diff --git a/README.md b/README.md
index 6a1d168ce4..554b422f78 100644
--- a/README.md
+++ b/README.md
@@ -390,9 +390,7 @@ python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg
 ```
 
 ```python
-model = torch.hub.load(
-    "ultralytics/yolov5", "custom", "yolov5s-cls.pt"
-)  # load from PyTorch Hub
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s-cls.pt")  # load from PyTorch Hub
 ```
 
 ### Export
diff --git a/README.zh-CN.md b/README.zh-CN.md
index 22b4374654..4c6f5f7098 100644
--- a/README.zh-CN.md
+++ b/README.zh-CN.md
@@ -390,9 +390,7 @@ python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg
 ```
 
 ```python
-model = torch.hub.load(
-    "ultralytics/yolov5", "custom", "yolov5s-cls.pt"
-)  # load from PyTorch Hub
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s-cls.pt")  # load from PyTorch Hub
 ```
 
 ### 模型导出
diff --git a/val.py b/val.py
index a0a740700b..782a939ec0 100644
--- a/val.py
+++ b/val.py
@@ -25,7 +25,7 @@
 import subprocess
 import sys
 from pathlib import Path
-
+/##################
 import numpy as np
 import torch
 from tqdm import tqdm
diff --git a/val.py.bak b/val.py.bak
new file mode 100644
index 0000000000..a0a740700b
--- /dev/null
+++ b/val.py.bak
@@ -0,0 +1,428 @@
+# YOLOv3 🚀 by Ultralytics, AGPL-3.0 license
+"""
+Validate a trained YOLOv3 detection model on a detection dataset.
+
+Usage:
+    $ python val.py --weights yolov5s.pt --data coco128.yaml --img 640
+
+Usage - formats:
+    $ python val.py --weights yolov5s.pt                 # PyTorch
+                              yolov5s.torchscript        # TorchScript
+                              yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
+                              yolov5s_openvino_model     # OpenVINO
+                              yolov5s.engine             # TensorRT
+                              yolov5s.mlmodel            # CoreML (macOS-only)
+                              yolov5s_saved_model        # TensorFlow SavedModel
+                              yolov5s.pb                 # TensorFlow GraphDef
+                              yolov5s.tflite             # TensorFlow Lite
+                              yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
+                              yolov5s_paddle_model       # PaddlePaddle
+"""
+
+import argparse
+import json
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+import numpy as np
+import torch
+from tqdm import tqdm
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0]  # YOLOv3 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
+
+from models.common import DetectMultiBackend
+from utils.callbacks import Callbacks
+from utils.dataloaders import create_dataloader
+from utils.general import (
+    LOGGER,
+    TQDM_BAR_FORMAT,
+    Profile,
+    check_dataset,
+    check_img_size,
+    check_requirements,
+    check_yaml,
+    coco80_to_coco91_class,
+    colorstr,
+    increment_path,
+    non_max_suppression,
+    print_args,
+    scale_boxes,
+    xywh2xyxy,
+    xyxy2xywh,
+)
+from utils.metrics import ConfusionMatrix, ap_per_class, box_iou
+from utils.plots import output_to_target, plot_images, plot_val_study
+from utils.torch_utils import select_device, smart_inference_mode
+
+
+def save_one_txt(predn, save_conf, shape, file):
+    # Save one txt result
+    gn = torch.tensor(shape)[[1, 0, 1, 0]]  # normalization gain whwh
+    for *xyxy, conf, cls in predn.tolist():
+        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
+        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
+        with open(file, "a") as f:
+            f.write(("%g " * len(line)).rstrip() % line + "\n")
+
+
+def save_one_json(predn, jdict, path, class_map):
+    # Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
+    image_id = int(path.stem) if path.stem.isnumeric() else path.stem
+    box = xyxy2xywh(predn[:, :4])  # xywh
+    box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
+    for p, b in zip(predn.tolist(), box.tolist()):
+        jdict.append(
+            {
+                "image_id": image_id,
+                "category_id": class_map[int(p[5])],
+                "bbox": [round(x, 3) for x in b],
+                "score": round(p[4], 5),
+            }
+        )
+
+
+def process_batch(detections, labels, iouv):
+    """
+    Return correct prediction matrix
+    Arguments:
