Megvii-BaseDetection
Training AP is always 0 using coco128 dataset
I used the coco128 and unzip into the datasets folder. The command is python tools/train.py -f exps/example/custom/yolox_s.py -d 1 -b 32 --fp16 -o -c C:\Users\olivi\YOLOX\pretrained\yolox_s.pth However, the APs have a lot of nan and 0. The log details are below.
2024-03-03 15:05:28 | INFO | yolox.core.trainer:130 - args: Namespace(experiment_name='yolox_s', name=None, dist_backend='nccl', dist_url=None, batch_size=32, devices=1, exp_file='exps/example/custom/yolox_s.py', resume=False, ckpt='C:\Users\olivi\YOLOX\pretrained\yolox_s.pth', start_epoch=None, num_machines=1, machine_rank=0, fp16=True, cache=None, occupy=True, logger='tensorboard', opts=[]) 2024-03-03 15:05:28 | INFO | yolox.core.trainer:131 - exp value: ╒═══════════════════╤════════════════════════════╕ │ keys │ values │ ╞═══════════════════╪════════════════════════════╡ │ seed │ None │ ├───────────────────┼────────────────────────────┤ │ output_dir │ './YOLOX_outputs' │ ├───────────────────┼────────────────────────────┤ │ print_interval │ 10 │ ├───────────────────┼────────────────────────────┤ │ eval_interval │ 1 │ ├───────────────────┼────────────────────────────┤ │ dataset │ None │ ├───────────────────┼────────────────────────────┤ │ num_classes │ 71 │ ├───────────────────┼────────────────────────────┤ │ depth │ 0.33 │ ├───────────────────┼────────────────────────────┤ │ width │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ act │ 'silu' │ ├───────────────────┼────────────────────────────┤ │ data_num_workers │ 4 │ ├───────────────────┼────────────────────────────┤ │ input_size │ (640, 640) │ ├───────────────────┼────────────────────────────┤ │ multiscale_range │ 5 │ ├───────────────────┼────────────────────────────┤ │ data_dir │ 'datasets/coco128' │ ├───────────────────┼────────────────────────────┤ │ train_ann │ 'instances_train2017.json' │ ├───────────────────┼────────────────────────────┤ │ val_ann │ 'instances_val2017.json' │ ├───────────────────┼────────────────────────────┤ │ test_ann │ 'instances_test2017.json' │ ├───────────────────┼────────────────────────────┤ │ mosaic_prob │ 1.0 │ ├───────────────────┼────────────────────────────┤ │ mixup_prob │ 1.0 │ ├───────────────────┼────────────────────────────┤ │ hsv_prob │ 1.0 │ ├───────────────────┼────────────────────────────┤ │ flip_prob │ 0.5 │ ├───────────────────┼────────────────────────────┤ │ degrees │ 10.0 │ ├───────────────────┼────────────────────────────┤ │ translate │ 0.1 │ ├───────────────────┼────────────────────────────┤ │ mosaic_scale │ (0.1, 2) │ ├───────────────────┼────────────────────────────┤ │ enable_mixup │ True │ ├───────────────────┼────────────────────────────┤ │ mixup_scale │ (0.5, 1.5) │ ├───────────────────┼────────────────────────────┤ │ shear │ 2.0 │ ├───────────────────┼────────────────────────────┤ │ warmup_epochs │ 5 │ ├───────────────────┼────────────────────────────┤ │ max_epoch │ 300 │ ├───────────────────┼────────────────────────────┤ │ warmup_lr │ 0 │ ├───────────────────┼────────────────────────────┤ │ min_lr_ratio │ 0.05 │ ├───────────────────┼────────────────────────────┤ │ basic_lr_per_img │ 0.00015625 │ ├───────────────────┼────────────────────────────┤ │ scheduler │ 'yoloxwarmcos' │ ├───────────────────┼────────────────────────────┤ │ no_aug_epochs │ 15 │ ├───────────────────┼────────────────────────────┤ │ ema │ True │ ├───────────────────┼────────────────────────────┤ │ weight_decay │ 0.0005 │ ├───────────────────┼────────────────────────────┤ │ momentum │ 0.9 │ ├───────────────────┼────────────────────────────┤ │ save_history_ckpt │ True │ ├───────────────────┼────────────────────────────┤ │ exp_name │ 'yolox_s' │ ├───────────────────┼────────────────────────────┤ │ test_size │ (640, 640) │ ├───────────────────┼────────────────────────────┤ │ test_conf │ 0.01 │ ├───────────────────┼────────────────────────────┤ │ nmsthre │ 0.65 │ ╘═══════════════════╧════════════════════════════╛ qt.qpa.fonts: Unable to open default EUDC font: "EUDC.TTE" 2024-03-03 15:05:34 | INFO | yolox.core.trainer:136 - Model Summary: Params: 8.96M, Gflops: 26.91 2024-03-03 15:05:34 | INFO | yolox.core.trainer:319 - loading checkpoint for fine tuning 2024-03-03 15:05:35 | WARNING | yolox.utils.checkpoint:24 - Shape of head.cls_preds.0.weight in checkpoint is torch.Size([80, 128, 1, 1]), while shape of head.cls_preds.0.weight in model is torch.Size([71, 128, 1, 1]). 2024-03-03 15:05:35 | WARNING | yolox.utils.checkpoint:24 - Shape of head.cls_preds.0.bias in checkpoint is torch.Size([80]), while shape of head.cls_preds.0.bias in model is torch.Size([71]). 2024-03-03 15:05:35 | WARNING | yolox.utils.checkpoint:24 - Shape of head.cls_preds.1.weight in checkpoint is torch.Size([80, 128, 1, 1]), while shape of head.cls_preds.1.weight in model is torch.Size([71, 128, 1, 1]). 2024-03-03 15:05:35 | WARNING | yolox.utils.checkpoint:24 - Shape of head.cls_preds.1.bias in checkpoint is torch.Size([80]), while shape of head.cls_preds.1.bias in model is torch.Size([71]). 2024-03-03 15:05:35 | WARNING | yolox.utils.checkpoint:24 - Shape of head.cls_preds.2.weight in checkpoint is torch.Size([80, 128, 1, 1]), while shape of head.cls_preds.2.weight in model is torch.Size([71, 128, 1, 1]). 2024-03-03 15:05:35 | WARNING | yolox.utils.checkpoint:24 - Shape of head.cls_preds.2.bias in checkpoint is torch.Size([80]), while shape of head.cls_preds.2.bias in model is torch.Size([71]). 2024-03-03 15:05:35 | INFO | yolox.data.datasets.coco:63 - loading annotations into memory... 2024-03-03 15:05:35 | INFO | yolox.data.datasets.coco:63 - Done (t=0.00s) 2024-03-03 15:05:35 | INFO | pycocotools.coco:86 - creating index... 2024-03-03 15:05:35 | INFO | pycocotools.coco:86 - index created! 2024-03-03 15:05:35 | INFO | yolox.core.trainer:155 - init prefetcher, this might take one minute or less... C:\Users\olivi\yolox\yolox\utils\metric.py:43: UserWarning: The torch.cuda.DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=, device='cuda') to create tensors. (Triggered internally at ..\torch\csrc\tensor\python_tensor.cpp:85.) x = torch.cuda.FloatTensor(256, 1024, block_mem) 2024-03-03 15:05:52 | INFO | yolox.data.datasets.coco:63 - loading annotations into memory... 2024-03-03 15:05:52 | INFO | yolox.data.datasets.coco:63 - Done (t=0.01s) 2024-03-03 15:05:52 | INFO | pycocotools.coco:86 - creating index... 2024-03-03 15:05:52 | INFO | pycocotools.coco:86 - index created! 2024-03-03 15:05:52 | INFO | yolox.core.trainer:191 - Training start... 2024-03-03 15:05:52 | INFO | yolox.core.trainer:192 - YOLOX( (backbone): YOLOPAFPN( (backbone): CSPDarknet( (stem): Focus( (conv): BaseConv( (conv): Conv2d(12, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ) (dark2): Sequential( (0): BaseConv( (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (1): CSPLayer( (conv1): BaseConv( (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv3): BaseConv( (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (m): Sequential( (0): Bottleneck( (conv1): BaseConv( (conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ) ) ) ) (dark3): Sequential( (0): BaseConv( (