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readme_ppyoloe.txt
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pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
----------------------- 转换权重 -----------------------
wget https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_300e_coco.pdparams
wget https://paddledet.bj.bcebos.com/models/ppyoloe_crn_m_300e_coco.pdparams
wget https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_300e_coco.pdparams
wget https://paddledet.bj.bcebos.com/models/ppyoloe_crn_x_300e_coco.pdparams
wget https://paddledet.bj.bcebos.com/models/pretrained/CSPResNetb_s_pretrained.pdparams
wget https://paddledet.bj.bcebos.com/models/pretrained/CSPResNetb_m_pretrained.pdparams
wget https://paddledet.bj.bcebos.com/models/pretrained/CSPResNetb_l_pretrained.pdparams
wget https://paddledet.bj.bcebos.com/models/pretrained/CSPResNetb_x_pretrained.pdparams
wget https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco.pdparams
wget https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_m_80e_coco.pdparams
wget https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams
wget https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_x_80e_coco.pdparams
wget https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_s_obj365_pretrained.pdparams
wget https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_m_obj365_pretrained.pdparams
wget https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_l_obj365_pretrained.pdparams
wget https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_x_obj365_pretrained.pdparams
python tools/convert_weights.py -f exps/ppyoloe/ppyoloe_crn_s_300e_coco.py -c ppyoloe_crn_s_300e_coco.pdparams -oc ppyoloe_crn_s_300e_coco.pth -nc 80
python tools/convert_weights.py -f exps/ppyoloe/ppyoloe_crn_m_300e_coco.py -c ppyoloe_crn_m_300e_coco.pdparams -oc ppyoloe_crn_m_300e_coco.pth -nc 80
python tools/convert_weights.py -f exps/ppyoloe/ppyoloe_crn_l_300e_coco.py -c ppyoloe_crn_l_300e_coco.pdparams -oc ppyoloe_crn_l_300e_coco.pth -nc 80
python tools/convert_weights.py -f exps/ppyoloe/ppyoloe_crn_x_300e_coco.py -c ppyoloe_crn_x_300e_coco.pdparams -oc ppyoloe_crn_x_300e_coco.pth -nc 80
python tools/convert_weights.py -f exps/ppyoloe/ppyoloe_crn_s_300e_coco.py -c CSPResNetb_s_pretrained.pdparams -oc CSPResNetb_s_pretrained.pth -nc 80 --only_backbone True
python tools/convert_weights.py -f exps/ppyoloe/ppyoloe_crn_m_300e_coco.py -c CSPResNetb_m_pretrained.pdparams -oc CSPResNetb_m_pretrained.pth -nc 80 --only_backbone True
python tools/convert_weights.py -f exps/ppyoloe/ppyoloe_crn_l_300e_coco.py -c CSPResNetb_l_pretrained.pdparams -oc CSPResNetb_l_pretrained.pth -nc 80 --only_backbone True
python tools/convert_weights.py -f exps/ppyoloe/ppyoloe_crn_x_300e_coco.py -c CSPResNetb_x_pretrained.pdparams -oc CSPResNetb_x_pretrained.pth -nc 80 --only_backbone True
python tools/convert_weights.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_s_80e_coco.py -c ppyoloe_plus_crn_s_80e_coco.pdparams -oc ppyoloe_plus_crn_s_80e_coco.pth -nc 80
python tools/convert_weights.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_m_80e_coco.py -c ppyoloe_plus_crn_m_80e_coco.pdparams -oc ppyoloe_plus_crn_m_80e_coco.pth -nc 80
python tools/convert_weights.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_l_80e_coco.py -c ppyoloe_plus_crn_l_80e_coco.pdparams -oc ppyoloe_plus_crn_l_80e_coco.pth -nc 80
python tools/convert_weights.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_x_80e_coco.py -c ppyoloe_plus_crn_x_80e_coco.pdparams -oc ppyoloe_plus_crn_x_80e_coco.pth -nc 80
python tools/convert_weights.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_s_80e_coco.py -c ppyoloe_crn_s_obj365_pretrained.pdparams -oc ppyoloe_crn_s_obj365_pretrained.pth -nc 365
python tools/convert_weights.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_m_80e_coco.py -c ppyoloe_crn_m_obj365_pretrained.pdparams -oc ppyoloe_crn_m_obj365_pretrained.pth -nc 365
python tools/convert_weights.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_l_80e_coco.py -c ppyoloe_crn_l_obj365_pretrained.pdparams -oc ppyoloe_crn_l_obj365_pretrained.pth -nc 365
