import streamlit as st import pandas as pd from backend.utils import load_dataset, use_container_width_percentage st.set_page_config(layout='wide') st.title('ImageNet-1k') st.markdown('This page shows the summary of 50,000 images in the validation set of [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k)') # SCREEN_WIDTH, SCREEN_HEIGHT = 2560, 1664 with st.spinner("Loading dataset..."): dataset_dict = {} for data_index in range(5): dataset_dict[data_index] = load_dataset(data_index) imagenet_df = pd.read_csv('./data/ImageNet_metadata.csv') class_labels = imagenet_df.ClassLabel.unique().tolist() class_labels.sort() selected_classes = st.multiselect('Class filter: ', options=['All'] + class_labels) if not ('All' in selected_classes or len(selected_classes) == 0): imagenet_df = imagenet_df[imagenet_df['ClassLabel'].isin(selected_classes)] # st.write(class_labels) col1, col2 = st.columns([2, 1]) with col1: st.dataframe(imagenet_df) use_container_width_percentage(100) with col2: st.text_area('Type anything here to copy later :)') image = None with st.form("display image"): img_index = st.text_input('Image ID to display') submitted = st.form_submit_button('Display this image') error_container = st.empty() if submitted: try: img_index = int(img_index) if img_index > 50000-1 or img_index < 0: error_container.error('The Image ID must be in range from 0 to 50000', icon="🚫") else: image = dataset_dict[img_index//10_000][img_index%10_000]['image'] class_label = dataset_dict[img_index//10_000][img_index%10_000]['label'] class_id = dataset_dict[img_index//10_000][img_index%10_000]['id'] except ValueError: error_container.error('Please enter an integer number for Image ID', icon = "🚫") if image != None: st.image(image) st.write('**Class label:** ', class_label) st.write('\n**Class id:** ', str(class_id))