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (1): CSPLayer( (conv1): BaseConv( (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv3): BaseConv( (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (m): Sequential( (0): Bottleneck( (conv1): BaseConv( (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ) (1): Bottleneck( (conv1): BaseConv( (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ) (2): Bottleneck( (conv1): BaseConv( (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ) ) ) ) (dark4): Sequential( (0): BaseConv( (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (1): CSPLayer( (conv1): BaseConv( (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv3): BaseConv( (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (m): Sequential( (0): Bottleneck( (conv1): BaseConv( (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ) (1): Bottleneck( (conv1): BaseConv( (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ) (2): Bottleneck( (conv1): BaseConv( (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ) ) ) ) (dark5): Sequential( (0): BaseConv( (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (1): SPPBottleneck( (conv1): BaseConv( (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (m): ModuleList( (0): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False) (1): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False) (2): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False) ) (conv2): BaseConv( (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ) (2): CSPLayer( (conv1): BaseConv( (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv3): BaseConv( (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (m): Sequential( (0): Bottleneck( (conv1): BaseConv( (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ) ) ) ) ) (upsample): Upsample(scale_factor=2.0, mode='nearest') (lateral_conv0): BaseConv( (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (C3_p4): CSPLayer( (conv1): BaseConv( (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv3): BaseConv( (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (m): Sequential( (0): Bottleneck( (conv1): BaseConv( (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ) ) ) (reduce_conv1): BaseConv( (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (C3_p3): CSPLayer( (conv1): BaseConv( (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv3): BaseConv( (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (m): Sequential( (0): Bottleneck( (conv1): BaseConv( (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ) ) ) (bu_conv2): BaseConv( (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (C3_n3): CSPLayer( (conv1): BaseConv( (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv3): BaseConv( (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (m): Sequential( (0): Bottleneck( (conv1): BaseConv( (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ) ) ) (bu_conv1): BaseConv( (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (C3_n4): CSPLayer( (conv1): BaseConv( (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv3): BaseConv( (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (m): Sequential( (0): Bottleneck( (conv1): BaseConv( (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (conv2): BaseConv( (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ) ) ) ) (head): YOLOXHead( (cls_convs): ModuleList( (0-2): 3 x Sequential( (0): BaseConv( (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (1): BaseConv( (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ) ) (reg_convs): ModuleList( (0-2): 3 x