+        detections (array[N, 6]), x1, y1, x2, y2, conf, class
+        labels (array[M, 5]), class, x1, y1, x2, y2
+    Returns:
+        correct (array[N, 10]), for 10 IoU levels
+    """
+    correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
+    iou = box_iou(labels[:, 1:], detections[:, :4])
+    correct_class = labels[:, 0:1] == detections[:, 5]
+    for i in range(len(iouv)):
+        x = torch.where((iou >= iouv[i]) & correct_class)  # IoU > threshold and classes match
+        if x[0].shape[0]:
+            matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()  # [label, detect, iou]
+            if x[0].shape[0] > 1:
+                matches = matches[matches[:, 2].argsort()[::-1]]
+                matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+                # matches = matches[matches[:, 2].argsort()[::-1]]
+                matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+            correct[matches[:, 1].astype(int), i] = True
+    return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
+
+
+@smart_inference_mode()
+def run(
+    data,
+    weights=None,  # model.pt path(s)
+    batch_size=32,  # batch size
+    imgsz=640,  # inference size (pixels)
+    conf_thres=0.001,  # confidence threshold
+    iou_thres=0.6,  # NMS IoU threshold
+    max_det=300,  # maximum detections per image
+    task="val",  # train, val, test, speed or study
+    device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+    workers=8,  # max dataloader workers (per RANK in DDP mode)
+    single_cls=False,  # treat as single-class dataset
+    augment=False,  # augmented inference
+    verbose=False,  # verbose output
+    save_txt=False,  # save results to *.txt
+    save_hybrid=False,  # save label+prediction hybrid results to *.txt
+    save_conf=False,  # save confidences in --save-txt labels
+    save_json=False,  # save a COCO-JSON results file
+    project=ROOT / "runs/val",  # save to project/name
+    name="exp",  # save to project/name
+    exist_ok=False,  # existing project/name ok, do not increment
+    half=True,  # use FP16 half-precision inference
+    dnn=False,  # use OpenCV DNN for ONNX inference
+    model=None,
+    dataloader=None,
+    save_dir=Path(""),
+    plots=True,
+    callbacks=Callbacks(),
+    compute_loss=None,
+):
+    # Initialize/load model and set device
+    training = model is not None
+    if training:  # called by train.py
+        device, pt, jit, engine = next(model.parameters()).device, True, False, False  # get model device, PyTorch model
+        half &= device.type != "cpu"  # half precision only supported on CUDA
+        model.half() if half else model.float()
+    else:  # called directly
+        device = select_device(device, batch_size=batch_size)
+
+        # Directories
+        save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
+        (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
+
+        # Load model
+        model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
+        stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
+        imgsz = check_img_size(imgsz, s=stride)  # check image size
+        half = model.fp16  # FP16 supported on limited backends with CUDA
+        if engine:
+            batch_size = model.batch_size
+        else:
+            device = model.device
+            if not (pt or jit):
+                batch_size = 1  # export.py models default to batch-size 1
+                LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models")
+
+        # Data
+        data = check_dataset(data)  # check
+
+    # Configure
+    model.eval()
+    cuda = device.type != "cpu"
+    is_coco = isinstance(data.get("val"), str) and data["val"].endswith(f"coco{os.sep}val2017.txt")  # COCO dataset
+    nc = 1 if single_cls else int(data["nc"])  # number of classes
+    iouv = torch.linspace(0.5, 0.95, 10, device=device)  # iou vector for mAP@0.5:0.95
+    niou = iouv.numel()
+
+    # Dataloader
+    if not training:
+        if pt and not single_cls:  # check --weights are trained on --data
+            ncm = model.model.nc
+            assert ncm == nc, (
+                f"{weights} ({ncm} classes) trained on different --data than what you passed ({nc} "
+                f"classes). Pass correct combination of --weights and --data that are trained together."
