image-classifier-weights

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:

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'))