Sequential( (0): BaseConv( (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (1): BaseConv( (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ) ) (cls_preds): ModuleList( (0-2): 3 x Conv2d(128, 71, kernel_size=(1, 1), stride=(1, 1)) ) (reg_preds): ModuleList( (0-2): 3 x Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1)) ) (obj_preds): ModuleList( (0-2): 3 x Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1)) ) (stems): ModuleList( (0): BaseConv( (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (1): BaseConv( (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) (2): BaseConv( (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) (act): SiLU(inplace=True) ) ) (l1_loss): L1Loss() (bcewithlog_loss): BCEWithLogitsLoss() (iou_loss): IOUloss() ) ) 2024-03-03 15:05:52 | INFO | yolox.core.trainer:203 - ---> start train epoch1 2024-03-03 15:05:59 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s 100%|####################################################################################| 1/1 [00:05<00:00, 5.11s/it] 2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process... 2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results... 2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s) 2024-03-03 15:06:04 | INFO | pycocotools.coco:366 - creating index... 2024-03-03 15:06:04 | INFO | pycocotools.coco:366 - index created! 2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation... 2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox 2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.01s). 2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results... 2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.03s). 2024-03-03 15:06:04 | INFO | yolox.core.trainer:354 - Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.092 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.103 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.103 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.060 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.100 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.069 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.138 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.138 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.180 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.100 per class AP:
| class | AP | class | AP | class | AP |
|---|---|---|---|---|---|
| 0 | nan | 1 | nan | 2 | nan |
| 3 | nan | 4 | nan | 5 | nan |
| 6 | nan | 7 | 0.000 | 8 | nan |
| 9 | nan | 10 | nan | 11 | nan |
| 12 | nan | 13 | nan | 14 | nan |
| 15 | nan | 16 | nan | 17 | nan |
| 18 | 0.000 | 19 | nan | 20 | nan |
| 21 | nan | 22 | nan | 23 | nan |
| 24 | nan | 25 | nan | 26 | 0.000 |
| 27 | nan | 28 | nan | 29 | nan |
| 30 | nan | 31 | nan | 32 | 30.000 |
| 33 | nan | 34 | 0.000 | 35 | nan |
| 36 | nan | 37 | 0.000 | 38 | nan |
| 39 | nan | 40 | nan | 41 | nan |
| 42 | nan | 43 | nan | 44 | 0.000 |
| 45 | nan | 46 | nan | 47 | nan |
| 48 | nan | 49 | 90.000 | 50 | nan |
| 51 | nan | 52 | nan | 53 | nan |
| 54 | nan | 55 | nan | 56 | 0.000 |
| 57 | 0.000 | 58 | nan | 59 | nan |
| 60 | nan | 61 | 0.000 | 62 | nan |
| 63 | nan | 64 | nan | 65 | nan |
| 66 | nan | 67 | nan | 68 | nan |
| 69 | 0.000 | 70 | 0.000 | ||
| per class AR: | |||||
| class | AR | class | AR | class | AR |
| :-------- | :------ | :-------- | :------- | :-------- | :------- |
| 0 | nan | 1 | nan | 2 | nan |
| 3 | nan | 4 | nan | 5 | nan |
| 6 | nan | 7 | 0.000 | 8 | nan |
| 9 | nan | 10 | nan | 11 | nan |
| 12 | nan | 13 | nan | 14 | nan |
| 15 | nan | 16 | nan | 17 | nan |
| 18 | 0.000 | 19 | nan | 20 | nan |
