Create README.md

README.md

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+---
+datasets:
+- ideepankarsharma2003/ImageClassificationStableDiffusion_small
+- ideepankarsharma2003/Midjourney_v6_Classification_small_shuffled
+- ideepankarsharma2003/AIGeneratedImages_Midjourney
+tags:
+- image-classification
+- ai-gen-images
+---
+
+
+# Model Card for AI Image Classification - Midjourney V6 & SDXL  
+
+## Model Details  
+
+### Model Description  
+
+This model is a **Swin Transformer-based classifier** designed to distinguish between **AI-generated** and **human-created** images, specifically focusing on outputs from **Midjourney V6** and **Stable Diffusion XL (SDXL)**. It has been trained on a curated dataset of AI-generated images.  
+
+- **Developed by:** Deepankar Sharma  
+- **Model type:** Image Classification (Swin Transformer)  
+- **Finetuned from model:** SwinForImageClassification  
+
+### Model Sources  
+
+- **Repository:** [Hugging Face Model Repository](https://huggingface.co/ideepankarsharma2003/AI_ImageClassification_MidjourneyV6_SDXL)  
+
+## Uses  
+
+### Direct Use  
+
+This model can be used for **detecting AI-generated images** from Midjourney V6 and SDXL. It is useful for content moderation, fact-checking, and detecting synthetic media.  
+
+### Out-of-Scope Use  
+
+- The model is **not designed** for detecting AI-generated images from all generative models.  
+- It **may not perform well** on heavily edited AI-generated images or images mixed with human elements.  
+- It is **not intended for forensic-level deepfake detection**.  
+
+## Bias, Risks, and Limitations  
+
+This model is trained specifically on **Midjourney V6** and **Stable Diffusion XL** datasets. It may not generalize well to images generated by other AI models. Additionally, biases in the dataset could lead to **false positives** (flagging real images as AI-generated) or **false negatives** (failing to detect AI-generated content).  
+
+### Recommendations  
+
+Users should verify results with additional tools and **not solely rely on this model** for high-stakes decisions. Model performance should be tested on domain-specific datasets before deployment.  
+
+## How to Get Started with the Model  
+
+You can use this model with the 🤗 Transformers library:  
+
+```python
+from transformers import AutoModelForImageClassification, AutoFeatureExtractor
+from PIL import Image
+import torch
+
+# Load model and feature extractor
+model_name = "ideepankarsharma2003/AI_ImageClassification_MidjourneyV6_SDXL"
+model = AutoModelForImageClassification.from_pretrained(model_name)
+feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
+
+# Load and preprocess image
+image = Image.open("path_to_image.jpg")
+inputs = feature_extractor(images=image, return_tensors="pt")
+
+# Perform inference
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_label = logits.argmax(-1).item()
+
+# Label Mapping
+id2label = {0: "ai_gen", 1: "human"}
+print("Predicted label:", id2label[predicted_label])
+```
+
+## Training Details  
+
+### Training Data  
+
+The model was trained on the following datasets:  
+
+- [ImageClassificationStableDiffusion_small](https://huggingface.co/datasets/ideepankarsharma2003/ImageClassificationStableDiffusion_small)  
+- [Midjourney_v6_Classification_small_shuffled](https://huggingface.co/datasets/ideepankarsharma2003/Midjourney_v6_Classification_small_shuffled)  
+- [AIGeneratedImages_Midjourney](https://huggingface.co/datasets/ideepankarsharma2003/AIGeneratedImages_Midjourney)  
+
+### Training Procedure  
+
+- **Image Size:** 224x224  
+- **Patch Size:** 4  
+- **Embedding Dimension:** 128  
+- **Layers:** 4  
+- **Attention Heads per Stage:** [4, 8, 16, 32]  
+- **Dropout Rates:**  
+  - Attention: 0.0  
+  - Hidden: 0.0  
+  - Drop Path: 0.1  
+- **Activation Function:** GeLU  
+- **Optimizer:** AdamW  
+- **Learning Rate Scheduler:** Cosine Annealing  
+- **Precision:** float32  
+- **Training Steps:** 3414  
+
+## Evaluation  
+
+### Testing Data, Factors & Metrics  
+
+#### Testing Data  
+
+The model was evaluated on a separate validation split from the training datasets.  
+
+#### Metrics  
+
+- **Accuracy**  
+- **Precision & Recall**  
+- **F1 Score**  
+
+### Summary  
+
+The model effectively distinguishes between AI-generated and human-created images, but its performance may be affected by dataset biases and out-of-distribution examples.  
+
+## Citation  
+
+If you use this model, please cite:  
+
+```bibtex
+@misc{ai_image_classification,
+  author = {Deepankar Sharma},
+  title = {AI Image Classification - Midjourney V6 & SDXL},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/ideepankarsharma2003/AI_ImageClassification_MidjourneyV6_SDXL}}
+}
+```
+
+## Model Card Authors  
+
+- **Author:** Deepankar Sharma  
+
+---