🔄 GyroScope — Image Rotation Prediction
GyroScope is a ResNet-18 trained from scratch to detect whether an image is rotated by 0°, 90°, 180°, or 270° — and correct it automatically.
Is that photo upside down? Let GyroScope figure it out.
🎯 Task
Given any image, GyroScope classifies its orientation into one of 4 classes :
Label Meaning Correction 0 0° — upright ✅ None 1 90° CCW Rotate 270° CCW 2 180° — upside down Rotate 180° 3 270° CCW (= 90° CW) Rotate 90° CCW
Correction formula: correction = (360 − detected_angle) % 360
📊 Benchmarks
Trained on
50,000 images from
ImageNet-1k × 4 rotations =
200k training samples .
Validated on
5,000 images × 4 rotations =
20k validation samples .
Metric Value Overall Val Accuracy 79.81%% Per-class: 0° (upright) 79.8% Per-class: 90° CCW 80.1% Per-class: 180° 79.4% Per-class: 270° CCW 79.8% Training Epochs 12 Training Time ~4h (Kaggle T4 GPU)
Benchmark with benchmark.py
1 ===============
2 RESULTS
3 ===============
4 Overall result: 411/500 correct
5 Hit rate: 82.20 %
6 ------------------------------
7 Details per rotation class:
8 0° : 96/124 correct ( 77.42%)
9 90° : 103/119 correct ( 86.55%)
10 180° : 112/129 correct ( 86.82%)
11 270° : 100/128 correct ( 78.12%)
12 ==============================
You can also do this benchmark, by using benchmark.py in this repo. :D
Training Curve
Epoch Train Acc Val Acc 1 41.4% 43.2% 2 52.0% 46.9% 3 59.4% 62.8% 4 64.1% 66.0% 5 67.8% 69.48% 6 70.6% 72.22% 7 73.3% 74.25% 8 75.6% 76.49% 9 77.5% 77.47% 10 79.1% 79.47% 11 80.3% 79.78% 12 80.9% 79.81%
🏗️ Architecture
Detail Value Base ResNet-18 (from scratch, no pretrained weights ) Parameters 11.2M Input 224 × 224 RGB Output 4 classes (0°, 90°, 180°, 270°) Framework 🤗 Hugging Face Transformers (ResNetForImageClassification)
Training Details
Optimizer: AdamW (lr=1e-3, weight_decay=0.05)Scheduler: Cosine annealing with 1-epoch linear warmupLoss: CrossEntropy with label smoothing (0.1)Augmentations: RandomCrop, ColorJitter, RandomGrayscale, RandomErasing⚠️ No flips — horizontal/vertical flips would corrupt rotation labelsMixed precision: FP16 via torch.cuda.amp
🚀 Quick Start
Installation
pip install transformers torch torchvision pillow requestsInference — Single Image from URL
--> Download use_with_UI.py first 😄
💡 Example
Input (rotated 180°):
cat image, rotated to the left by 90° GyroScope Output:
📐 Recognized: 90° | Correction: 270°
📊 Probs: {'0°': '0.0257', '90°': '0.8706', '180°': '0.0735', '270°': '0.0300'}
cat image, now correctly rotated Original Image Source: Link to Pexels ⚠️ Limitations
Rotationally symmetric images (balls, textures, patterns) are inherently ambiguous — no model can reliably classify these.
Trained on natural images (ImageNet). Performance may degrade on:
Documents / text-heavy images
Medical imaging
Satellite / aerial imagery
Abstract art
Only handles 90° increments — arbitrary angles (e.g. 45° or 135°) are not supported !
Trained from scratch on 50k images — a pretrained backbone would likely yield higher accuracy (Finetuning).
📝 Use Cases
📸 Photo management — auto-correct phone/camera orientation
🗂️ Data preprocessing — fix rotated images in scraped datasets
🤖 ML pipelines — orientation normalization before feeding to downstream models
🖼️ Digital archives — batch-correct scanned/uploaded images
Yesterday, I was sorting photos and like every photo was rotated wrong! This inspired me to make this tool 😂
💻 Training code
The full training code can be found in train.py. Have fun 😊
📜 License
Apache 2.0
🙏 Acknowledgments
Dataset: ILSVRC/ImageNet-1k
Architecture: Microsoft ResNet via 🤗 Transformers
Trained on Kaggle (Tesla T4 GPU)
GyroScope — because every image deserves to stand upright.
