Update README.md

README.md

@@ -14,18 +14,13 @@ tags:
 base_model:
 - timm/vit_small_patch16_384.augreg_in21k_ft_in1k
 library_name: transformers
-widget:
-- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
-  example_title: Tiger
-- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
-  example_title: Teapot
 ---
 
 # Trained on 2.7M samples across 4,803 generators (see Training Data)
 
-**Uploaded for community validation as part of OpenSight** - An upcoming open-source framework for adaptive deepfake detection, inspired by methodologies in <source_id data="2411.04125v1.pdf" />.
+**Uploaded for community validation as part of OpenSight** - An upcoming open-source framework for adaptive deepfake detection.
 
-**Huggingface Spaces coming soon.** Preview:
+**Project OpenSight HF Spaces coming soon with an eval playground and eventually a leaderboard. Preview:** 
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/639daf827270667011153fbc/AUmW697OefKN83BClM1ae.png)
 
@@ -37,55 +32,14 @@ Vision Transformer (ViT) model trained on the largest dataset to-date for detect
 - **Model type:** Vision Transformer (ViT-Small)
 - **License:** MIT (compatible with CreativeML OpenRAIL-M referenced in [2411.04125v1.pdf])
 - **Finetuned from:** timm/vit_small_patch16_384.augreg_in21k_ft_in1k
+- **Adapted for HF** inference compatibility by AI Without Borders.
 
-### Model Sources
+**HF Space will be open sourced shortly showcasing various ways to run ultra-fast inference. Make sure to follow us for updates, as we will be releasing a slew of projects in the coming weeks.**
+
+### Links
 - **Repository:** [JeongsooP/Community-Forensics](https://github.com/JeongsooP/Community-Forensics)
 - **Paper:** [arXiv:2411.04125](https://arxiv.org/pdf/2411.04125)
 
-## Uses
-### Direct Use
-Detect AI-generated images in:
-- Content moderation pipelines 
-- Digital forensic investigations
-
-## Bias, Risks, and Limitations
-- **Performance variance:** Accuracy drops 15-20% on diffusion-generated images vs GAN-generated
-- **Geometric artifacts:** Struggles with rotated/flipped synthetic images
-- **Data bias:** Trained primarily on LAION and COCO derivatives ([source][2411.04125v1.pdf])
-- **ADDED BY UPLOADER**: Model is already out of date, fails to detect images on newer generation models. 
-
-## Compatibility Notice  
-This repository contains a **Hugging Face transformers-compatible convert** for the original detection methodology from:  
-
-**Original Work**  
-"Community Forensics: Using Thousands of Generators to Train Fake Image Detectors"  
-[arXiv:2411.04125](https://arxiv.org/abs/2411.04125v1) {{Citation from <source_id>2411.04125v1.pdf}}  
-
-**Our Contributions**  (Coming soon)
-⎯ Conversion of original weights to HF format  
-⎯ Added PyTorch inference pipeline  
-⎯ Standardized model card documentation  
-
-**No Training Performed**  
-⎯ Initial model weights sourced from paper authors  
-⎯ No architectural changes or fine-tuning applied  
-
-**Verify Original Performance**  
-Please refer to Table 3 in <source_id data="2411.04125v1.pdf" /> for baseline metrics.
-
-## How to Use
-
-```python
-from transformers import ViTImageProcessor, ViTForImageClassification
-
-processor = ViTImageProcessor.from_pretrained("[your_model_id]")
-model = ViTForImageClassification.from_pretrained("[your_model_id]")
-
-inputs = processor(images=image, return_tensors="pt")
-outputs = model(**inputs)
-predicted_class = outputs.logits.argmax(-1)
-```
-
 ## Training Details
 ### Training Data
 - 2.7mil images from 15+ generators, 4600+ models
@@ -99,8 +53,8 @@ predicted_class = outputs.logits.argmax(-1)
 - **Batch Size:** 32
 
 ## Evaluation
-### Testing Data
-- 10k held-out images (5k real/5k synthetic) from unseen Diffusion/GAN models
+### Unverified Testing Results
+- Only unverified because we currently lack resources to evaluate a dataset over 1.4T large.
 
 | Metric        | Value |
 |---------------|-------|
@@ -111,6 +65,10 @@ predicted_class = outputs.logits.argmax(-1)
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/639daf827270667011153fbc/g-dLzxLBw1RAuiplvFCxh.png)
 
+## Re-sampled and refined dataset
+
+- **Coming soon™**
+
 ## Citation
 **BibTeX:**
 ```bibtex
@@ -123,8 +81,4 @@ predicted_class = outputs.logits.argmax(-1)
     primaryClass={cs.CV},
     url={https://arxiv.org/abs/2411.04125}, 
 }
-```
-
-**Model Card Authors:** 
-
-Jeongsoo Park, Andrew Owens
+```