3-Tier Client-Server Architecture with Model-Serving Inference Pipeline: Separates concerns into Presentation (React frontend), Application (Flask backend API), and Data/Model (TensorFlow GAN).
The process of an image-to-image translation GAN, detailing each stage of the architecture, loss functions, and training steps used in tasks like sketch-to-color generation. https://huggingface.co/spaces/madhi9/jewelry/tree/main
Pix2Pix (Conditional GAN)
Why: It is specifically designed for image-to-image translation tasks like turning sketches into realistic images.
How it works:
Uses a U-Net as the generator (preserves spatial information).
Uses a PatchGAN as the discriminator (focuses on high-frequency details).
Trained on: Pairs of images (Sketch ↔ Realistic Jewelry Image).
- Data Preparation
- Architecture Design (Generator and Discriminator)
- Forward Pass Through the GAN
- Loss Calculation and Backpropagation
- Training Loop
- Image Generation and Evaluation
The first step involves preparing the dataset for the GAN model. For sketch-to-color image translation:
- Input Data: You’ll have pairs of images, where each pair consists of a sketch image and its corresponding colored version.
- Image Preprocessing: Resize images to a fixed dimension (e.g., 256x256 or 512x512). Normalize pixel values to a range (e.g., -1 to 1 for the generator).
- Data Augmentation (Optional): Apply transformations like flipping or rotation to diversify the training data, helping the model generalize.
The core of the GAN architecture consists of two networks:
The U-Net generator architecture is commonly used in image translation tasks. It has an encoder-decoder structure with skip connections:
- Encoder (Downsampling): The encoder progressively reduces spatial dimensions, capturing high-level features. It applies multiple convolutional layers, each followed by a downsampling layer (e.g., max pooling or stride convolutions).
- Bottleneck: The smallest layer in terms of spatial size, where the highest-level features are stored.
- Decoder (Upsampling): The decoder restores spatial resolution back to the original image size by using transpose convolutions (upsampling layers). It enables the generator to produce an output image of the same size as the input.
- Skip Connections: The skip connections between encoder and decoder layers allow low-level details (edges, textures) from the input image to pass directly to corresponding layers in the decoder, improving fine detail preservation.
The PatchGAN discriminator architecture divides the image into patches and evaluates each patch independently:
- Convolutions: The discriminator applies convolutional layers, which reduce the spatial size of patches while capturing features within each patch.
- Output Layer: Instead of a single real/fake classification for the whole image, PatchGAN outputs a matrix of probabilities, each indicating if a specific patch is real or generated.
- Advantages: PatchGAN focuses on local features, which is crucial for high-quality detail (textures, edges) and enables the model to process larger images without scaling the discriminator.
The forward pass involves:
-
Generating an Image with the Generator:
- The generator receives a sketch image as input and outputs a colorized version. During this forward pass, the generator learns to adjust pixel values to create a realistic colored image from the sketch.
-
Evaluating Real and Generated Images with the Discriminator:
- The discriminator receives two inputs: real colored images (ground truth) and the generated images from the generator.
- It produces two outputs for each patch in both images: a probability score close to 1 for real images and close to 0 for generated (fake) images.
In the training process, the GAN computes two main types of losses: one for the generator and one for the discriminator.
- Real Loss: The discriminator should predict a value close to 1 for real images. Loss is computed based on the difference between predicted and actual (1) values for real images.
- Fake Loss: For generated images, the discriminator should predict values close to 0. Loss is computed based on the difference between predicted and actual (0) values for generated images.
The discriminator's total loss combines real and fake losses, encouraging it to accurately classify both real and generated images.
- Adversarial Loss: The generator’s goal is to “fool” the discriminator. The generator’s adversarial loss is computed based on the discriminator’s output for generated images, aiming to make these outputs as close to 1 as possible (real).
- Reconstruction Loss (L1 or MAE Loss): This measures pixel-wise differences between the generated image and the ground truth colored image. Minimizing this loss ensures that the generated image resembles the target image in structure, color, and texture.
The generator’s total loss is a weighted combination of adversarial and reconstruction losses. The adversarial component makes the image look realistic, while reconstruction loss maintains fidelity to the input sketch.
The training loop alternates between training the discriminator and generator:
-
Train the Discriminator:
- Update weights to maximize real and fake classification accuracy. This ensures the discriminator’s ability to distinguish real from generated images.
-
Train the Generator:
- Update weights to minimize adversarial loss (fooling the discriminator) and reconstruction loss (matching the target image). This teaches the generator to create realistic and structurally accurate colorizations.
