🐛 docs(standardization): update categorical listings

- correct duplicate usages across readme/body
- classify 'prithivMLmods/Deepfake-Detection-Exp-02-22', formerly wrongly marked between both description sections
- explicitly denote 'prithivMLmods/Deepfake-Detection-Exp-02-22'

- enumerate visible models to clearly distinguish from previous instance models

🐛 fix(model): augment model selections
- add model
- add additional entry of 'ideepankarsharma2003/AI_ImageClassification_MidjourneyV6_SDXL' to MODEL_PATHS

🐛 refactor(model): consolidating model usage
- add model
- add clf_6, feature_extractor_6, and model_6 in prediction function to distinguish newly added (ideepankarsharma2003/AI_ImageClassification_MidjourneyV6_SDXL') classes, respectively
- modify separate statements to assign return to completed function

🐛 refactor(model): improving label classification
- update classification of previously unlisted model 6 in proper configuration

README.md

@@ -13,6 +13,7 @@ preload_from_hub:
   - cmckinle/sdxl-flux-detector
   - Organika/sdxl-detector
   - prithivMLmods/Deep-Fake-Detector-v2-Model
+  - prithivMLmods/Deepfake-Detection-Exp-02-22
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -26,7 +26,8 @@ MODEL_PATHS = {
     "model_3": "Organika/sdxl-detector",
     "model_4": "cmckinle/sdxl-flux-detector",
     "model_5": "prithivMLmods/Deep-Fake-Detector-v2-Model",
-    "model_5b": "prithivMLmods/Deepfake-Detection-Exp-02-22"
+    "model_5b": "prithivMLmods/Deepfake-Detection-Exp-02-22",
+    "model_6": "ideepankarsharma2003/AI_ImageClassification_MidjourneyV6_SDXL"
 }
 
 CLASS_NAMES = {
@@ -35,7 +36,9 @@ CLASS_NAMES = {
     "model_3": ['AI', 'Real'],
     "model_4": ['AI', 'Real'],
     "model_5": ['Realism', 'Deepfake'],
-    "model_5b": ['Real', 'Deepfake']
+    "model_5b": ['Real', 'Deepfake'],
+    "model_6": ['ai_gen', 'human'],
+
 }
 
 # Load models and processors
@@ -45,6 +48,11 @@ def load_models():
     model_1 = model_1.to(device)
     clf_1 = pipeline(model=model_1, task="image-classification", image_processor=image_processor_1, device=device)
 
+    image_processor_6 = AutoImageProcessor.from_pretrained(MODEL_PATHS["model_6"], use_fast=True)
+    model_6 = Swinv2ForImageClassification.from_pretrained(MODEL_PATHS["model_6"])
+    model_6 = model_6.to(device)
+    clf_6 = pipeline(model=model_6, task="image-classification", image_processor=image_processor_6, device=device)
+
     clf_2 = pipeline("image-classification", model=MODEL_PATHS["model_2"], device=device)
 
     feature_extractor_3 = AutoFeatureExtractor.from_pretrained(MODEL_PATHS["model_3"], device=device)
@@ -56,9 +64,9 @@ def load_models():
     clf_5 = pipeline("image-classification", model=MODEL_PATHS["model_5"], device=device)
     clf_5b = pipeline("image-classification", model=MODEL_PATHS["model_5b"], device=device)
 
-    return clf_1, clf_2, feature_extractor_3, model_3, feature_extractor_4, model_4, clf_5, clf_5b
+    return clf_1, clf_2, feature_extractor_3, model_3, feature_extractor_4, model_4, clf_5, clf_5b, clf_6
 
-clf_1, clf_2, feature_extractor_3, model_3, feature_extractor_4, model_4, clf_5, clf_5b = load_models()
+clf_1, clf_2, feature_extractor_3, model_3, feature_extractor_4, model_4, clf_5, clf_5b, clf_6 = load_models()
 
 @spaces.GPU(duration=10)
 def predict_with_model(img_pil, clf, class_names, confidence_threshold, model_name, model_id, feature_extractor=None):
@@ -110,6 +118,7 @@ def predict_image(img, confidence_threshold):
     label_4, result_4output = predict_with_model(img_pil, model_4, CLASS_NAMES["model_4"], confidence_threshold, "SDXL + FLUX", 4, feature_extractor_4)
     label_5, result_5output = predict_with_model(img_pilvits, clf_5, CLASS_NAMES["model_5"], confidence_threshold, "ViT-base Newcomer", 5)
     label_5b, result_5boutput = predict_with_model(img_pilvits, clf_5b, CLASS_NAMES["model_5b"], confidence_threshold, "ViT-base Newcomer", 6)
+    label_6, result_6output = predict_with_model(img_pilvits, clf_6, CLASS_NAMES["model_6"], confidence_threshold, "Swin Midjourney/SDXL", 6)
 
     combined_results = {
         "SwinV2/detect": label_1,
@@ -117,10 +126,11 @@ def predict_image(img, confidence_threshold):
         "Swin/SDXL": label_3,
         "Swin/SDXL-FLUX": label_4,
         "prithivMLmods": label_5,
-        "prithivMLmods-2-22": label_5b
+        "prithivMLmods-2-22": label_5b,
+        "SwinMidSDXL": label_6
     }
 
-    combined_outputs = [result_1output, result_2output, result_3output, result_4output, result_5output, result_5boutput]
+    combined_outputs = [result_1output, result_2output, result_3output, result_4output, result_5output, result_5boutput, result_6output]
     return img_pil, combined_outputs
 # Define a function to generate the HTML content