python tools/convert_weights.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_x_80e_coco.py -c ppyoloe_crn_x_obj365_pretrained.pdparams -oc ppyoloe_crn_x_obj365_pretrained.pth -nc 365
----------------------- 预测 -----------------------
python tools/demo.py image -f exps/ppyoloe/ppyoloe_crn_s_300e_coco.py -c ppyoloe_crn_s_300e_coco.pth --path assets/000000000019.jpg --conf 0.15 --tsize 640 --save_result --device gpu
python tools/demo.py image -f exps/ppyoloe/ppyoloe_crn_m_300e_coco.py -c ppyoloe_crn_m_300e_coco.pth --path assets/000000000019.jpg --conf 0.15 --tsize 640 --save_result --device gpu
python tools/demo.py image -f exps/ppyoloe/ppyoloe_crn_l_300e_coco.py -c ppyoloe_crn_l_300e_coco.pth --path assets/000000000019.jpg --conf 0.15 --tsize 640 --save_result --device gpu
python tools/demo.py image -f exps/ppyoloe/ppyoloe_crn_x_300e_coco.py -c ppyoloe_crn_x_300e_coco.pth --path assets/000000000019.jpg --conf 0.15 --tsize 640 --save_result --device gpu
python tools/demo.py image -f exps/ppyoloe/ppyoloe_crn_l_voc2012.py -c 6.pth --path assets/000000000019.jpg --conf 0.15 --tsize 640 --save_result --device gpu
python tools/demo.py image -f exps/ppyoloe_plus/ppyoloe_plus_crn_s_80e_coco.py -c ppyoloe_plus_crn_s_80e_coco.pth --path assets/000000000019.jpg --conf 0.15 --tsize 640 --save_result --device gpu
----------------------- 迁移学习，带上-c（--ckpt）参数读取预训练模型。 -----------------------
后台启动：
nohup xxx > ppyolo.log 2>&1 &
复现paddle版ppyoloe_s迁移学习（不冻结骨干网络）:（可以加--fp16， -eb表示验证时的批大小）
python tools/train.py -f exps/ppyoloe/ppyoloe_crn_s_voc2012.py -d 1 -b 4 -eb 2 -c ppyoloe_crn_s_300e_coco.pth --fp16
python tools/eval.py -f exps/ppyoloe/ppyoloe_crn_s_voc2012.py -d 1 -b 4 -c 16.pth --conf 0.01 --tsize 640
python tools/demo.py image -f exps/ppyoloe/ppyoloe_crn_s_voc2012.py -c 16.pth --path assets/000000000019.jpg --conf 0.15 --tsize 640 --save_result --device gpu
2机2卡训练：(发现一个隐藏知识点：获得损失（训练）、推理 都要放在模型的forward()中进行，否则DDP会计算错误结果。)
export CUDA_VISIBLE_DEVICES=0
python tools/train.py -f exps/ppyoloe/ppyoloe_crn_s_voc2012.py --dist-url tcp://192.168.0.106:12312 --num_machines 2 --machine_rank 0 -b 4 -eb 2 -c ppyoloe_crn_s_300e_coco.pth --fp16
1机2卡训练：(发现一个隐藏知识点：获得损失（训练）、推理 都要放在模型的forward()中进行，否则DDP会计算错误结果。)
export CUDA_VISIBLE_DEVICES=0,1
nohup python tools/train.py -f exps/ppyoloe/ppyoloe_crn_s_voc2012.py -d 2 -b 24 -eb 24 -w 4 -ew 4 -lrs 0.7 -c ppyoloe_crn_s_300e_coco.pth > ppyoloe_s.log 2>&1 &
tail -n 20 ppyoloe_s.log
实测ppyoloe_s的AP(0.50:0.95)可以到达0.48+、AP(0.50)可以到达0.68+、AP(small)可以到达0.15+。
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训练 ppyoloe_xs
python tools/train.py -f exps/ppyoloe/ppyoloe_crn_xs_voc2012.py -d 1 -b 4 -eb 2 -c ppyoloe_crn_s_300e_coco.pth
实测ppyoloe_xs的AP(0.50:0.95)可以到达0.xx+、AP(0.50)可以到达0.xx+、AP(small)可以到达0.xx+。
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复现paddle版ppyoloe_l迁移学习（冻结了骨干网络）:（可以加--fp16， -eb表示验证时的批大小）
python tools/train.py -f exps/ppyoloe/ppyoloe_crn_l_voc2012.py -d 1 -b 8 -eb 2 -c ppyoloe_crn_l_300e_coco.pth --fp16
python tools/eval.py -f exps/ppyoloe/ppyoloe_crn_l_voc2012.py -d 1 -b 4 -c 16.pth --conf 0.01 --tsize 640
python tools/demo.py image -f exps/ppyoloe/ppyoloe_crn_l_voc2012.py -c 16.pth --path assets/000000000019.jpg --conf 0.15 --tsize 640 --save_result --device gpu
1机2卡训练：(发现一个隐藏知识点：获得损失（训练）、推理 都要放在模型的forward()中进行，否则DDP会计算错误结果。)
export CUDA_VISIBLE_DEVICES=0,1
nohup python tools/train.py -f exps/ppyoloe/ppyoloe_crn_l_voc2012.py -d 2 -b 8 -eb 2 -c ppyoloe_crn_l_300e_coco.pth --fp16 > ppyoloe_l.log 2>&1 &
tail -n 20 ppyoloe_l.log
实测ppyoloe_l的AP(0.50:0.95)可以到达0.66+、AP(0.50)可以到达0.85+、AP(small)可以到达0.28+。
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感兴趣的同学可以加载 CSPResNetb_l_pretrained.pth 从头训练ppyoloe_l（voc2012数据集）：
python tools/train.py -f exps/ppyoloe/ppyoloe_crn_l_voc2012.py -d 1 -b 8 -eb 2 -c CSPResNetb_l_pretrained.pth --fp16
训练完16个epoch后，实测ppyoloe_l的AP(0.50:0.95)可以到达0.05+、AP(0.50)可以到达0.16+、AP(small)可以到达0.00+。
