Build an AI Image Caption Generator (Python + Vision AI)#AI #ComputerVision #Python

This project shows how to build an AI image caption generator that can automatically describe an image using deep learning. The system combines computer vision and natural language processing. You will use a pretrained CNN to extract image features and a sequence model to generate captions. ⭐ 1. Load an Image Dataset A common dataset for captioning is Flickr8k or MS COCO. Example structure: images/ captions.txt Load captions: import pandas as pd captions = pd.read_csv("captions.txt") print(captions.head()) Tools commonly used: Kaggle datasets PyTorch datasets TensorFlow datasets ⭐ 2. Extract Image Features with a Pretrained CNN Instead of training a vision model from scratch, we use pretrained models such as: ResNet VGG16 EfficientNet Example using PyTorch: import torchvision.models as models import torch model = models.resnet50(pretrained=True) model = torch.nn.Sequential(*list(model.children())[:-1]) Then extract features: features = model(image_tensor) This converts images into feature vectors that the caption model can understand. ⭐ 3. Prepare Text Captions Captions must be tokenized and converted into sequences. Example preprocessing: from tensorflow.keras.preprocessing.text import Tokenizer tokenizer = Tokenizer() tokenizer.fit_on_texts(captions["caption"]) Convert captions to sequences: sequences = tokenizer.texts_to_sequences(captions["caption"]) Vocabulary size determines the language model complexity. ⭐ 4. Train a Caption Generation Model A common architecture is: Image Features → Dense Layer Text Input → LSTM Merge → Output Caption Example (simplified): from tensorflow.keras.layers import Input, LSTM, Embedding, Dense caption_input = Input(shape=(max_length,)) embedding = Embedding(vocab_size, 256)(caption_input) lstm = LSTM(256)(embedding) output = Dense(vocab_size, activation="softmax")(lstm) This model learns to predict the next word in a caption sequence. ⭐ 5. Generate Captions for New Images After training, we