+            )
+        model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz))  # warmup
+        pad, rect = (0.0, False) if task == "speed" else (0.5, pt)  # square inference for benchmarks
+        task = task if task in ("train", "val", "test") else "val"  # path to train/val/test images
+        dataloader = create_dataloader(
+            data[task],
+            imgsz,
+            batch_size,
+            stride,
+            single_cls,
+            pad=pad,
+            rect=rect,
+            workers=workers,
+            prefix=colorstr(f"{task}: "),
+        )[0]
+
+    seen = 0
+    confusion_matrix = ConfusionMatrix(nc=nc)
+    names = model.names if hasattr(model, "names") else model.module.names  # get class names
+    if isinstance(names, (list, tuple)):  # old format
+        names = dict(enumerate(names))
+    class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
+    s = ("%22s" + "%11s" * 6) % ("Class", "Images", "Instances", "P", "R", "mAP50", "mAP50-95")
+    tp, fp, p, r, f1, mp, mr, map50, ap50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
+    dt = Profile(), Profile(), Profile()  # profiling times
+    loss = torch.zeros(3, device=device)
+    jdict, stats, ap, ap_class = [], [], [], []
+    callbacks.run("on_val_start")
+    pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT)  # progress bar
+    for batch_i, (im, targets, paths, shapes) in enumerate(pbar):
+        callbacks.run("on_val_batch_start")
+        with dt[0]:
+            if cuda:
+                im = im.to(device, non_blocking=True)
+                targets = targets.to(device)
+            im = im.half() if half else im.float()  # uint8 to fp16/32
+            im /= 255  # 0 - 255 to 0.0 - 1.0
+            nb, _, height, width = im.shape  # batch size, channels, height, width
+
+        # Inference
+        with dt[1]:
+            preds, train_out = model(im) if compute_loss else (model(im, augment=augment), None)
+
+        # Loss
+        if compute_loss:
+            loss += compute_loss(train_out, targets)[1]  # box, obj, cls
+
+        # NMS
+        targets[:, 2:] *= torch.tensor((width, height, width, height), device=device)  # to pixels
+        lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []  # for autolabelling
+        with dt[2]:
+            preds = non_max_suppression(
+                preds, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls, max_det=max_det
+            )
+
+        # Metrics
+        for si, pred in enumerate(preds):
+            labels = targets[targets[:, 0] == si, 1:]
+            nl, npr = labels.shape[0], pred.shape[0]  # number of labels, predictions
+            path, shape = Path(paths[si]), shapes[si][0]
+            correct = torch.zeros(npr, niou, dtype=torch.bool, device=device)  # init
+            seen += 1
+
+            if npr == 0:
+                if nl:
+                    stats.append((correct, *torch.zeros((2, 0), device=device), labels[:, 0]))
+                    if plots:
+                        confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
+                continue
+
+            # Predictions
+            if single_cls:
+                pred[:, 5] = 0
+            predn = pred.clone()
+            scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1])  # native-space pred
+
+            # Evaluate
+            if nl:
+                tbox = xywh2xyxy(labels[:, 1:5])  # target boxes
+                scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1])  # native-space labels
+                labelsn = torch.cat((labels[:, 0:1], tbox), 1)  # native-space labels
+                correct = process_batch(predn, labelsn, iouv)
+                if plots:
+                    confusion_matrix.process_batch(predn, labelsn)
+            stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0]))  # (correct, conf, pcls, tcls)
+
+            # Save/log
+            if save_txt:
+                save_one_txt(predn, save_conf, shape, file=save_dir / "labels" / f"{path.stem}.txt")
+            if save_json:
+                save_one_json(predn, jdict, path, class_map)  # append to COCO-JSON dictionary
+            callbacks.run("on_val_image_end", pred, predn, path, names, im[si])
+
+        # Plot images
+        if plots and batch_i < 3:
+            plot_images(im, targets, paths, save_dir / f"val_batch{batch_i}_labels.jpg", names)  # labels
+            plot_images(im, output_to_target(preds), paths, save_dir / f"val_batch{batch_i}_pred.jpg", names)  # pred
+
+        callbacks.run("on_val_batch_end", batch_i, im, targets, paths, shapes, preds)
+
+    # Compute metrics
+    stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)]  # to numpy
+    if len(stats) and stats[0].any():
+        tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
+        ap50, ap = ap[:, 0], ap.mean(1)  # AP@0.5, AP@0.5:0.95
+        mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
+    nt = np.bincount(stats[3].astype(int), minlength=nc)  # number of targets per class
+
+    # Print results
+    pf = "%22s" + "%11i" * 2 + "%11.3g" * 4  # print format
+    LOGGER.info(pf % ("all", seen, nt.sum(), mp, mr, map50, map))
+    if nt.sum() == 0:
+        LOGGER.warning(f"WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels")
+
+    # Print results per class
+    if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
+        for i, c in enumerate(ap_class):
+            LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
+
+    # Print speeds
+    t = tuple(x.t / seen * 1e3 for x in dt)  # speeds per image
+    if not training:
+        shape = (batch_size, 3, imgsz, imgsz)
+        LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}" % t)
+
+    # Plots
+    if plots:
+        confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
+        callbacks.run("on_val_end", nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)
+
+    # Save JSON
+    if save_json and len(jdict):
+        w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ""  # weights
+        anno_json = str(Path("../datasets/coco/annotations/instances_val2017.json"))  # annotations
+        if not os.path.exists(anno_json):
+            anno_json = os.path.join(data["path"], "annotations", "instances_val2017.json")
+        pred_json = str(save_dir / f"{w}_predictions.json")  # predictions
+        LOGGER.info(f"\nEvaluating pycocotools mAP... saving {pred_json}...")
