About val the data

[523997931]

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Question

Hi, I want to know, if I could use the val scripts just for some classes, for example, I want to val the pretrained model, but I only care about the mAP of person or car.

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[github-actions[bot]]

👋 Hello @523997931, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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[glenn-jocher]

@523997931 python val.py --verbose will display per-class metrics.

[523997931]

@523997931 python val.py --verbose will display per-class metrics.

Thank you for your reply!

[ZhouBay-TF]

@glenn-jocher ，hello，I want to get the value of "P R mAP@.5 mAP@”， when i use "python val.py --verbose",i get all val is 0;

[glenn-jocher]

@xinkangzhou sure, then your mAP is zero.

[github-actions[bot]]

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

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[Symbadian]

val.py --verbose

Hi @glenn-jocher,

Forgive my lack of ignorance but I keep getting this error when I try visualizing the new classes that were added in the training when running the validation script as per your direction python val.py --verbose.

None of the new classes are being shown in the validation verbose stage only the 80 classes per the original dataset. Based on all of the examples provided, I followed such to the best of my knowledge and I cannot seem to get this understanding.

Can you help me analyze this, please? I modified all configurations based on my knowledge but this keeps on popping up, please help!! and I am not sure what this means no matter how much I google??!!??!?

` (base) Symbadian-MacBook-Pro:yolov5 symbadian$ python3 val.py --verbose val: data=data/coco128.yaml, weights=yolov5s.pt, batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, task=val, device=, workers=8, single_cls=False, augment=False, verbose=True, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False YOLOv5 🚀 2022-5-7 torch 1.11.0 CPU

Fusing layers... YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients This is data in the val.py file {'path': '../datasets/coco128', 'train': ['/Users/symbadian/Desktop/LIRIS_new_dataset/datasets/coco128/images/train', '/Users/symbadian/Desktop/LIRIS_new_dataset/datasets/coco128/images/train2017'], 'val': ['/Users/symbadian/Desktop/LIRIS_new_dataset/datasets/coco128/images/val', '/Users/symbadian/Desktop/LIRIS_new_dataset/datasets/coco128/images/train2017'], 'test': None, 'nc': 106, 'names': ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'Beating', 'CuttingInKitchen', 'Discus_Amgst_ppl', 'Discuss_Wr_OnBrd', 'DiscusW_Give_Item', 'Enter_Room', 'Fencing', 'Fighting', 'Gun_wp_Deploy', 'Hand_Shake', 'Knife_Deploy', 'Leaves_Room', 'Make_Answer_Tel', 'Nunchucks', 'Pick_Up_Object', 'Put_Down_Object', 'Put_Item_In_Draw', 'Reading', 'Shooting', 'Stabbing', 'SumoWrestling', 'Typing_On_KeyBrd', 'Unlock_Dr_Leaves', 'Unlock_Rm_Enters', 'Unlock_Rm_Unsucc', 'Walking']} This is nc from val.py file 106 This is ncm from val.py file 80 Traceback (most recent call last): File "/Users/symbadian/Desktop/LIRIS_new_dataset/yolov5/val.py", line 399, in main(opt) File "/Users/symbadian/Desktop/LIRIS_new_dataset/yolov5/val.py", line 372, in main run(*vars(opt)) File "/Users/symbadian/miniforge3/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context return func(args, **kwargs) File "/Users/symbadian/Desktop/LIRIS_new_dataset/yolov5/val.py", line 170, in run assert ncm == nc, f'{weights[0]} ({ncm} classes) trained on different --data than what you passed ({nc} ' \ TypeError: 'PosixPath' object is not subscriptable`

[glenn-jocher]

@Symbadian 👋 hi, thanks for letting us know about this possible problem with YOLOv5 🚀. We've created a few short guidelines below to help users provide what we need in order to start investigating a possible problem.

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