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ppyoloe_plus_s迁移学习（不冻结骨干网络）:（可以加--fp16， -eb表示验证时的批大小）
python tools/eval.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_s_voc2012.py -d 1 -b 4 -c 16.pth --conf 0.01 --tsize 640
python tools/demo.py image -f exps/ppyoloe_plus/ppyoloe_plus_crn_s_voc2012.py -c 16.pth --path assets/000000000019.jpg --conf 0.15 --tsize 640 --save_result --device gpu
1机2卡训练：(发现一个隐藏知识点：获得损失（训练）、推理 都要放在模型的forward()中进行，否则DDP会计算错误结果。)
export CUDA_VISIBLE_DEVICES=0,1
nohup python tools/train.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_s_voc2012.py -d 2 -b 16 -eb 8 -w 4 -ew 4 -lrs 1.0 -c ppyoloe_crn_s_obj365_pretrained.pth --fp16 > ppyoloe_plus_s_from_obj365.log 2>&1 &
tail -n 20 ppyoloe_plus_s_from_obj365.log
实测 ppyoloe_plus_s_from_obj365 的AP(0.50:0.95)可以到达0.59+、AP(0.50)可以到达0.78+、AP(small)可以到达0.20+。
日志见 train_ppyolo_in_voc2012/mmdet_ppyoloe_plus_s_from_obj365_2gpu.txt
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读 COCO 预训练模型（实际上由obj365 fine-tune 得到）进行fine-tune：
export CUDA_VISIBLE_DEVICES=0,1
nohup python tools/train.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_s_voc2012.py -d 2 -b 16 -eb 8 -w 4 -ew 4 -lrs 1.0 -c ppyoloe_plus_crn_s_80e_coco.pth > ppyoloe_plus_s_from_coco.log 2>&1 &
实测 ppyoloe_plus_s_from_coco 的AP(0.50:0.95)可以到达0.62+、AP(0.50)可以到达0.81+、AP(small)可以到达0.22+。
日志见(这是在完全空闲的服务器，2卡4090测速) train_ppyolo_in_voc2012/mmdet_ppyoloe_plus_s_from_obj365(to_coco)_2gpu.txt
tensorboard --logdir=./PPYOLOEPlus_outputs/
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做 ppyoloe_s 的对比实验（读 ppyoloe 的模型）：
export CUDA_VISIBLE_DEVICES=0,1
nohup python tools/train.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_s_voc2012.py -d 2 -b 16 -eb 8 -w 4 -ew 4 -lrs 1.0 -c ppyoloe_crn_s_300e_coco.pth --fp16 > ppyoloe_s_from_coco.log 2>&1 &
实测 ppyoloe_s_from_coco 的AP(0.50:0.95)可以到达0.61+、AP(0.50)可以到达0.80+、AP(small)可以到达0.23+。
日志见 train_ppyolo_in_voc2012/mmdet_ppyoloe_plus_s_from_coco_2gpu.txt
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pcp:
export CUDA_VISIBLE_DEVICES=0,1
nohup python tools/train.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_s_pcp.py -d 2 -b 24 -eb 8 -c ppyoloe_crn_s_obj365_pretrained.pth > ppyoloe_plus_s_from_obj365.log 2>&1 &
实测 ppyoloe_plus_s_from_obj365 的AP(0.50:0.95)可以到达0.xx+、AP(0.50)可以到达0.xx+、AP(small)可以到达0.xx+。
日志见 train_ppyolo_in_voc2012/xxxxxxxxxx.txt
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读 COCO 预训练模型（实际上由obj365 fine-tune 得到）进行fine-tune：
export CUDA_VISIBLE_DEVICES=0,1
nohup python tools/train.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_s_pcp.py -d 2 -b 24 -eb 8 -c ppyoloe_plus_crn_s_80e_coco.pth > ppyoloe_plus_s_from_coco.log 2>&1 &
实测 ppyoloe_plus_s_from_coco 的AP(0.50:0.95)可以到达0.xx+、AP(0.50)可以到达0.xx+、AP(small)可以到达0.xx+。
日志见 train_ppyolo_in_voc2012/xxxxxxxxxx.txt
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做 ppyoloe_s 的对比实验（读 ppyoloe 的模型）：
export CUDA_VISIBLE_DEVICES=0,1
nohup python tools/train.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_s_pcp.py -d 2 -b 24 -eb 8 -c ppyoloe_crn_s_300e_coco.pth > ppyoloe_s_from_coco.log 2>&1 &
实测 ppyoloe_s_from_coco 的AP(0.50:0.95)可以到达0.xx+、AP(0.50)可以到达0.xx+、AP(small)可以到达0.xx+。
日志见 train_ppyolo_in_voc2012/xxxxxxxxxx.txt
----------------------- 知识蒸馏 -----------------------
蒸馏损失有 F.binary_cross_entropy, 所以不使用 fp16, 除非 F.binary_cross_entropy 前转成float32 且 使用 with torch.cuda.amp.autocast(enabled=False):
1.先训练老师模型:
读 COCO 预训练模型（实际上由obj365 fine-tune 得到）进行fine-tune：
export CUDA_VISIBLE_DEVICES=0,1
nohup python tools/train.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_l_voc2012.py -d 2 -b 16 -eb 8 -w 4 -ew 4 -lrs 1.0 -c ppyoloe_plus_crn_l_80e_coco.pth > ppyoloe_plus_l_from_coco.log 2>&1 &
实测 ppyoloe_plus_l_from_coco 的AP(0.50:0.95)可以到达0.715+、AP(0.50)可以到达0.881+、AP(small)可以到达0.314+。
日志见 train_ppyolo_in_voc2012/ppyoloe_plus_l_from_obj365(to_coco)_2gpu.txt
2.蒸馏:
读 COCO 预训练模型（实际上由obj365 fine-tune 得到）进行fine-tune：
export CUDA_VISIBLE_DEVICES=0,1
