Merge branch 'main' of https://huggingface.co/Hyunil/CSATv2

README.md

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+---
+datasets:
+- ILSVRC/imagenet-1k
+metrics:
+- accuracy
+---
+CSATv2
+
+CSATv2 is a lightweight high-resolution vision backbone designed to maximize throughput at 512×512 resolution.
+By applying frequency-domain projection 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 Projection** for removing redundant spatial information
+- 🎯 **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 **Demino** for contributing ideas and feedback that greatly helped in lightweighting and optimizing the model.
+
+Model description
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/633a801b7646c9f51a05cc92/pynK0OWbjH5WUlu8L7OTj.png)
+
+This model is designed primarily for image classification tasks and can also serve as a high-throughput backbone for object detection.
+```python
+import torch
+from datasets import load_dataset
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+
+# 예시 데이터: 고양이 이미지
+dataset = load_dataset("huggingface/cats-image")
+image = dataset["test"]["image"][0]
+
+# 👉 CSATv2 모델로 교체
+model_name = "Hyunil/CSATv2"
+
+# Preprocessor + Model 로드
+processor = AutoImageProcessor.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForImageClassification.from_pretrained(model_name, trust_remote_code=True)
+
+# 전처리
+inputs = processor(image, return_tensors="pt")
+
+# 추론
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+pred = logits.argmax(-1).item()
+print("Predicted label:", model.config.id2label[pred])
+```