added code and files

.gitattributes

@@ -31,4 +31,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.xz filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
+*.index filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+

app.py

@@ -0,0 +1,87 @@
+import os
+import streamlit as st
+from PIL import Image
+
+from inference import get_predictions, get_nearest_k
+
+
+st.title('Fashion accessories prediction and search Demo')
+
+sample_files = os.listdir('./data/small_images')
+tot_index = len(sample_files)
+sample_path = './data/small_images'
+
+if 'image_index' not in st.session_state:
+    st.session_state['image_index'] = 0
+
+if 'which_button' not in st.session_state:
+    st.session_state['which_button'] = 'sample_button'
+
+sample_col, upload_col = st.tabs(['Select from sample images', 'Upload file'])
+with upload_col:
+    use_uploaded_image = True
+    uploaded_file = st.file_uploader("Select a picture from your computer(png/jpg) :", type=['png', 'jpg', 'jpeg'])
+    if uploaded_file is not None:
+        img = Image.open(uploaded_file)
+        st.image(img, caption='Uploaded Image')
+        use_uploaded_image = st.button("Use uploaded image")
+        if use_uploaded_image is True:
+            st.session_state['which_button'] = 'upload_button'
+
+with sample_col:
+    use_sample_image = True
+    st.write("Select one from these available samples: ")
+    current_index = st.session_state['image_index']
+    current_image = Image.open(os.path.join(sample_path, sample_files[current_index]))
+
+    prev_button, next_button = st.columns(2)
+    with prev_button:
+        prev = st.button('prev_image')
+    with next_button:
+        next = st.button('next_image')
+    if prev:
+        current_index = (current_index - 1) % tot_index
+    if next:
+        current_index = (current_index + 1) % tot_index
+    st.session_state['image_index'] = current_index
+    sample_image = Image.open(os.path.join(sample_path, sample_files[current_index]))
+    st.image(sample_image, caption='Chosen image')
+
+    use_sample_image = st.button("Use this Sample")
+    if use_sample_image is True:
+        st.session_state['which_button'] = 'sample_button'
+
+classification_button, search_button = st.columns(2)
+with classification_button:
+    predict_clicked = st.button("Get categories predictions")
+
+with search_button:
+    search_clicked = st.button("Get similar looking products")
+
+if predict_clicked:
+    which_button = st.session_state['which_button']
+    if which_button == 'sample_button':
+        predictions = get_predictions(sample_image)
+    elif which_button == 'upload_button':
+        predictions = get_predictions(img)
+    st.markdown('**The model predictions along with their probabilities are :**')
+    st.table(predictions)
+elif search_clicked:
+    which_button = st.session_state['which_button']
+    if which_button == 'sample_button':
+        top_k_preds = get_nearest_k(sample_image)
+    elif which_button == 'upload_button':
+        top_k_preds = get_nearest_k(img)
+    all_distances = top_k_preds[0][0]
+    all_valid_distances = [dist for dist in all_distances if dist < 300]
+    pred_to_show = len(all_valid_distances)
+    st.markdown('**The top 5 similar product predictions are :**')
+    if pred_to_show == 0:
+        st.markdown('No similar visually looking similar products found in the database.')
+    else:
+        pred_cols = st.columns(pred_to_show)
+        for i in range(len(pred_cols)):
+            with pred_cols[i]:
+                temp_img = Image.open(os.path.join(sample_path, str(top_k_preds[1][0][i])+'.jpg'))
+                st.image(temp_img, caption=str(round(top_k_preds[0][0][i], 2))+' distance')
+

data/.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,1122 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "10cc8208",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import shutil"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f724713d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orig_dir = os.path.join('./small_images/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "37ae5254",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_filenames = os.listdir(orig_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2947c1e4",
+   "metadata": {},
+   "outputs": [
+    {
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+       " '2628.jpg',\n",
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+       " '355.jpg',\n",
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+       " '17454.jpg',\n",
+       " '10017.jpg',\n",
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+       " '35478.jpg',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_filenames"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "bb0be752",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dest_dir1 = os.path.join('./small_images_0_9999')\n",
+    "dest_dir2 = os.path.join('./small_images_10000_19999')\n",
+    "dest_dir3 = os.path.join('./small_images_20000_29999')\n",
+    "dest_dir4 = os.path.join('./small_images_30000_39999')\n",
+    "dest_dir5 = os.path.join('./small_images_40000_49999')\n",
+    "dest_dir6 = os.path.join('./small_images_50000_59999')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "767c19f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for file in all_filenames:\n",
+    "    if int(file[0:-4]) < 9999:\n",
+    "        shutil.copyfile(os.path.join(orig_dir, file), os.path.join(dest_dir1, file))\n",
+    "    elif int(file[0:-4]) < 19999:\n",
+    "        shutil.copyfile(os.path.join(orig_dir, file), os.path.join(dest_dir2, file))\n",
+    "    elif int(file[0:-4]) < 29999:\n",
+    "        shutil.copyfile(os.path.join(orig_dir, file), os.path.join(dest_dir3, file))\n",
+    "    elif int(file[0:-4]) < 39999:\n",
+    "        shutil.copyfile(os.path.join(orig_dir, file), os.path.join(dest_dir4, file))\n",
+    "    elif int(file[0:-4]) < 49999:\n",
+    "        shutil.copyfile(os.path.join(orig_dir, file), os.path.join(dest_dir5, file))\n",
+    "    elif int(file[0:-4]) < 59999:\n",
+    "        shutil.copyfile(os.path.join(orig_dir, file), os.path.join(dest_dir6, file))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec86d4a9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.8.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

data/final-models/resnet_152_classification.pt

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+size 271396293

data/index_files/resnet152_unweighted_flat.index

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