Question: i'm training a classifier and i made sure all the pictures are jpg but still, this error occurs: InvalidArgumentError: Unknown image file format. One of JPEG, PNG, GIF, BMP required. [[{{node decode_image/DecodeImage}}]] [[IteratorGetNext]] [Op:__inference_train_function_1481] i tried training on a smaller dataset and also they were all jpg and there was no problem this is the code: import numpy as np import tensorflow as tf from tensorflow import keras dataset = keras.preprocessing.image_dataset_from_directory( '/content/drive/MyDrive/fi_dataset/train', batch_size=64, image_size=(200, 200)) dense = keras.layers.Dense(units=16) inputs = keras.Input(shape=(None, None, 3)) from tensorflow.keras import layers x = CenterCrop(height=150, width=150)(inputs) x = Rescaling(scale=1.0 / 255)(x) x = layers.Conv2D(filters=32, kernel_size=(3, 3), activation="relu")(x) x = layers.MaxPooling2D(pool_size=(3, 3))(x) x = layers.Conv2D(filters=32, kernel_size=(3, 3), activation="relu")(x) x = layers.MaxPooling2D(pool_size=(3, 3))(x) x = layers.Conv2D(filters=32, kernel_size=(3, 3), activation="relu")(x) x = layers.GlobalAveragePooling2D()(x) num_classes = 1 outputs = layers.Dense(num_classes, activation="sigmoid")(x) model = keras.Model(inputs=inputs, outputs=outputs) data = np.random.randint(0, 256, size=(64, 200, 200, 3)).astype("float32") processed_data = model(data) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=[keras.metrics.binary_accuracy],) history=model.fit(dataset, epochs=10) 

Answer: When you say you made sure they were jpg's how did you verify that? Just because the extension is .jpg does not mean the file is a true jpg image. I suggest you run the code below to see which image may be defective. import os import cv2 def check_images( s_dir, ext_list): bad_images=[] bad_ext=[] s_list= os.listdir(s_dir) for klass in s_list: klass_path=os.path.join (s_dir, klass) print ('processing class directory ', klass) if os.path.isdir(klass_path): file_list=os.listdir(klass_path) for f in file_list: f_path=os.path.join (klass_path,f) index=f.rfind('.') ext=f[index+1:].lower() if ext not in ext_list: print('file ', f_path, ' has an invalid extension ', ext) bad_ext.append(f_path) if os.path.isfile(f_path): try: img=cv2.imread(f_path) shape=img.shape except: print('file ', f_path, ' is not a valid image file') bad_images.append(f_path) else: print('*** fatal error, you a sub directory ', f, ' in class directory ', klass) else: print ('*** WARNING*** you have files in ', s_dir, ' it should only contain sub directories') return bad_images, bad_ext source_dir =r'c:\temp\people\storage' good_exts=['jpg', 'png', 'jpeg', 'gif', 'bmp' ] # list of acceptable extensions bad_file_list, bad_ext_list=check_images(source_dir, good_exts) if len(bad_file_list) !=0: print('improper image files are listed below') for i in range (len(bad_file_list)): print (bad_file_list[i]) else: print(' no improper image files were found')  even this might not be enough because it checks the file's extension name. Actually it might have an extension name jpg but be in say a tiff format. To take it a stepfurther you can add some code that if the extension is not in the good extension list you could read the image and if it is valid use cv2 to convert it to say a jpg and then write it back to the file.