Update README.md

README.md

@@ -12,3 +12,51 @@ short_description: image_caption_generator
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 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+
+---
+title: Image Captioning LSTM
+emoji: 📸
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.12.0
+app_file: app.py
+pinned: false
+---
+
+# Image Captioning with LSTM
+
+This application generates descriptive captions for images using a deep learning model trained on the Flickr8K dataset.
+
+## Model Architecture
+
+- **Image Encoder**: ResNet50 (pre-trained on ImageNet) extracts visual features
+- **Caption Decoder**: LSTM-based sequence generator with embedding layer
+- **Vocabulary Size**: ~8000 unique words
+- **Max Caption Length**: 40 tokens
+
+## Features
+
+- **Greedy Search**: Fast caption generation by selecting the most probable word at each step
+- **Beam Search**: Improved caption quality by exploring multiple candidate sequences (k=3)
+
+## Training Dataset
+
+The model was trained on the Flickr8K dataset containing:
+- 8,000 images
+- 5 captions per image (40,000 captions total)
+
+## How to Use
+
+1. Upload an image
+2. Choose caption generation method (Greedy or Beam Search)
+3. Click Submit to generate caption
+
+## Model Performance
+
+The model achieves competitive BLEU scores on the test set, providing natural and descriptive captions for a variety of scenes.
+
+## Citation
+
+Dataset: M. Hodosh, P. Young and J. Hockenmaier (2013) "Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics", Journal of Artificial Intelligence Research, Volume 47, pages 853-899