我正在尝试使用 YOLOV3 AND Pytorch 实现对象检测。我正在我的自定义数据集上训练模型，该数据集仅包含一种类型的 200 张图像，并且只有一个对象（已标记，供参考。请查看下图）。我已经对图像进行了注释并将它们保存为 .txt 格式。
数据集
--------图片 img1.jpg img2.jpg
--------标签 img1.txt img2.txt
test.txt 有效.txt
我的模型训练部分工作正常。在每个时代（检查点）之后，我都会保存权重。我为 20 个 Epoch 值运行程序（训练部分），并为对象检测部分获取最后保存的检查点权重。只有 1 个类 (IRA)，索引 (0) 我的 Yolo.cfg 文件也是正确的。[class = 1, filters = 18]（在文件的三个部分更新）
但问题是：-在运行程序的对象检测部分时，我收到此错误
此问题仅针对此问题。我在其他图像数据集上进行了测试，效果很好，但我不知道为什么在这种情况下它没有给出任何标签？
这是我的训练模型部分
from __future__ import division
from models import *
from utils import *
from datasets import *
from parse_config import *
import os
import sys
import time
import datetime
import argparse
import torch
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision import transforms
from torch.autograd import Variable
import torch.optim as optim
parser = argparse.ArgumentParser()
parser.add_argument("-f")
parser.add_argument("--epochs", type=int, default=20, help="number of epochs")
parser.add_argument("--image_folder", type=str, default="/content/drive/My Drive/FINAL_PYTORCH_DATA/images", help="path to dataset")
parser.add_argument("--batch_size", type=int, default=16, help="size of each image batch")
parser.add_argument("--model_config_path", type=str, default="/content/drive/My Drive/config/yolov3.cfg", help="path to model config file")
parser.add_argument("--data_config_path", type=str, default="/content/drive/My Drive/config/coco.data", help="path to data config file")
parser.add_argument("--weights_path", type=str, default="/content/drive/My Drive/config/yolov3.weights", help="path to weights file")
parser.add_argument("--class_path", type=str, default="/content/drive/My Drive/config/coco.names", help="path to class label file")
parser.add_argument("--conf_thres", type=float, default=0.8, help="object confidence threshold")
parser.add_argument("--nms_thres", type=float, default=0.4, help="iou thresshold for non-maximum suppression")
parser.add_argument("--n_cpu", type=int, default=0, help="number of cpu threads to use during batch generation")
parser.add_argument("--img_size", type=int, default=416, help="size of each image dimension")
parser.add_argument("--checkpoint_interval", type=int, default=1, help="interval between saving model weights")
parser.add_argument("--checkpoint_dir", type=str, default="/content/checkpoints", help="directory where model checkpoints are saved")
parser.add_argument("--use_cuda", type=bool, default=True, help="whether to use cuda if available")
opt = parser.parse_args()
print(opt)
cuda = torch.cuda.is_available() and opt.use_cuda
os.makedirs("checkpoints", exist_ok=True)
classes = load_classes(opt.class_path)
# Get data configuration
data_config = parse_data_config(opt.data_config_path)
train_path = data_config["train"]
# Get hyper parameters
hyperparams = parse_model_config(opt.model_config_path)[0]
learning_rate = float(hyperparams["learning_rate"])
momentum = float(hyperparams["momentum"])
decay = float(hyperparams["decay"])
burn_in = int(hyperparams["burn_in"])
# Initiate model
model = Darknet(opt.model_config_path)
model.load_weights(opt.weights_path)
#model.apply(weights_init_normal)
if cuda:
model = model.cuda()
model.train()
# Get dataloader
dataloader = torch.utils.data.DataLoader(
ListDataset(train_path), batch_size=opt.batch_size, shuffle=False, num_workers=opt.n_cpu
)
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()))
for epoch in range(opt.epochs):
for batch_i, (_, imgs, targets) in enumerate(dataloader):
