【YOLO】yolov5的训练策略2 |
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目录
一、默认的锚定框二、自动锚定框三、基于kmean计算训练数据集新的anchors
一、默认的锚定框
在yolov5x.yaml中查看锚定框的数据，如下： # yolov5中预先设定了锚定框，这些锚框是针对coco数据集的，其他目标检测也适用。 # 这些框针对的图片大小是640x640，是默认的anchor大小。 # 需要注意的是在目标检测任务中，一般使用大特征图上去检测小目标，因为大特征图含有更多小目标信息， # 因此大特征图上的anchor数值通常设置为小数值，小特征图检测大目标，因此小特征图上anchor数值设置较大。 anchors: - [10,13, 16,30, 33,23] # P3/8 最大特征图上的锚框 - [30,61, 62,45, 59,119] # P4/16 中等特征图上的锚框 - [116,90, 156,198, 373,326] # P5/32 最小特征图上的锚框 二、自动锚定框在yolov5 中训练开始前，计算数据集标注信息针对默认锚定框的最佳召回率，当最佳召回率大于等于0.98时，则不需要更新锚定框；如果最佳召回率小于0.98，则需要重新计算数据集的锚定框，如果计算处理更好则更新原理的anchors。 代码在 yolov5/utils/autoanchor.py。 def check_anchors(dataset, detect, thr=4.0, imgsz=640): """ # Check anchor fit to data, recompute if necessary 检查 anchor你和数据情况, 如果不合理重新计算anchors 输入: dataset - 数据集对象 detect - 模型的最后一层，检测头 thr - 阈值？ imgsz - 图片大小 """ m = detect # 例如: shape = 640 * [1280 960] / 1280 shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) # augment scale 随机缩放系数 scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # 计算框的wh wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # 计算指标 bpr,aat def metric(k): # compute metric r = wh[:, None] / k[None] x = torch.min(r, 1 / r).min(2)[0] # ratio metric best = x.max(1)[0] # best_x aat = (x > 1 / thr).float().sum(1).mean() # anchors above threshold bpr = (best > 1 / thr).float().mean() # best possible recall return bpr, aat # 计算BPR(最好的召回率) stride = m.stride.to(m.anchors.device).view(-1, 1, 1) # model strides anchors = m.anchors.clone() * stride # current anchors bpr, aat = metric(anchors.cpu().view(-1, 2)) s = f'\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). ' # 如果召回率>0.98,当前的anchors是正常的 # 否则就修正anchors if bpr > 0.98: logging.info(f'{s}Current anchors are a good fit to dataset ✅') else: logging.info(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...') na = m.anchors.numel() // 2 # number of anchors # 基于kmean方法重新计算 anchors anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) new_bpr = metric(anchors)[0] # 如果新的anchors更好就替换 if new_bpr > bpr: # replace anchors anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) m.anchors[:] = anchors.clone().view_as(m.anchors) check_anchor_order(m) # must be in pixel-space (not grid-space) m.anchors /= stride s = f'{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)' else: s = f'{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)' logging.info(s) 三、基于kmean计算训练数据集新的anchors代码在 yolov5/utils/autoanchor.py。 def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True): """ Creates kmeans-evolved anchors from training dataset Arguments: dataset: path to data.yaml, or a loaded dataset n: number of anchors img_size: image size used for training thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 gen: generations to evolve anchors using genetic algorithm verbose: print all results Return: k: kmeans evolved anchors Usage: from utils.autoanchor import *; _ = kmean_anchors() """ from scipy.cluster.vq import kmeans npr = np.random thr = 1 / thr def metric(k, wh): # compute metrics r = wh[:, None] / k[None] x = torch.min(r, 1 / r).min(2)[0] # ratio metric # x = wh_iou(wh, torch.tensor(k)) # iou metric return x, x.max(1)[0] # x, best_x def anchor_fitness(k): # mutation fitness _, best = metric(torch.tensor(k, dtype=torch.float32), wh) return (best * (best > thr).float()).mean() # fitness def print_results(k, verbose=True): k = k[np.argsort(k.prod(1))] # sort small to large x, best = metric(k, wh0) bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr s = f'{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n' \ f'{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{besan():.3f}-mean/best, ' \ f'past_thr={x[x > thr].mean():.3f}-mean: ' for x in k: s += '%i,%i, ' % (round(x[0]), round(x[1])) if verbose: logging.info(s[:-2]) return k if isinstance(dataset, str): # *.yaml file with open(dataset, errors='ignore') as f: data_dict = yaml.safe_load(f) # model dict from utils.dataloaders import LoadImagesAndLabels dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True) # Get label wh shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh # Filter i = (wh0 |
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