Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+datasets:
+- cifar10
+metrics:
+- accuracy
+library_name: pytorch
+tags:
+- image-captioning
+- resnet18
+- lstm
+---
+
+# ResNet18 Image Captioning Weights (CIFAR-10)
+
+This repository contains the trained weights for an image captioning system consisting of a **CNN Encoder** and an **RNN Decoder**, fine-tuned on the CIFAR-10 dataset.
+
+## 📦 Model Components
+
+### 1. Encoder (`encoder`)
+- **Architecture:** ResNet18 (Feature Extractor)
+- **Output Dim:** 256
+- **Purpose:** Extracts high-level visual features from input images. The final fully connected layer was replaced to map features to the embedding space.
+
+### 2. Decoder (`decoder`)
+- **Architecture:** LSTM-based RNN
+- **Hidden Dim:** 512
+- **Embedding Dim:** 256
+- **Purpose:** Generates descriptive sequences based on the features received from the Encoder.
+
+## 🚀 Usage
+
+You can load these weights directly using the `huggingface_hub` library in Python:
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+
+# Download weights
+encoder_path = hf_hub_download(repo_id="Sher1988/image-classifier-weights", filename="encoder")
+decoder_path = hf_hub_download(repo_id="Sher1988/image-classifier-weights", filename="decoder")
+
+# Load into your model classes
+# encoder.load_state_dict(torch.load(encoder_path, map_location='cpu'))
+# decoder.load_state_dict(torch.load(decoder_path, map_location='cpu'))