Files Added

README.md

@@ -1,3 +1,12 @@
 ---
-license: other
+title: Battle Of The Image Classifiers
+emoji: 🐨
+colorFrom: green
+colorTo: gray
+sdk: gradio
+sdk_version: 3.16.1
+app_file: app.py
+pinned: false
 ---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\iitsi\\anaconda3\\lib\\site-packages\\gradio\\blocks.py:503: UserWarning: Cannot load huggingface. Caught Exception: The space huggingface does not exist\n",
+      "  warnings.warn(f\"Cannot load {theme}. Caught Exception: {str(e)}\")\n",
+      "c:\\Users\\iitsi\\anaconda3\\lib\\site-packages\\gradio\\deprecation.py:40: UserWarning: `layout` parameter is deprecated, and it has no effect\n",
+      "  warnings.warn(value)\n",
+      "ERROR:    [Errno 10048] error while attempting to bind on address ('127.0.0.1', 7864): only one usage of each socket address (protocol/network address/port) is normally permitted\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "from transformers import pipeline\n",
+    "\n",
+    "model_names = [\n",
+    "    \"apple/mobilevit-small\",\n",
+    "    \"facebook/deit-base-patch16-224\",\n",
+    "    \"facebook/convnext-base-224\",\n",
+    "    \"google/vit-base-patch16-224\",\n",
+    "    \"google/mobilenet_v2_1.4_224\",\n",
+    "    \"microsoft/resnet-50\",\n",
+    "    \"microsoft/swin-base-patch4-window7-224\",\n",
+    "    \"microsoft/beit-base-patch16-224\",\n",
+    "    \"nvidia/mit-b0\",\n",
+    "    \"shi-labs/nat-base-in1k-224\",\n",
+    "    \"shi-labs/dinat-base-in1k-224\",\n",
+    "]\n",
+    "\n",
+    "\n",
+    "def process(image_file, top_k):\n",
+    "    labels = []\n",
+    "    for m in model_names:\n",
+    "        p = pipeline(\"image-classification\", model=m)\n",
+    "        pred = p(image_file)\n",
+    "        labels.append({x[\"label\"]: x[\"score\"] for x in pred[:top_k]})\n",
+    "    return labels\n",
+    "\n",
+    "\n",
+    "# Inputs\n",
+    "image = gr.Image(type=\"filepath\", label=\"Upload an image\")\n",
+    "top_k = gr.Slider(minimum=1, maximum=5, step=1, value=5, label=\"Top k classes\")\n",
+    "\n",
+    "# Output\n",
+    "labels = [gr.Label(label=m) for m in model_names]\n",
+    "\n",
+    "description = \"This Space lets you quickly compare the most popular image classifiers available on the hub, including the recent NAT and DINAT models. 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 :)\"\n",
+    "\n",
+    "iface = gr.Interface(\n",
+    "    theme=\"huggingface\",\n",
+    "    description=description,\n",
+    "    layout=\"horizontal\",\n",
+    "    fn=process,\n",
+    "    inputs=[image, top_k],\n",
+    "    outputs=labels,\n",
+    "    examples=[\n",
+    "        [\"bike.jpg\", 5],\n",
+    "        [\"car.jpg\", 5],\n",
+    "        [\"food.jpg\", 5],\n",
+    "    ],\n",
+    "    allow_flagging=\"never\",\n",
+    ")\n",
+    "\n",
+    "iface.launch()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "a10896378c7586fe060a2352f1f55c7416a5095f8b10e25e98ff567d7ae9c79d"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

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+torch==1.13.1
+transformers>=4.25.1
+https://shi-labs.com/natten/wheels/cpu/torch1.13/natten-0.14.4%2Btorch1130cpu-cp38-cp38-linux_x86_64.whl