nohup python tools/train.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_s_voc2012.py -d 2 -b 16 -eb 8 -w 4 -ew 4 -lrs 1.0 -c ppyoloe_plus_crn_s_80e_coco.pth -sf exps/slim/distill/ppyoloe_plus_crn_l_voc2012_l2s.py -sc PPYOLOEPlus_outputs/ppyoloe_plus_crn_l_voc2012/16.pth > ppyoloe_plus_s_from_coco.log 2>&1 &
恢复训练:
nohup python tools/train.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_s_voc2012.py -d 2 -b 16 -eb 8 -w 4 -ew 4 -lrs 1.0 -c 7.pth --resume -sf exps/slim/distill/ppyoloe_plus_crn_l_voc2012_l2s.py -sc PPYOLOEPlus_outputs/ppyoloe_plus_crn_l_voc2012/16.pth > ppyoloe_plus_s_from_coco.log 2>&1 &
(单卡调试)
python tools/train.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_s_voc2012.py -d 1 -b 4 -eb 4 -w 1 -ew 1 -lrs 1.0 -c ppyoloe_plus_crn_s_80e_coco.pth -sf exps/slim/distill/ppyoloe_plus_crn_l_voc2012_l2s.py -sc PPYOLOEPlus_outputs/ppyoloe_plus_crn_l_voc2012/16.pth
实测 ppyoloe_plus_s_from_coco 的AP(0.50:0.95)可以到达0.xxx+、AP(0.50)可以到达0.xxx+、AP(small)可以到达0.xxx+。
日志见 train_ppyolo_in_voc2012/ppyoloe_plus_s_distill_l2s.txt
仔细检查：
单卡和多卡时，PPdetModelEMA 的 state_dict 不保存老师的权重，训练时ema也不更新老师的权重
单卡和多卡训练时时，老师的权重不被训练，bn的均值和方差也不会变化
evaluate_and_save_model()
aaaaaaaa.py 对比查看 老师权重的变化
----------------------- 恢复训练（加上参数--resume） -----------------------
----------------------- 评估 -----------------------
python tools/eval.py -f exps/ppyoloe/ppyoloe_crn_s_300e_coco.py -d 1 -b 8 -w 4 -c ppyoloe_crn_s_300e_coco.pth --conf 0.01 --tsize 640
Average forward time: 21.56 ms, Average NMS time: 0.00 ms, Average inference time: 21.56 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.423
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.593
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.458
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.582
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.336
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.546
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.583
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.356
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.640
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.756
python tools/eval.py -f exps/ppyoloe/ppyoloe_crn_s_300e_coco.py -d 1 -b 8 -w 4 -c ppyoloe_crn_s_300e_coco.pth --conf 0.01 --tsize 320
Average forward time: 14.12 ms, Average NMS time: 0.00 ms, Average inference time: 14.12 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.341
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.491
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.362
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.117
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.378
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.571
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.288
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.450
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.477
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.174
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.549
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.744
python tools/eval.py -f exps/ppyoloe/ppyoloe_crn_m_300e_coco.py -d 1 -b 8 -c ppyoloe_crn_m_300e_coco.pth --conf 0.01 --tsize 640
Average forward time: 35.90 ms, Average NMS time: 0.00 ms, Average inference time: 35.90 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.482
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.654
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.523
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.294
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.527
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.645
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.365
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.597
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.635
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.427
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.687
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.803
python tools/eval.py -f exps/ppyoloe/ppyoloe_crn_l_300e_coco.py -d 1 -b 4 -c ppyoloe_crn_l_300e_coco.pth --conf 0.01 --tsize 640
Average forward time: 49.45 ms, Average NMS time: 0.00 ms, Average inference time: 49.45 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.505
