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license: mit
tags:
- image-classification
- remote-sensing
- resnet
- pytorch
- transformers
- self-supervised-learning
- contrastive-learning
- moco
---
# MoCo-TP-ResNet-50
ResNet-50 model pre-trained using MoCo-v2 with Temporal Pairing (TP) for geography-aware self-supervised learning on remote sensing images.
## Model Details
- **Architecture:** ResNet-50
- **Pre-training:** MoCo-v2 with Temporal Pairing (TP)
- **Input size:** 224×224×3
- **Feature dimension:** 2048 (before classification head)
- **Parameters:** ~23.6M
- **Training:** Self-supervised pre-training on fMoW dataset (200 epochs)
## Usage
### Feature Extraction
```python
from transformers import AutoModelForImageClassification
import torch
# Load model for feature extraction
model = AutoModelForImageClassification.from_pretrained(
"BiliSakura/MoCo-TP-ResNet-50",
trust_remote_code=True
)
# Inference - extract features
model.eval()
input_image = torch.randn(1, 3, 224, 224) # (batch, channels, height, width)
with torch.no_grad():
outputs = model(pixel_values=input_image, return_dict=True)
features = outputs["features"] # Shape: (1, 2048)
```
### Fine-tuning for Classification
To fine-tune the model for a specific classification task, you can add a classification head:
```python
from transformers import AutoModelForImageClassification, AutoConfig
import torch.nn as nn
# Load config and modify num_labels
config = AutoConfig.from_pretrained(
"BiliSakura/MoCo-TP-ResNet-50",
trust_remote_code=True
)
config.num_labels = 10 # Your number of classes
# Load model
model = AutoModelForImageClassification.from_pretrained(
"BiliSakura/MoCo-TP-ResNet-50",
config=config,
trust_remote_code=True
)
# The model will automatically replace the identity head with a classification head
# Now you can fine-tune on your dataset
```
## Model Architecture
The model consists of:
- **Backbone:** ResNet-50 (conv1, bn1, layer1-4)
- **Feature extractor:** Adaptive average pooling + flattening
- **Classification head:** Linear layer (2048 -> num_labels), or Identity for feature extraction
## Pre-training Details
This model was pre-trained using:
- **Method:** MoCo-v2 (Momentum Contrast) with Temporal Pairing
- **Dataset:** fMoW (Functional Map of the World)
- **Epochs:** 200
- **Loss:** Contrastive Predictive Coding (CPC)
- **Augmentation:** MoCo v2 augmentation (random resized crop, color jitter, grayscale, Gaussian blur)
## Citation
If you use this model, please cite the original Geography-Aware SSL paper:
```bibtex
@article{ayush2021geography,
title={Geography-Aware Self-Supervised Learning},
author={Ayush, Kumar and Uzkent, Burak and Meng, Chenlin and Tanmay, Kumar and Burke, Marshall and Lobell, David and Ermon, Stefano},
journal={ICCV},
year={2021}
}
```
**Original Repository:** [sustainlab-group/geography-aware-ssl](https://github.com/sustainlab-group/geography-aware-ssl)
## License
MIT License - for academic use only.
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