From 7e89697b8f88955c293d426cf1ebd273c00059d2 Mon Sep 17 00:00:00 2001
From: Julio Cesar Contreras Huerta
+ +
+ + +### **2. SWIN Models** SWIN models are optimized for varying levels of detail and offer size options: `lightweight`, `small`, `medium`, and `expanded`. ```python @@ -184,7 +214,13 @@ Available sizes: - `medium` - `expanded` -#### **3. MAMBA Models** + ++ +
+ + +### **3. MAMBA Models** MAMBA models also come in various sizes, similar to SWIN and CNN: `lightweight`, `small`, `medium`, and `expanded`. ```python @@ -205,8 +241,13 @@ Available sizes: - `medium` - `expanded` ++ +
+ + -#### **4. Diffusion Model** +### **4. Diffusion Model** The opensrdiffusion model is only available in the `large` size. This model is suited for deep resolution enhancement without additional configurations. ```python @@ -220,30 +261,35 @@ models = supers2.setmodel( ) ``` -#### **5. Simple Models (Bilinear and Bicubic)** +### **5. Simple Models (Bilinear and Bicubic)** For fast interpolation, bilinear and bicubic interpolation models can be used. These models do not require complex configurations and are useful for quick evaluations of enhanced resolution. + ```python from supers2.models.simple import BilinearSR, BicubicSR # Bilinear Interpolation Model bilinear_model = BilinearSR(device=device, scale_factor=4).to(device) -super_bilinear = bilinear_model(X[None]) +super_bilinear = bilinear_model(X[None]).squeeze(0) # Bicubic Interpolation Model bicubic_model = BicubicSR(device=device, scale_factor=4).to(device) -super_bicubic = bicubic_model(X[None]) +super_bicubic = bicubic_model(X[None]).squeeze(0) ``` -### **Apply spatial resolution enhancement** ++ +
+ +## **Apply spatial resolution enhancement** -### **Predict only RGBNIR bands** 🌍 +## **Predict only RGBNIR bands** 🌍 ```python superX = supers2.predict_rgbnir(X[[2, 1, 0, 6]]) ``` -### **Estimate the uncertainty of the model** 📊 +## **Estimate the uncertainty of the model** 📊 ```python from supers2.trained_models import SRmodels @@ -267,11 +313,11 @@ ax[1].imshow(std_map[0:3].cpu().numpy().transpose(1, 2, 0)*100) ax[1].set_title("Standard Deviation") plt.show() ``` -- +
+ ### Estimate the Local Attention Map of the model 📊 @@ -296,4 +342,4 @@ plt.show() ``` -### Use the opensr-test and supers2 to analyze the hallucination pixels 📊 +## Use the opensr-test and supers2 to analyze the hallucination pixels 📊 diff --git a/assets/images/bibi.png b/assets/images/bibi.png new file mode 100644 index 0000000000000000000000000000000000000000..650d3d773f3ae806118b1e144ad6f3746fa6b134 GIT binary patch literal 433743 zcmd42WmHvd_ddE2DUmRc1_9|5kPelS1|U56~pkc+p?;iu~qk@RkTFXtnvv zvERaz5JciZ7FsfwFv<|L_m-HmfVEaorYB$i$=nACH9Y4Z^o!W@i7elv=}Oa1AeCQ2 zTp!jW>h{6!B*?9xrmCIM#2qwA*JdCy-h#|KYYsHAqWI);v3h>BE*K2K7uQ;h4i)j^ zapbAhb^?d{S;}kMm_r25m6V*`EC+EtFae5p;v$GE7L$7gdj=qV8;F$Vogy&rB-S5f zCz=DCFjs{UI7;aUkWAK3w0;Kp8I#wv%a~?6TkI%Mw`sUqWnTWt>`V)FFCvRBvZ~9? zTyqF-6$>43fE(zzI4sFr-*F+^+yLM8cRrgPFJP|QZ`r&6jfkJOaYNdaCsyneS!5U% z#nhl`OpCnHG$IzCnSClRs?RSRFN6EKN({C5OIO|fS zZ!RV}1-!;VVUE_~elkOd!H4>Tv56&NxUI%vSo8#M^3@9U8)3+{I(U)`GD*;QC{dKS 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