 314W / 350W | 9331MiB / 12053MiB | 97% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 1 NVIDIA GeForce ... Off | 00000000:1B:00.0 Off | N/A |
| 88% 65C P2 309W / 350W | 8837MiB / 12053MiB | 90% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 26007 C ...39_torch1.10.1/bin/python 9325MiB |
| 1 N/A N/A 26008 C ...39_torch1.10.1/bin/python 8835MiB |
+-----------------------------------------------------------------------------+