Update README.md

README.md

@@ -6,47 +6,110 @@ language:
 library_name: fastai
 pipeline_tag: image-to-image
 ---
-Model Card: GAN Colorization Model
-Model Description
+# Model Description
 This GAN-based model performs image colorization, transforming grayscale images into color images. It leverages a generator network to predict the color channels and a discriminator network to improve the colorization quality through adversarial training.
 
-Model Details
+# Model Details
 Model Name: GAN Colorization Model
 Model Architecture: The model uses a ResNet-34 backbone as the encoder in the generator network and a PatchGAN discriminator network.
 Framework: PyTorch
 Repository: Hammad712/GAN-Colorization-Model
-Model Training
-Dataset
+# Model Training
+## Dataset
 Dataset Used: COCO 2017
 Training Size: 8000 images
 Validation Size: 2000 images
 Image Size: 256x256 pixels
-Training Configuration
+## Training Configuration
 Batch Size: 16
 Number of Epochs: 5
 Optimizer for Generator: Adam (learning rate: 0.0004, betas: 0.5, 0.999)
 Optimizer for Discriminator: Adam (learning rate: 0.0004, betas: 0.5, 0.999)
-Loss Functions:
+## Loss Functions:
 GAN Loss: Binary Cross-Entropy Loss with Logits
 L1 Loss: L1 Loss for pixel-wise comparison between generated and real color channels
 
-Usage
+# Inference Code
+
+from huggingface_hub import hf_hub_download
+import torch
+from PIL import Image
+from torchvision import transforms
+from skimage.color import rgb2lab, lab2rgb
+import numpy as np
+import matplotlib.pyplot as plt
+
+#Download the model from Hugging Face Hub
+repo_id = "Hammad712/GAN-Colorization-Model"
+model_filename = "generator.pt"
+model_path = hf_hub_download(repo_id=repo_id, filename=model_filename)
+
+#Define the generator model (same architecture as used during training)
+from fastai.vision.learner import create_body
+from torchvision.models import resnet34
+from fastai.vision.models.unet import DynamicUnet
+
+def build_generator(n_input=1, n_output=2, size=256):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    backbone = create_body(resnet34(), pretrained=True, n_in=n_input, cut=-2)
+    G_net = DynamicUnet(backbone, n_output, (size, size)).to(device)
+    return G_net
+
+#Initialize and load the model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+G_net = build_generator(n_input=1, n_output=2, size=256)
+G_net.load_state_dict(torch.load(model_path, map_location=device))
+G_net.eval()
+
+#Preprocessing function
+def preprocess_image(img_path):
+    img = Image.open(img_path).convert("RGB")
+    img = transforms.Resize((256, 256), Image.BICUBIC)(img)
+    img = np.array(img)
+    img_to_lab = rgb2lab(img).astype("float32")
+    img_to_lab = transforms.ToTensor()(img_to_lab)
+    L = img_to_lab[[0], ...] / 50. - 1.
+    return L.unsqueeze(0).to(device)
+
+#Inference function
+def colorize_image(img_path, model):
+    L = preprocess_image(img_path)
+    with torch.no_grad():
+        ab = model(L)
+    L = (L + 1.) * 50.
+    ab = ab * 110.
+    Lab = torch.cat([L, ab], dim=1).permute(0, 2, 3, 1).cpu().numpy()
+    rgb_imgs = []
+    for img in Lab:
+        img_rgb = lab2rgb(img)
+        rgb_imgs.append(img_rgb)
+    return np.stack(rgb_imgs, axis=0)
+
+#Example image path
+img_path = "/path/to/your/image.jpg"  # Replace with your image path
+
+#Perform inference
+colorized_images = colorize_image(img_path, G_net)
+
+#Display the result
+plt.imshow(colorized_images[0])
+plt.axis("off")
+plt.show()
+
+# Usage
 To use the model for image colorization, ensure that the dependencies are installed and run the inference code provided. You will need to replace the image path with your own image for colorization.
 
-Model Performance
+# Model Performance
 Qualitative Results
 The model generates visually plausible colorizations for grayscale images. Here are some examples of colorized outputs:
 
-
-
-Limitations
+# Limitations
 The model may not always produce accurate colors for objects with complex or unusual color distributions.
 Performance may degrade for images that significantly differ from the training dataset.
 Citation
 If you use this model in your research, please cite the original repository:
 
-bibtex
-Copy code
+# bibtex
 @misc{Hammad7122023GANColorization,
   title={GAN-Colorization-Model},
   author={Hammad712},
@@ -54,5 +117,5 @@ Copy code
   publisher={Hugging Face},
   howpublished={\url{https://huggingface.co/Hammad712/GAN-Colorization-Model}},
 }
-Contact
+# Contact
 For any issues or inquiries, please reach out to the model author through the Hugging Face repository.