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ImageNet-1K Alternative Data Sources

Since the Kaggle competition has ended, here are alternative ways to get ImageNet-1K:

Option 1: ImageNet Official Website (Requires Academic Email)

Best for: Academic/research use Time: 1-2 days for approval + download time

  1. Go to: https://image-net.org/download-images.php
  2. Register with academic email
  3. Request access to ILSVRC2012 dataset
  4. Wait for approval (usually 1-2 days)
  5. Download and upload to Google Drive
  6. Mount Drive in Colab

Option 2: Use ImageNet-100 First (Already Validated)

Best for: Quick validation that AST scales beyond CIFAR-10 Time: Available now via Kaggle

# In Colab, you can download ImageNet-100 directly
!pip install kaggle
!kaggle datasets download -d ambityga/imagenet100

# This is the dataset you ALREADY validated with 92.12% accuracy!
# Shows AST scales from CIFAR-10 (60K) → ImageNet-100 (126K)

Why this makes sense:

  • You already proved AST works on ImageNet-100 (92.12% accuracy, 61% savings)
  • ImageNet-100 is 10× larger than CIFAR-10
  • You can announce: "AST scales from 60K to 126K images" immediately
  • ImageNet-1K can be done later for the full paper

Option 3: Alternative ImageNet-1K Sources

A. Academic Torrents

Website: https://academictorrents.com/details/a306397ccf9c2ead27155983c254227c0fd938e2
Pros: Free, no approval needed
Cons: Torrent download (slower)

B. Cloud Storage

Some universities host ImageNet on cloud storage for research purposes. Check your university's research computing resources.

C. Pre-downloaded Google Drive

If you have access to someone who already has ImageNet-1K, they can share via Google Drive.

Option 4: Use TensorFlow Datasets (Streaming)

Best for: Avoiding large downloads Time: Immediate

import tensorflow_datasets as tfds

# This streams ImageNet without downloading all 150GB
ds = tfds.load('imagenet2012', split='train', shuffle_files=True)

Caveat: Requires adapting your PyTorch code to work with TensorFlow datasets

Option 5: Use Hugging Face Datasets

from datasets import load_dataset

# Hugging Face hosts ImageNet (may require authentication)
dataset = load_dataset("imagenet-1k")

Recommended Path Forward

For Immediate Announcement:

Use ImageNet-100 (you already have results):

  • ✅ 92.12% accuracy
  • ✅ 61.5% energy savings
  • ✅ Scales from CIFAR-10 (60K) → ImageNet-100 (126K) → 10× increase
  • ✅ Published to PyPI
  • ✅ Ready to announce NOW

Announcement: "AST achieves 92% accuracy on ImageNet-100 with 61% energy savings"

For Full ImageNet-1K Validation (Later):

  1. Register at ImageNet.org (takes 1-2 days)
  2. Download to local machine
  3. Upload to Google Drive (one-time cost)
  4. Run Conservative config for publication-quality results

This way you can:

  • ✅ Announce results NOW with ImageNet-100
  • ✅ Add ImageNet-1K results later for the paper

Quick Decision Matrix

Goal Best Option Time Status
Announce AST now ImageNet-100 0 (done!) ✅ Ready
Quick validation ImageNet-100 0 (done!) ✅ Ready
Full paper ImageNet.org → Drive 2-3 days ⏳ Pending
Streaming option TF Datasets 1 hour setup 🔧 Requires code changes

My Recommendation: Announce with ImageNet-100 results NOW (you already have publication-quality results!), then work on ImageNet-1K for the full paper.