imgs = Variable(imgs.type(Tensor))
targets = Variable(targets.type(Tensor), requires_grad=False)
optimizer.zero_grad()
loss = model(imgs, targets)
loss.backward()
optimizer.step()
print(
"[Epoch %d/%d, Batch %d/%d] [Losses: x %f, y %f, w %f, h %f, conf %f, cls %f, total %f, recall: %.5f, precision: %.5f]"
% (
epoch,
opt.epochs,
batch_i,
len(dataloader),
model.losses["x"],
model.losses["y"],
model.losses["w"],
model.losses["h"],
model.losses["conf"],
model.losses["cls"],
loss.item(),
model.losses["recall"],
model.losses["precision"],
)
)
model.seen += imgs.size(0)
if epoch % opt.checkpoint_interval == 0:
model.save_weights("%s%d.weights" % (opt.checkpoint_dir, epoch))
这是我的模型加载部分：-
config_path='/content/drive/My Drive/config/yolov3.cfg'
weights_path='/content/checkpoints19.weights'
class_path='/content/drive/My Drive/config/coco.names'
img_size=416
conf_thres=0.8
nms_thres=0.4
# Load model and weights
model = Darknet(config_path, img_size=img_size)
model.load_weights(weights_path)
model.cuda()
model.eval()
classes = utils.load_classes(class_path)
Tensor = torch.cuda.FloatTensor
这是我的对象检测部分：-
def detect_image(img):
# scale and pad image
ratio = min(img_size/img.size[0], img_size/img.size[1])
imw = round(img.size[0] * ratio)
imh = round(img.size[1] * ratio)
img_transforms = transforms.Compose([ transforms.Resize((imh, imw)),
transforms.Pad((max(int((imh-imw)/2),0), max(int((imw-imh)/2),0), max(int((imh-imw)/2),0), max(int((imw-imh)/2),0)),
(128,128,128)),
transforms.ToTensor(),
])
# convert image to Tensor
image_tensor = img_transforms(img).float()
image_tensor = image_tensor.unsqueeze_(0)
input_img = Variable(image_tensor.type(Tensor))
# run inference on the model and get detections
with torch.no_grad():
detections = model(input_img)
detections = utils.non_max_suppression(detections, 80, conf_thres, nms_thres)
return detections[0]
# load image and get detections
img_path = "/content/image1 - Copy (2).jpg"
prev_time = time.time()
img = Image.open(img_path)
detections = detect_image(img)
inference_time = datetime.timedelta(seconds=time.time() - prev_time)
print ('Inference Time: %s' % (inference_time))
# Get bounding-box colors
cmap = plt.get_cmap('tab20b')
colors = [cmap(i) for i in np.linspace(0, 1, 20)]
img = np.array(img)
plt.figure()
fig, ax = plt.subplots(1, figsize=(12,9))
ax.imshow(img)
pad_x = max(img.shape[0] - img.shape[1], 0) * (img_size / max(img.shape))
pad_y = max(img.shape[1] - img.shape[0], 0) * (img_size / max(img.shape))
unpad_h = img_size - pad_y
unpad_w = img_size - pad_x
k=[]
if detections is not None:
unique_labels = detections[:, -1].cpu().unique()
n_cls_preds = len(unique_labels)
bbox_colors = random.sample(colors, n_cls_preds)
# browse detections and draw bounding boxes
for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
box_h = ((y2 - y1) / unpad_h) * img.shape[0]
box_w = ((x2 - x1) / unpad_w) * img.shape[1]
y1 = ((y1 - pad_y // 2) / unpad_h) * img.shape[0]
x1 = ((x1 - pad_x // 2) / unpad_w) * img.shape[1]
color = bbox_colors[int(np.where(unique_labels == int(cls_pred))[0])]
bbox = patches.Rectangle((x1, y1), box_w, box_h, linewidth=2, edgecolor=color, facecolor='none')
ax.add_patch(bbox)
k=classes[int(cls_pred)]
plt.text(x1, y1, s=classes[int(cls_pred)], color='white', verticalalignment='top',
bbox={'color': color, 'pad': 0})
plt.axis('off')
# save image
plt.savefig(img_path.replace(".jpg", "-det.jpg"), bbox_inches='tight', pad_inches=0.0)
plt.show()
print(k)
如果有人可以帮助我理解，是什么导致了这个错误以及如何解决这个问题？