metadata
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:
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'))