| 21 | nan | 22 | nan | 23 | nan |
| 24 | nan | 25 | nan | 26 | 0.000 |
| 27 | nan | 28 | nan | 29 | nan |
| 30 | nan | 31 | nan | 32 | 90.000 |
| 33 | nan | 34 | 0.000 | 35 | nan |
| 36 | nan | 37 | 0.000 | 38 | nan |
| 39 | nan | 40 | nan | 41 | nan |
| 42 | nan | 43 | nan | 44 | 0.000 |
| 45 | nan | 46 | nan | 47 | nan |
| 48 | nan | 49 | 90.000 | 50 | nan |
| 51 | nan | 52 | nan | 53 | nan |
| 54 | nan | 55 | nan | 56 | 0.000 |
| 57 | 0.000 | 58 | nan | 59 | nan |
| 60 | nan | 61 | 0.000 | 62 | nan |
| 63 | nan | 64 | nan | 65 | nan |
| 66 | nan | 67 | nan | 68 | nan |
| 69 | 0.000 | 70 | 0.000 |
2024-03-03 15:06:04 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s 2024-03-03 15:06:05 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s 2024-03-03 15:06:05 | INFO | yolox.core.trainer:203 - ---> start train epoch2 2024-03-03 15:06:07 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s 100%|####################################################################################| 1/1 [00:05<00:00, 5.63s/it] 2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process... 2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results... 2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s) 2024-03-03 15:06:13 | INFO | pycocotools.coco:366 - creating index... 2024-03-03 15:06:13 | INFO | pycocotools.coco:366 - index created! 2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation... 2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox 2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.01s). 2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results... 2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.03s). 2024-03-03 15:06:13 | INFO | yolox.core.trainer:354 - Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.115 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.154 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.154 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.120 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.100 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.115 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.115 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.115 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.120 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.100 per class AP:
| class | AP | class | AP | class | AP |
|---|---|---|---|---|---|
| 0 | nan | 1 | nan | 2 | nan |
| 3 | nan | 4 | nan | 5 | nan |
| 6 | nan | 7 | 0.000 | 8 | nan |
| 9 | nan | 10 | nan | 11 | nan |
| 12 | nan | 13 | nan | 14 | nan |
| 15 | nan | 16 | nan | 17 | nan |
| 18 | 0.000 | 19 | nan | 20 | nan |
| 21 | nan | 22 | nan | 23 | nan |
| 24 | nan | 25 | nan | 26 | 0.000 |
| 27 | nan | 28 | nan | 29 | nan |
| 30 | nan | 31 | nan | 32 | 0.000 |
| 33 | nan | 34 | 0.000 | 35 | nan |
| 36 | nan | 37 | 0.000 | 38 | nan |
| 39 | nan | 40 | nan | 41 | nan |
| 42 | nan | 43 | nan | 44 | 0.000 |
| 45 | nan | 46 | nan | 47 | nan |
| 48 | nan | 49 | 90.000 | 50 | nan |
| 51 | nan | 52 | nan | 53 | nan |
| 54 | nan | 55 | nan | 56 | 0.000 |
| 57 | 0.000 | 58 | nan | 59 | nan |
| 60 | nan | 61 | 60.000 | 62 | nan |
| 63 | nan | 64 | nan | 65 | nan |
| 66 | nan | 67 | nan | 68 | nan |
| 69 | 0.000 | 70 | 0.000 | ||
| per class AR: | |||||
| class | AR | class | AR | class | AR |
| :-------- | :------ | :-------- | :------- | :-------- | :------ |
| 0 | nan | 1 | nan | 2 | nan |
| 3 | nan | 4 | nan | 5 | nan |
| 6 | nan | 7 | 0.000 | 8 | nan |
| 9 | nan | 10 | nan | 11 | nan |
| 12 | nan | 13 | nan | 14 | nan |
| 15 | nan | 16 | nan | 17 | nan |
| 18 | 0.000 | 19 | nan | 20 | nan |