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.681
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.547
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.335
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.552
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 ] = 0.377
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.617
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.657
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.461
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.709
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.819
python tools/eval.py -f exps/ppyoloe/ppyoloe_crn_x_300e_coco.py -d 1 -b 2 -c ppyoloe_crn_x_300e_coco.pth --conf 0.01 --tsize 640
Average forward time: 79.69 ms, Average NMS time: 0.00 ms, Average inference time: 79.70 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.515
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.690
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.555
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.338
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 ] = 0.682
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.383
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.628
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.669
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.484
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.718
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.823
python tools/eval.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_s_80e_coco.py -d 1 -b 8 -c ppyoloe_plus_crn_s_80e_coco.pth --conf 0.01 --tsize 640
Average forward time: 15.57 ms, Average NMS time: 0.00 ms, Average inference time: 15.57 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.431
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.599
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.474
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.256
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.469
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.583
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.342
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.567
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.625
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.424
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.688
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.777
python tools/eval.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_m_80e_coco.py -d 1 -b 8 -c ppyoloe_plus_crn_m_80e_coco.pth --conf 0.01 --tsize 640
Average forward time: 20.78 ms, Average NMS time: 0.00 ms, Average inference time: 20.78 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.492
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.663
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.539
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.306
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.534
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.656
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.373
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.614
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.673
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.491
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.728
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.823
python tools/eval.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_l_80e_coco.py -d 1 -b 8 -c ppyoloe_plus_crn_l_80e_coco.pth --conf 0.01 --tsize 640
Average forward time: 22.93 ms, Average NMS time: 0.00 ms, Average inference time: 22.93 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.523
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.694
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.573
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.344
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.571
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.685
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.385
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.641
