ValueError: Found input variables with inconsistent numbers of samples

[saoko]

I'm trying to write a bounding box regression training script with Keras and TensorFlow for object detection. I have a dataset of 3153 images (in jpg extension) and an txt file of bounding box annotations which consists 6430 lines (some pictures have multiple bounding box). This is a part of txt file (to know how it look):

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2007_000027 101 174 351 349 2007_000032 180 195 229 213 2007_000032 189 26 238 44 2007_000129 1 74 462 272 2007_000129 19 252 487 334 2007_000170 91 3 206 43 2007_000170 28 4 372 461 2007_000272 71 25 500 304 2007_000323 3 277 375 500 2007_000323 3 12 375 305

I created a configuration file, which stores directories to some files:

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BASE_PATH = "dataset" IMAGES_PATH = os.path.sep.join([BASE_PATH, "images"]) ANNOTS_PATH = os.path.sep.join([BASE_PATH, "bboxes.txt"])   BASE_OUTPUT = "output" MODEL_PATH = os.path.sep.join([BASE_OUTPUT, "detector.h5"]) PLOT_PATH = os.path.sep.join([BASE_OUTPUT, "plot.png"]) TEST_FILENAMES = os.path.sep.join([BASE_OUTPUT, "test_images.txt"])   INIT_LR = 1e-4 NUM_EPOCHS = 25 BATCH_SIZE = 32

The second file includes code to train my data:

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print("INFO - loading dataset...") rows = open(config.ANNOTS_PATH).read().strip().split("\n") data = [] targets = [] filenames = []   for row in rows:     row = row.split(' ')     (filename, startX, startY, endX, endY) = row     suffix = ".jpg"     imagePath = os.path.sep.join([config.IMAGES_PATH, filename+suffix])     image = cv2.imread(imagePath)     (h, w) = image.shape[:2]       startX = float(startX) / w     startY = float(startY) / h     endX = float(endX) / w     endY = float(endY) / h       image = load_img(imagePath, target_size=(224, 224))     image = img_to_array(image)       data.append(image)     targets.append((startX, startY, endX, endY))     filenames.append   data = np.array(data, dtype="float32") / 255.0 targets = np.array(targets, dtype="float32")   split = train_test_split(data, targets, filenames, test_size=0.10, random_state=42)   (trainImages, testImages) = split[:2] (trainTargets, testTargets) = split[2:4] (trainFilenames, testFilenames) = split[4:]   print("INFO - saving testing filenames...") f = open(config.TEST_FILENAMES, "w") f.write("\n".join(testFilenames)) f.close()   vgg = VGG16(weights="imagenet", include_top=False, input_tensor=Input(shape=(224, 224, 3))) vgg.trainable = False   flatten = vgg.output flatten = Flatten()(flatten)   bboxHead = Dense(128, activation="relu")(flatten) bboxHead = Dense(64, activation="relu")(bboxHead) bboxHead = Dense(32, activation="relu")(bboxHead) bboxHead = Dense(4, activation="sigmoid")(bboxHead)   model = Model(inputs=vgg.input, outputs=bboxHead)   opt = Adam(lr=config.INIT_LR) model.compile(loss="mse", optimizer=opt) print(model.summary())   print("INFO - training bounding box regressor...") H = model.fit(     trainImages, trainTargets,     validation_data=(testImages, testTargets),     batch_size=config.BATCH_SIZE, epochs=config.NUM_EPOCHS, verbose=1)   print("INFO - saving objects detector model...") model.save(config.MODEL_PATH, save_format="h5")   N = config.NUM_EPOCHS plt.style.use("ggplot") plt.figure() plt.plot(np.arange(0, N), H.history["loss"], label="train_loss") plt.plot(np.arange(0, N), H.history["val_loss"], label="val_loss") plt.title("Bounding box regression loss on training set") plt.xlabel("Epoch #") plt.ylabel("Loss") plt.legend(loc="lower left") plt.savefig(config.PLOT_PATH)

When I run my code I get the next error:

Traceback (most recent call last): File "/Users/username/Downloads/od/train.py", line 47, in split = train_test_split(data, targets, filenames, test_size=0.10, random_state=42) File "/usr/local/lib/python3.9/site-packages/sklearn/model_selection/_split.py", line 2430, in train_test_split arrays = indexable(*arrays) File "/usr/local/lib/python3.9/site-packages/sklearn/utils/validation.py", line 433, in indexable check_consistent_length(*result) File "/usr/local/lib/python3.9/site-packages/sklearn/utils/validation.py", line 387, in check_consistent_length raise ValueError( ValueError: Found input variables with inconsistent numbers of samples: [6430, 6430, 0]

I understand that the number of lines is not equal to number of images, but I can't change data in txt file. Can someone help me to correct this code to train my data properly.

Thanks!