| 21 | nan | 22 | nan | 23 | nan |
| 24 | nan | 25 | nan | 26 | 0.000 |
| 27 | nan | 28 | nan | 29 | nan |
| 30 | nan | 31 | nan | 32 | 0.000 |
| 33 | nan | 34 | 0.000 | 35 | nan |
| 36 | nan | 37 | 0.000 | 38 | nan |
| 39 | nan | 40 | nan | 41 | nan |
| 42 | nan | 43 | nan | 44 | 0.000 |
| 45 | nan | 46 | nan | 47 | nan |
| 48 | nan | 49 | 90.000 | 50 | nan |
| 51 | nan | 52 | nan | 53 | nan |
| 54 | nan | 55 | nan | 56 | 0.000 |
| 57 | 0.000 | 58 | nan | 59 | nan |
| 60 | nan | 61 | 60.000 | 62 | nan |
| 63 | nan | 64 | nan | 65 | nan |
| 66 | nan | 67 | nan | 68 | nan |
| 69 | 0.000 | 70 | 0.000 |
2024-03-03 15:06:13 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s 2024-03-03 15:06:14 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s 2024-03-03 15:06:14 | INFO | yolox.core.trainer:203 - ---> start train epoch3 2024-03-03 15:06:20 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s 100%|####################################################################################| 1/1 [00:04<00:00, 4.98s/it] 2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process... 2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results... 2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s) 2024-03-03 15:06:25 | INFO | pycocotools.coco:366 - creating index... 2024-03-03 15:06:25 | INFO | pycocotools.coco:366 - index created! 2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation... 2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox 2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.01s). 2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results... 2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.02s). 2024-03-03 15:06:25 | INFO | yolox.core.trainer:354 - Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.031 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.038 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.038 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.044 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.062 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.062 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.062 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.089 per class AP:
| class | AP | class | AP | class | AP |
|---|---|---|---|---|---|
| 0 | nan | 1 | nan | 2 | nan |
| 3 | nan | 4 | nan | 5 | nan |
| 6 | nan | 7 | 0.000 | 8 | nan |
| 9 | nan | 10 | nan | 11 | nan |
| 12 | nan | 13 | nan | 14 | nan |
| 15 | nan | 16 | nan | 17 | nan |
| 18 | 0.000 | 19 | nan | 20 | nan |
| 21 | nan | 22 | nan | 23 | nan |
| 24 | nan | 25 | nan | 26 | 0.000 |
| 27 | nan | 28 | nan | 29 | nan |
| 30 | nan | 31 | nan | 32 | 0.000 |
| 33 | nan | 34 | 0.000 | 35 | nan |
| 36 | nan | 37 | 0.000 | 38 | nan |
| 39 | nan | 40 | nan | 41 | nan |
| 42 | nan | 43 | nan | 44 | 0.000 |
| 45 | nan | 46 | nan | 47 | nan |
| 48 | nan | 49 | 40.000 | 50 | nan |
| 51 | nan | 52 | nan | 53 | nan |
| 54 | nan | 55 | nan | 56 | 0.000 |
| 57 | 0.000 | 58 | nan | 59 | nan |
| 60 | nan | 61 | 0.000 | 62 | nan |
| 63 | nan | 64 | nan | 65 | nan |
| 66 | nan | 67 | nan | 68 | nan |
| 69 | 0.000 | 70 | 0.000 | ||
| per class AR: | |||||
| class | AR | class | AR | class | AR |
| :-------- | :------ | :-------- | :------- | :-------- | :------ |
| 0 | nan | 1 | nan | 2 | nan |
| 3 | nan | 4 | nan | 5 | nan |
| 6 | nan | 7 | 0.000 | 8 | nan |
| 9 | nan | 10 | nan | 11 | nan |
| 12 | nan | 13 | nan | 14 | nan |
| 15 | nan | 16 | nan | 17 | nan |
| 18 | 0.000 | 19 | nan | 20 | nan |
| 21 | nan | 22 | nan | 23 | nan |
| 24 | nan | 25 | nan | 26 | 0.000 |
| 27 | nan | 28 | nan | 29 | nan |