-
Alternate:
- Training alternates between the two models. One typical method is to train the discriminator on a batch of real and fake images and then train the generator on another batch.
- Iterating through this loop stabilizes the GAN by balancing the abilities of both networks.
After sufficient training epochs, the GAN can generate realistic, high-quality colorized images from sketches. Evaluate the generated images to ensure quality and consistency:
- Qualitative Evaluation: Visually inspect generated images for quality, accuracy, and consistency with ground truth images.
- Quantitative Metrics (Optional): Metrics like Inception Score (IS), Frechet Inception Distance (FID), or L1 loss can provide quantitative measures of generation quality.
For tasks like sketch-to-color translation, visual quality is paramount since fine details and realistic colors are essential for a good output.
- Data Preparation: Organize and preprocess paired data for input-output mapping.
- Architecture Design: U-Net for detailed generation and PatchGAN for local realism.
- Forward Pass: Generate images and classify them as real/fake for adversarial learning.
- Loss Calculation: Use adversarial loss (to fool discriminator) and L1 loss (to match the target image).
- Training Loop: Alternate between training the discriminator and generator, iterating for stable results.
- Image Generation and Evaluation: Generate and evaluate output images, improving based on metrics or visual inspection.
This structured approach allows GANs to progressively improve their ability to generate realistic, detailed images from simpler sketches, eventually achieving high-quality results for image-to-image translation tasks.
It has classification wheather input sketch img is jewelry or not and classification of types of jewelry like earring,bracelet,ring,necklace.
Model: "model"
input_1 (InputLayer) [(None, 1024, 1024, 3)] 0 []
sequential (Sequential) (None, 512, 512, 64) 3072 ['input_1[0][0]']
sequential_1 (Sequential) (None, 256, 256, 128) 131584 ['sequential[0][0]']
sequential_2 (Sequential) (None, 128, 128, 256) 525312 ['sequential_1[0][0]']
sequential_3 (Sequential) (None, 64, 64, 512) 2099200 ['sequential_2[0][0]']
sequential_4 (Sequential) (None, 32, 32, 512) 4196352 ['sequential_3[0][0]']
sequential_5 (Sequential) (None, 16, 16, 512) 4196352 ['sequential_4[0][0]']
sequential_6 (Sequential) (None, 8, 8, 512) 4196352 ['sequential_5[0][0]']
sequential_7 (Sequential) (None, 4, 4, 512) 4196352 ['sequential_6[0][0]']
sequential_8 (Sequential) (None, 2, 2, 512) 4196352 ['sequential_7[0][0]']
sequential_9 (Sequential) (None, 4, 4, 512) 4196352 ['sequential_8[0][0]']
concatenate (Concatenate) (None, 4, 4, 1024) 0 ['sequential_9[0][0]',
'sequential_7[0][0]']
sequential_10 (Sequential) (None, 8, 8, 512) 8390656 ['concatenate[0][0]']
concatenate_1 (Concatenate (None, 8, 8, 1024) 0 ['sequential_10[0][0]',
) 'sequential_6[0][0]']
sequential_11 (Sequential) (None, 16, 16, 512) 8390656 ['concatenate_1[0][0]']
concatenate_2 (Concatenate (None, 16, 16, 1024) 0 ['sequential_11[0][0]',
) 'sequential_5[0][0]']
sequential_12 (Sequential) (None, 32, 32, 512) 8390656 ['concatenate_2[0][0]']
concatenate_3 (Concatenate (None, 32, 32, 1024) 0 ['sequential_12[0][0]',
) 'sequential_4[0][0]']
sequential_13 (Sequential) (None, 64, 64, 256) 4195328 ['concatenate_3[0][0]']
concatenate_4 (Concatenate (None, 64, 64, 768) 0 ['sequential_13[0][0]',
) 'sequential_3[0][0]']
sequential_14 (Sequential) (None, 128, 128, 128) 1573376 ['concatenate_4[0][0]']
concatenate_5 (Concatenate (None, 128, 128, 384) 0 ['sequential_14[0][0]',
) 'sequential_2[0][0]']
sequential_15 (Sequential) (None, 256, 256, 64) 393472 ['concatenate_5[0][0]']
concatenate_6 (Concatenate (None, 256, 256, 192) 0 ['sequential_15[0][0]',
) 'sequential_1[0][0]']
conv2d_transpose_7 (Conv2D (None, 512, 512, 3) 9219 ['concatenate_6[0][0]']
Transpose)
================================================================================================== Total params: 59280643 (226.14 MB) Trainable params: 59268739 (226.09 MB) Non-trainable params: 11904 (46.50 KB)