| --- |
| datasets: |
| - ILSVRC/imagenet-1k |
| metrics: |
| - accuracy |
| license: apache-2.0 |
| --- |
| # CSATv2 |
|
|
| CSATv2 is a lightweight high-resolution vision backbone designed to maximize throughput at 512×512 resolution. |
| By applying frequency-domain compression at the input stage, the model suppresses redundant spatial information and achieves extremely fast inference. |
|
|
| ## Highlights |
|
|
| - 🚀 **2,800 images/s at 512×512 resolution (A6000 1×GPU)** |
| - ⚡ **Frequency-domain compression** for lightweight and efficient modeling |
| - 🎯 **80.02%** ImageNet-1K Top-1 Accuracy |
| - 🪶 Only **11M parameters** |
| - 🧩 Suitable for **image classification** or as a **high-throughput detection backbone** |
|
|
| This model is an improved version of the architecture used in the [paper](https://www.mdpi.com/2306-5354/10/11/1279) |
|
|
| Special thanks to **Juno** for contributing ideas and feedback that greatly helped in lightweighting and optimizing the model. |
|
|
| ### Background and Motivation |
|
|
| In computational pathology, a single whole-slide image (WSI) is typically |
| partitioned into thousands to tens of thousands of high-resolution image |
| patches (e.g., 512×512 pixels) for analysis. |
|
|
| This setting places strong constraints on both throughput and latency: |
| even small inefficiencies in patch-level inference can lead to |
| prohibitively long end-to-end processing times at the slide level. |
|
|
| CSATv2 was originally designed to address this constraint by enabling |
| high-throughput, high-resolution inference while preserving classification |
| accuracy. In practical deployments, this design reduced slide-level |
| processing time from tens of minutes to approximately one minute, |
| making near–real-time pathological analysis feasible at scale. |
|
|
|
|
| ## Model description |
|
|
|  |
|
|
| ## Training Details |
|
|
| The model was trained on ImageNet-1K using a high-resolution training |
| pipeline adapted from common ImageNet training practices. |
|
|
| - Dataset: ImageNet-1K |
| - Input resolution: 512×512 |
| - Model: CSATv2 |
| - Optimizer: AdamW |
| - Learning rate: 2e-3 |
| - Learning rate schedule: Cosine |
| - Epochs: 300 |
| - Warmup epochs: 5 |
| - Weight decay: 2e-2 |
| - Batch size: 128 (per GPU) |
| - Mixed precision training: Enabled (AMP) |
|
|
| ### Data Augmentation |
| - Random resized crop (scale: 0.08–1.0, ratio: 3/4–4/3) |
| - Horizontal flip (p = 0.5) |
| - RandAugment (rand-m7-mstd0.5-inc1) |
| - Mixup (α = 0.8) |
| - CutMix (α = 1.0) |
| - Bicubic interpolation |
|
|
| ### Regularization |
| - Label smoothing: Disabled (handled implicitly via Mixup / CutMix) |
| - Dropout / DropPath: Disabled |
| - Random erase: Disabled |
|
|
| ### Optimization Details |
| - Exponential Moving Average (EMA): Enabled |
| - EMA decay: 0.99996 |
| - Gradient clipping: Disabled |
| - Channels-last memory format: Optional |
|
|
| The training pipeline was adapted from publicly available ImageNet |
| training repositories (Solving ImageNet), |
| with task-specific modifications for high-resolution and |
| high-throughput training. |
|
|
|
|
| ## Usage |
| ```python |
| import torch |
| from datasets import load_dataset |
| from transformers import AutoImageProcessor, AutoModelForImageClassification |
| |
| # example data : cat image |
| dataset = load_dataset("huggingface/cats-image") |
| image = dataset["test"]["image"][0] |
| |
| # 👉 CSATv2 |
| model_name = "Hyunil/CSATv2" |
| |
| # Preprocessor + Model Load |
| processor = AutoImageProcessor.from_pretrained(model_name, trust_remote_code=True) |
| model = AutoModelForImageClassification.from_pretrained(model_name, trust_remote_code=True) |
| |
| # Preprocessing |
| inputs = processor(image, return_tensors="pt") |
| |
| # Inference |
| with torch.no_grad(): |
| logits = model(**inputs).logits |
| |
| pred = logits.argmax(-1).item() |
| print("Predicted label:", model.config.id2label[pred]) |
| ``` |
|
|
| ### Contact |
| This project was conducted by members of MLPA Lab. |
| Welcome feedback and suggestion, questions. |
| - gusdlf93@naver.com |
| - juno.demie.oh@gmail.com |