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.699
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.521
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.755
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.846
python tools/eval.py -f exps/ppyoloe_plus/ppyoloe_plus_crn_x_80e_coco.py -d 1 -b 8 -c ppyoloe_plus_crn_x_80e_coco.pth --conf 0.01 --tsize 640
Average forward time: 27.18 ms, Average NMS time: 0.00 ms, Average inference time: 27.18 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.542
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.712
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.594
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.372
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.587
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.699
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.394
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.657
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.711
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.542
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.765
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.851
----------------------- 复现COCO上的精度 -----------------------
(5)ppyoloe_crn_s_300e_coco
(
PaddleDetection-release-2.4 的总批大小是 256=8*32
PaddleDetection-release-2.6 的总批大小是 64=8*8
另外，加上--fp16实测会导致loss出现nan，所以不加，不使用混合精度训练。
)
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
nohup python tools/train.py -f exps/ppyoloe/ppyoloe_crn_s_300e_coco.py -d 8 -b 64 -eb 64 -w 4 -ew 4 -c CSPResNetb_s_pretrained.pth > ppyoloe_crn_s_300e_coco_8gpu.log 2>&1 &
训练日志见 train_coco/ppyoloe_s_8gpu.txt
实测训300 epochs后，最高mAP为42.10，基本上能达到转换的官方权重( Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.423)
python tools/eval.py -f exps/ppyoloe/ppyoloe_crn_s_300e_coco.py -d 8 -b 64 -c 300.pth --conf 0.01 --tsize 640
只有双卡的时候：
export CUDA_VISIBLE_DEVICES=0,1
nohup python tools/train.py -f exps/ppyoloe/ppyoloe_crn_s_300e_coco.py -d 2 -b 16 -eb 16 -w 4 -ew 4 -c CSPResNetb_s_pretrained.pth > ppyoloe_crn_s_300e_coco_2gpu.log 2>&1 &
----------------------- 导出为ncnn -----------------------
python tools/demo.py ncnn -f exps/ppyoloe/ppyoloe_crn_s_300e_coco.py -c ppyoloe_crn_s_300e_coco.pth --ncnn_output_path ppyoloe_crn_s_300e_coco
python tools/demo.py ncnn -f exps/ppyoloe/ppyoloe_crn_m_300e_coco.py -c ppyoloe_crn_m_300e_coco.pth --ncnn_output_path ppyoloe_crn_m_300e_coco
python tools/demo.py ncnn -f exps/ppyoloe/ppyoloe_crn_l_300e_coco.py -c ppyoloe_crn_l_300e_coco.pth --ncnn_output_path ppyoloe_crn_l_300e_coco
python tools/demo.py ncnn -f exps/ppyoloe/ppyoloe_crn_x_300e_coco.py -c ppyoloe_crn_x_300e_coco.pth --ncnn_output_path ppyoloe_crn_x_300e_coco
python tools/demo.py ncnn -f exps/ppyoloe/ppyoloe_crn_l_voc2012.py -c 6.pth --ncnn_output_path ppyoloe_crn_l_voc2012_epoch_6
cd build/examples
./test2_06_ppyoloe_ncnn ../../my_tests/000000000019.jpg ppyoloe_crn_s_300e_coco.param ppyoloe_crn_s_300e_coco.bin
cd build/examples
./test2_06_ppyoloe_ncnn ../../my_tests/000000000019.jpg ppyoloe_crn_m_300e_coco.param ppyoloe_crn_m_300e_coco.bin
cd build/examples
./test2_06_ppyoloe_ncnn ../../my_tests/000000000019.jpg ppyoloe_crn_l_300e_coco.param ppyoloe_crn_l_300e_coco.bin
cd build/examples
./test2_06_ppyoloe_ncnn ../../my_tests/000000000019.jpg ppyoloe_crn_x_300e_coco.param ppyoloe_crn_x_300e_coco.bin
cd build/examples
./test2_06_ppyoloe_ncnn ../../my_tests/000000000019.jpg ppyoloe_crn_l_voc2012_epoch_6.param ppyoloe_crn_l_voc2012_epoch_6.bin
./test2_06_ppyoloe_ncnn ../../my_tests/000000013659.jpg ppyoloe_crn_s_300e_coco.param ppyoloe_crn_s_300e_coco.bin
./test2_06_ppyoloe_ncnn ../../my_tests/000000013659.jpg ppyoloe_crn_m_300e_coco.param ppyoloe_crn_m_300e_coco.bin
./test2_06_ppyoloe_ncnn ../../my_tests/000000013659.jpg ppyoloe_crn_l_300e_coco.param ppyoloe_crn_l_300e_coco.bin
./test2_06_ppyoloe_ncnn ../../my_tests/000000013659.jpg ppyoloe_crn_x_300e_coco.param ppyoloe_crn_x_300e_coco.bin
./test2_06_ppyoloe_ncnn ../../my_tests/000000013659.jpg ppyoloe_crn_l_voc2012_epoch_6.param ppyoloe_crn_l_voc2012_epoch_6.bin