| 30 | nan | 31 | nan | 32 | 0.000 |
| 33 | nan | 34 | 0.000 | 35 | nan |
| 36 | nan | 37 | 0.000 | 38 | nan |
| 39 | nan | 40 | nan | 41 | nan |
| 42 | nan | 43 | nan | 44 | 0.000 |
| 45 | nan | 46 | nan | 47 | nan |
| 48 | nan | 49 | 80.000 | 50 | nan |
| 51 | nan | 52 | nan | 53 | nan |
| 54 | nan | 55 | nan | 56 | 0.000 |
| 57 | 0.000 | 58 | nan | 59 | nan |
| 60 | nan | 61 | 0.000 | 62 | nan |
| 63 | nan | 64 | nan | 65 | nan |
| 66 | nan | 67 | nan | 68 | nan |
| 69 | 0.000 | 70 | 0.000 |
2024-03-03 15:06:25 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s 2024-03-03 15:06:25 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s 2024-03-03 15:06:25 | INFO | yolox.core.trainer:203 - ---> start train epoch4 2024-03-03 15:06:28 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s 100%|####################################################################################| 1/1 [00:05<00:00, 5.17s/it] 2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process... 2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results... 2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s) 2024-03-03 15:06:33 | INFO | pycocotools.coco:366 - creating index... 2024-03-03 15:06:33 | INFO | pycocotools.coco:366 - index created! 2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation... 2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox 2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.01s). 2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results... 2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.02s). 2024-03-03 15:06:33 | INFO | yolox.core.trainer:354 - Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 per class AP:
| class | AP | class | AP | class | AP |
|---|---|---|---|---|---|
| 0 | nan | 1 | nan | 2 | nan |
| 3 | nan | 4 | nan | 5 | nan |
| 6 | nan | 7 | 0.000 | 8 | nan |
| 9 | nan | 10 | nan | 11 | nan |
| 12 | nan | 13 | nan | 14 | nan |
| 15 | nan | 16 | nan | 17 | nan |
| 18 | 0.000 | 19 | nan | 20 | nan |
| 21 | nan | 22 | nan | 23 | nan |
| 24 | nan | 25 | nan | 26 | 0.000 |
| 27 | nan | 28 | nan | 29 | nan |
| 30 | nan | 31 | nan | 32 | 0.000 |
| 33 | nan | 34 | 0.000 | 35 | nan |
| 36 | nan | 37 | 0.000 | 38 | nan |
| 39 | nan | 40 | nan | 41 | nan |
| 42 | nan | 43 | nan | 44 | 0.000 |
| 45 | nan | 46 | nan | 47 | nan |
| 48 | nan | 49 | 0.000 | 50 | nan |
| 51 | nan | 52 | nan | 53 | nan |
| 54 | nan | 55 | nan | 56 | 0.000 |
| 57 | 0.000 | 58 | nan | 59 | nan |
| 60 | nan | 61 | 0.000 | 62 | nan |
| 63 | nan | 64 | nan | 65 | nan |
| 66 | nan | 67 | nan | 68 | nan |
| 69 | 0.000 | 70 | 0.000 | ||
| per class AR: | |||||
| class | AR | class | AR | class | AR |
| :-------- | :------ | :-------- | :------ | :-------- | :------ |
| 0 | nan | 1 | nan | 2 | nan |
| 3 | nan | 4 | nan | 5 | nan |
| 6 | nan | 7 | 0.000 | 8 | nan |
| 9 | nan | 10 | nan | 11 | nan |
| 12 | nan | 13 | nan | 14 | nan |
| 15 | nan | 16 | nan | 17 | nan |
| 18 | 0.000 | 19 | nan | 20 | nan |
| 21 | nan | 22 | nan | 23 | nan |
| 24 | nan | 25 | nan | 26 | 0.000 |
| 27 | nan | 28 | nan | 29 | nan |
| 30 | nan | 31 | nan | 32 | 0.000 |
| 33 | nan | 34 | 0.000 | 35 | nan |
| 36 | nan | 37 | 0.000 | 38 | nan |
| 39 | nan | 40 | nan | 41 | nan |
| 42 | nan | 43 | nan | 44 | 0.000 |
| 45 | nan | 46 | nan | 47 | nan |
| 48 | nan | 49 | 0.000 | 50 | nan |
| 51 | nan | 52 | nan | 53 | nan |
| 54 | nan | 55 | nan | 56 | 0.000 |
| 57 | 0.000 | 58 | nan | 59 | nan |
| 60 | nan | 61 | 0.000 | 62 | nan |
| 63 | nan | 64 | nan | 65 | nan |
| 66 | nan | 67 | nan | 68 | nan |
| 69 | 0.000 | 70 | 0.000 |