+        with open(pred_json, "w") as f:
+            json.dump(jdict, f)
+
+        try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
+            check_requirements("pycocotools>=2.0.6")
+            from pycocotools.coco import COCO
+            from pycocotools.cocoeval import COCOeval
+
+            anno = COCO(anno_json)  # init annotations api
+            pred = anno.loadRes(pred_json)  # init predictions api
+            eval = COCOeval(anno, pred, "bbox")
+            if is_coco:
+                eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files]  # image IDs to evaluate
+            eval.evaluate()
+            eval.accumulate()
+            eval.summarize()
+            map, map50 = eval.stats[:2]  # update results (mAP@0.5:0.95, mAP@0.5)
+        except Exception as e:
+            LOGGER.info(f"pycocotools unable to run: {e}")
+
+    # Return results
+    model.float()  # for training
+    if not training:
+        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ""
+        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
+    maps = np.zeros(nc) + map
+    for i, c in enumerate(ap_class):
+        maps[c] = ap[i]
+    return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t
+
+
+def parse_opt():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path")
+    parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov3-tiny.pt", help="model path(s)")
+    parser.add_argument("--batch-size", type=int, default=32, help="batch size")
+    parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)")
+    parser.add_argument("--conf-thres", type=float, default=0.001, help="confidence threshold")
+    parser.add_argument("--iou-thres", type=float, default=0.6, help="NMS IoU threshold")
+    parser.add_argument("--max-det", type=int, default=300, help="maximum detections per image")
+    parser.add_argument("--task", default="val", help="train, val, test, speed or study")
+    parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
+    parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
+    parser.add_argument("--single-cls", action="store_true", help="treat as single-class dataset")
+    parser.add_argument("--augment", action="store_true", help="augmented inference")
+    parser.add_argument("--verbose", action="store_true", help="report mAP by class")
+    parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
+    parser.add_argument("--save-hybrid", action="store_true", help="save label+prediction hybrid results to *.txt")
+    parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels")
+    parser.add_argument("--save-json", action="store_true", help="save a COCO-JSON results file")
+    parser.add_argument("--project", default=ROOT / "runs/val", help="save to project/name")
+    parser.add_argument("--name", default="exp", help="save to project/name")
+    parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
+    parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
+    parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
+    opt = parser.parse_args()
+    opt.data = check_yaml(opt.data)  # check YAML
+    opt.save_json |= opt.data.endswith("coco.yaml")
+    opt.save_txt |= opt.save_hybrid
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
+
+    if opt.task in ("train", "val", "test"):  # run normally
+        if opt.conf_thres > 0.001:  # https://github.com/ultralytics/yolov5/issues/1466
+            LOGGER.info(f"WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results")
+        if opt.save_hybrid:
+            LOGGER.info("WARNING ⚠️ --save-hybrid will return high mAP from hybrid labels, not from predictions alone")
+        run(**vars(opt))
+
+    else:
+        weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]
+        opt.half = torch.cuda.is_available() and opt.device != "cpu"  # FP16 for fastest results
+        if opt.task == "speed":  # speed benchmarks
+            # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...
+            opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False
+            for opt.weights in weights:
+                run(**vars(opt), plots=False)
+
+        elif opt.task == "study":  # speed vs mAP benchmarks
+            # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...
+            for opt.weights in weights:
+                f = f"study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt"  # filename to save to
+                x, y = list(range(256, 1536 + 128, 128)), []  # x axis (image sizes), y axis
+                for opt.imgsz in x:  # img-size
+                    LOGGER.info(f"\nRunning {f} --imgsz {opt.imgsz}...")
+                    r, _, t = run(**vars(opt), plots=False)
+                    y.append(r + t)  # results and times
+                np.savetxt(f, y, fmt="%10.4g")  # save
+            subprocess.run(["zip", "-r", "study.zip", "study_*.txt"])
+            plot_val_study(x=x)  # plot
+        else:
+            raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")')
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)