Clip Rsicd

🚀 Model Card: clip-rsicd

This model is a fine - tuned version of CLIP by OpenAI. It aims to enhance zero - shot image classification, text - to - image, and image - to - image retrieval, specifically for remote sensing images.

🚀 Quick Start

You can start using the clip-rsicd model with the following code example:

from PIL import Image
import requests

from transformers import CLIPProcessor, CLIPModel

model = CLIPModel.from_pretrained("flax-community/clip-rsicd")
processor = CLIPProcessor.from_pretrained("flax-community/clip-rsicd")

url = "https://raw.githubusercontent.com/arampacha/CLIP-rsicd/master/data/stadium_1.jpg"
image = Image.open(requests.get(url, stream=True).raw)

labels = ["residential area", "playground", "stadium", "forrest", "airport"]
inputs = processor(text=[f"a photo of a {l}" for l in labels], images=image, return_tensors="pt", padding=True)

outputs = model(**inputs)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
for l, p in zip(labels, probs[0]):
    print(f"{l:<16} {p:.4f}")

Try it on colab

✨ Features

• Designed to improve zero - shot image classification, text - to - image, and image - to - image retrieval on remote sensing images.
• Released several checkpoints for performance evaluation.

📦 Installation

To reproduce the fine - tuning procedure, you can use the released script. The model was trained using a batch size of 1024, an adafactor optimizer with linear warm - up and decay, and a peak learning rate of 1e - 4 on 1 TPU - v3 - 8. The full log of the training run can be found on WandB.

💻 Usage Examples

Basic Usage

from PIL import Image
import requests

from transformers import CLIPProcessor, CLIPModel

model = CLIPModel.from_pretrained("flax-community/clip-rsicd")
processor = CLIPProcessor.from_pretrained("flax-community/clip-rsicd")

url = "https://raw.githubusercontent.com/arampacha/CLIP-rsicd/master/data/stadium_1.jpg"
image = Image.open(requests.get(url, stream=True).raw)

labels = ["residential area", "playground", "stadium", "forrest", "airport"]
inputs = processor(text=[f"a photo of a {l}" for l in labels], images=image, return_tensors="pt", padding=True)

outputs = model(**inputs)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
for l, p in zip(labels, probs[0]):
    print(f"{l:<16} {p:.4f}")

📚 Documentation

• Fine - tuning CLIP on RSICD with HuggingFace and flax/jax on colab using TPU

🔧 Technical Details

Model Date

July 2021

Model Type

The base model uses a ViT - B/32 Transformer architecture as an image encoder and a masked self - attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss.

Model Version

We release several checkpoints for the clip-rsicd model. Refer to our github repo for performance metrics on zero - shot classification for each of those.

Demo

Check out the model's text - to - image and image - to - image capabilities using this demo.

📄 License

No license information provided in the original document.

Model Use

Intended Use

The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero - shot, arbitrary image classification.

In addition, we can imagine applications in defense and law enforcement, climate change and global warming, and even some consumer applications. A partial list of applications can be found here. In general, we think such models can be useful as digital assistants for humans engaged in searching through large collections of images.

We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.

Primary intended uses

The primary intended users of these models are AI researchers.

We primarily imagine the model will be used by researchers to better understand the robustness, generalization, and other capabilities, biases, and constraints of computer vision models.

Data

The model was trained on publicly available remote sensing image captions datasets, namely RSICD, UCM, and Sydney. More information on the datasets used can be found on our project page.

Performance and Limitations

Performance

Limitations

The model is fine - tuned on RSI data but can contain some biases and limitations of the original CLIP model. Refer to CLIP model card for details on those.