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| ### Model Description |
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| This repository contains the trained checkpoints (.pt) and generated samples (.png) for two generative models trained on a subset of the STL-10 dataset. |
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| - Pixel DDPM: Standard diffusion on 96x96 RGB images. |
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| - Latent DDPM (LDM): Diffusion on a compressed 12x12x4 latent space (using a VAE). |
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| Note: This repository only contains the weights. To load and use these models, |
| you must have the original model definitions (DDPM.py and VAE.py) in your local Python environment. |
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| - **Developed by:** Linoy Geva & Ron Chernoguz |
| - **Model type:** DDPM/LPM |
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| ## Uses |
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| This model is designed to generate synthetic data that mimics the characteristics of the STL-10 dataset. |
| It is intended for use in experimental research comparing the effectiveness of diffusion-based data augmentation |
| against traditional/manual augmentation techniques for CNN classification. |
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| ### Recommendations |
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| <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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| ## Training Details |
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| ### Training Data |
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| <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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| [More Information Needed] |
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| ### Training Procedure |
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| <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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| #### Training Hyperparameters |
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| =================== |
| PIXEL DDPM (Baseline Model) ================== |
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| • Optimization Strategy: |
| - Optimizer: AdamW |
| - Learning Rate: 2e-4 (with Cosine Annealing) |
| - Weight Decay: 1e-3 |
| - Batch Size: 32 |
| - Total Epochs: 225 |
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| • Model Architecture: |
| - Input: 96x96 RGB Images |
| - Base Channels: 64 |
| - Channel Mults: (1, 2, 4, 8) |
| - EMA: Disabled |
| - Diffusion: 1000 Timesteps (Linear Schedule) |
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| =================== |
| LATENT DDPM (LDM) ============================ |
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| • Optimization Strategy: |
| - Optimizer: AdamW |
| - Learning Rate: 1e-4 (with Cosine Annealing) |
| - Weight Decay: 1e-3 |
| - Batch Size: 64 |
| - Total Epochs: 2500 (Target) |
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| • Model Architecture: |
| - Input: 12x12x4 Latents (via VAE) |
| - Base Channels: 128 |
| - Channel Mults: (2, 2) |
| - EMA: Enabled (Decay: 0.99) |
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| =========================================================== |
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| #### Testing Data |
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| <!-- This should link to a Dataset Card if possible. --> |
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| [More Information Needed] |
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| ### Results |
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| [More Information Needed] |
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| #### Summary |
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| ### Compute Infrastructure |
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| [More Information Needed] |
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| #### Hardware |
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| [More Information Needed] |
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