2024-03-03 15:06:33 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s 2024-03-03 15:06:33 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s 2024-03-03 15:06:33 | INFO | yolox.core.trainer:203 - ---> start train epoch5 2024-03-03 15:06:36 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s 100%|####################################################################################| 1/1 [00:05<00:00, 5.09s/it] 2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process... 2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results... 2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s) 2024-03-03 15:06:41 | INFO | pycocotools.coco:366 - creating index... 2024-03-03 15:06:41 | INFO | pycocotools.coco:366 - index created! 2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation... 2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox 2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.02s). 2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results... 2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.03s). 2024-03-03 15:06:41 | INFO | yolox.core.trainer:354 - Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000 per class AP:
| class | AP | class | AP | class | AP |
|---|---|---|---|---|---|
| 0 | nan | 1 | nan | 2 | nan |
| 3 | nan | 4 | nan | 5 | nan |
| 6 | nan | 7 | 0.000 | 8 | nan |
| 9 | nan | 10 | nan | 11 | nan |
| 12 | nan | 13 | nan | 14 | nan |
| 15 | nan | 16 | nan | 17 | nan |
| 18 | 0.000 | 19 | nan | 20 | nan |
| 21 | nan | 22 | nan | 23 | nan |
| 24 | nan | 25 | nan | 26 | 0.000 |
| 27 | nan | 28 | nan | 29 | nan |
| 30 | nan | 31 | nan | 32 | 0.000 |
| 33 | nan | 34 | 0.000 | 35 | nan |
| 36 | nan | 37 | 0.000 | 38 | nan |
| 39 | nan | 40 | nan | 41 | nan |
| 42 | nan | 43 | nan | 44 | 0.000 |
| 45 | nan | 46 | nan | 47 | nan |
| 48 | nan | 49 | 0.000 | 50 | nan |
| 51 | nan | 52 | nan | 53 | nan |
| 54 | nan | 55 | nan | 56 | 0.000 |
| 57 | 0.000 | 58 | nan | 59 | nan |
| 60 | nan | 61 | 0.000 | 62 | nan |
| 63 | nan | 64 | nan | 65 | nan |
| 66 | nan | 67 | nan | 68 | nan |
| 69 | 0.000 | 70 | 0.000 | ||
| per class AR: | |||||
| class | AR | class | AR | class | AR |
| :-------- | :------ | :-------- | :------ | :-------- | :------ |
| 0 | nan | 1 | nan | 2 | nan |
| 3 | nan | 4 | nan | 5 | nan |
| 6 | nan | 7 | 0.000 | 8 | nan |
| 9 | nan | 10 | nan | 11 | nan |
| 12 | nan | 13 | nan | 14 | nan |
| 15 | nan | 16 | nan | 17 | nan |
| 18 | 0.000 | 19 | nan | 20 | nan |
| 21 | nan | 22 | nan | 23 | nan |
| 24 | nan | 25 | nan | 26 | 0.000 |
| 27 | nan | 28 | nan | 29 | nan |
| 30 | nan | 31 | nan | 32 | 0.000 |
| 33 | nan | 34 | 0.000 | 35 | nan |
| 36 | nan | 37 | 0.000 | 38 | nan |
| 39 | nan | 40 | nan | 41 | nan |
| 42 | nan | 43 | nan | 44 | 0.000 |
| 45 | nan | 46 | nan | 47 | nan |
| 48 | nan | 49 | 0.000 | 50 | nan |
| 51 | nan | 52 | nan | 53 | nan |
| 54 | nan | 55 | nan | 56 | 0.000 |
| 57 | 0.000 | 58 | nan | 59 | nan |
| 60 | nan | 61 | 0.000 | 62 | nan |
| 63 | nan | 64 | nan | 65 | nan |
| 66 | nan | 67 | nan | 68 | nan |
| 69 | 0.000 | 70 | 0.000 |
2024-03-03 15:06:41 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s 2024-03-03 15:06:42 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s 2024-03-03 15:06:42 | INFO | yolox.core.trainer:203 - ---> start train epoch6
me tooo. I mean I get this error with custom dataset but still it would help to know why its like this with coco
Found the issue. The annotations in coco128 are incorrect, specifically the class ordering. I tried the same evaluation on the dataset downloaded from https://cocodataset.org/#download and it works perfectly fine.
