Initial version

README.md

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 ---
-title: Battle Of Image Classifiers
+title: Battle Of The Image Classifiers
 emoji: 🐨
 colorFrom: green
 colorTo: gray

app.py

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+import gradio as gr
+from transformers import pipeline
+
+model_names = [
+    "facebook/deit-base-patch16-224",
+    "facebook/convnext-base-224",
+    "google/vit-base-patch16-224",
+    "microsoft/resnet-50",
+    "microsoft/swin-base-patch4-window7-224",
+    "microsoft/beit-base-patch16-224",
+    "nvidia/mit-b0",
+    "shi-labs/nat-base-in1k-224",
+    "shi-labs/dinat-base-in1k-224"
+]
+
+
+def process(image_file, top_k, model_name):
+    p = pipeline("image-classification", model=model_name)
+    pred = p(image_file)
+    return {x["label"]: x["score"] for x in pred[:top_k]}
+
+# Inputs
+image = gr.Image(type="filepath", label="Upload an image")
+top_k = gr.Slider(minimum=1, maximum=10, step=1, value=5, label="Top k classes")
+model_selection = gr.Dropdown(model_names, label="Pick a model")
+
+# Output
+labels = gr.Label()
+
+description = "This Space lets you quickly compare the most popular image classifier models available on the hub. All of them have been fine-tuned on the ImageNet-1k dataset. Anecdotally, the three sample images have been generated with a Stable Diffusion model :)"
+
+iface = gr.Interface(
+    theme="huggingface",
+    description=description,
+    fn=process,
+    inputs=[image, top_k, model_selection],
+    outputs=[labels],
+    examples=[
+	["bike.jpg", 5, "google/vit-base-patch16-224"],
+	["car.jpg", 5, "microsoft/swin-base-patch4-window7-224"],
+	["food.jpg", 5, "facebook/convnext-base-224"]
+    ],
+    allow_flagging="never",
+)
+
+iface.launch()

requirements.txt

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+torch
+transformers>=4.25.1
+natten