Trying cods classif

app copy.py

@@ -0,0 +1,29 @@
+import gradio as gr
+from ultralytics import YOLO
+from PIL import Image
+
+# Load a pretrained YOLOv8n model
+model = YOLO("yolov8n.pt")
+
+
+def main_function(lbd, img):
+    results = model(img)  # predict on an image
+    r = results[0]
+    im_bgr = r.plot()  # BGR-order numpy array
+    im_rgb = Image.fromarray(im_bgr[..., ::-1])  # RGB-order PIL image
+    new_img = im_rgb
+    # res = results[0].save(filename="output.jpg")  # save the image
+    # # load image
+    # new_img = Image.open("output.jpg")
+    return new_img
+
+
+iface = gr.Interface(
+    fn=main_function,
+    inputs=["slider", gr.Image(type="pil")],
+    outputs=gr.Image(type="pil"),
+    examples=[
+        [0, "bus.jpg"],
+    ],
+)
+iface.launch()

app.py

@@ -1,6 +1,31 @@
 import gradio as gr
 from ultralytics import YOLO
 from PIL import Image
+from cods.classif.data import ClassificationDataset
+from cods.classif.models import ClassificationModel
+from cods.classif.cp import ClassificationConformalizer
+
+
+def classif(img):
+    model_name = "resnet34"
+    pretrained_resnet_34 = timm.create_model(model_name, pretrained=True)
+    classifier = ClassificationModel(model=pretrained_resnet_34, model_name=model_name)
+
+    val_dataset = ClassificationDataset(...)  # path to imagenet validation set
+
+    val_preds = classifier.build_predictions(
+        val_dataset,
+        dataset_name="imagenet",
+        split_name="cal",
+        batch_size=512,
+        shuffle=False,
+    )
+
+    cc = ClassificationConformalizer(method="lac", preprocess="softmax")
+    cc.lbd = 0.9
+    conf_cls = cc.conformalize(val_preds)
+    return str(conf_cls)
+
 
 # Load a pretrained YOLOv8n model
 model = YOLO("yolov8n.pt")
@@ -19,11 +44,11 @@ def main_function(lbd, img):
 
 
 iface = gr.Interface(
-    fn=main_function,
-    inputs=["slider", gr.Image(type="pil")],
-    outputs=gr.Image(type="pil"),
+    fn=classif,  # main_function,
+    inputs=gr.Image(type="pil"),  # ["slider", gr.Image(type="pil")],
+    outputs=gr.Textbox(),  # Image(type="pil"),
     examples=[
-        [0, "bus.jpg"],
+        "bus.jpg",  # [0, "bus.jpg"],
     ],
 )
 iface.launch()

requirements.txt

@@ -4,6 +4,7 @@ gradio
 opencv-python
 Pillow
 matplotlib
+timm
+cods @ git+https://github.com/leoandeol/cods@main
 
-
-ultralytics
+#ultralytics