| --- |
| license: mit |
| datasets: |
| - vicpantoja2/aedes-egg-images |
| language: |
| - en |
| --- |
| # Experiments |
|
|
| ## Real-ESRGAN |
| | **Model** | **Total Iters** | **Best Iteration** | **Total Duration** | **Status / Result** | |
| | --- | --- | --- | --- | --- | |
| | **Real-ESRNet x4** | 1,000,000 | **824,800** | 5d 20h 21m | ✅ Stable (End of learning) | |
| | **Real-ESRGAN x4** | 400,000 | **260,900** | ~3.5 days | 🔄 GAN Dynamics (Stable) | |
| | **SR3 (Rounds 1-4)** | 1,185,000 | **1,085,000** | ~3d 19h | 🔴 Saturation / Overfitting | |
| | **SR3 (Round 5)** | 1,200,000 | **925,000** | ~2d | 🏆 Best PSNR (17.81 dB) | |
|
|
| | **Model / Phase** | **Average Time per Epoch** | **Time per Iteration** | |
| | --- | --- | --- | |
| | **Real-ESRNet x4** | **~13.7 min** | 0.500s | |
| | **Real-ESRGAN x4** | **~23 min** | 0.816s (avg) | |
| | **SR3 (Rounds 1-4)** | **Variable** | ~0.86s | |
| | **SR3 (Round 5)** | **-** | ~0.46s | |
|
|
| --- |
|
|
| ### **1. Real-ESRNet x4** |
| - **Learning**: Exemplary behavior. The minimum loss occurred late (824k), indicating that the model continued refining details for a long time before saturating. |
| - **Verdict**: Checkpoint 800k+ is the best for mathematical fidelity (PSNR/SSIM). |
|
|
| --- |
|
|
| ### **2. Real-ESRGAN x4** |
| - **Convergence**: Lowest pixel loss at 260k. After this point, pixel loss increased while Perceptual/GAN losses fluctuated, focusing on textures. |
| - **Verdict**: After 260k, the Discriminator prioritizes visual realism over pure metrics (L1). |
|
|
| --- |
|
|
| ## SR3 (Super-Resolution via Iterative Refinement)** |
|
|
|
|
| #### **Training Rounds Summary** |
|
|
| | Round | Interval (Iterations) | Epochs | Total Duration | |
| | :--- | :--- | :--- | :--- | |
| | **Round 1** | 0 — 1,185,000 | 0 — 968 | 91h 07m 22s | |
| | **Round 2** | 0 — 1,200,000 | 0 — 83 | 47h 57m 16s | |
|
|
| > [!NOTE] |
| > Training 1 was performed with **411 images** until iteration **1,085,000**. It then proceeded with **4,932 images** for **23h 49m 14s**. |
| > Training 2 was performed with **4,932 images images** |
|
|
| **Maximum Performance per Round:** |
| - **Best Checkpoint (Round 1)**: **17.42 dB** (Iter 1,085,000, Epoch 916). |
| - **Best Checkpoint (Round 2)**: **17.81 dB** (Iter 925,000, Epoch 57). |
|
|
| **📌 Training Configuration** |
| - **Data**: LR/SR image pairs at 64x64 and 128x128 resolutions. |
|
|
| --- |
|
|
| #### **Rounds Detailing** |
|
|
| ##### **Round 1: Consolidated Training (Incremental)** |
| This round represents the initial development, including dataset transition and hardware optimizations. |
| - **Total Duration**: 91h 07m 22s. |
| - **Metrics**: |
| - **Peak (Best PSNR)**: **17.42 dB**. |
| - **Stability**: Showed high initial variability (±2.14 dB), stabilizing after the introduction of *Gradient Accumulation*. |
| - **Technical Observations**: |
| - **Hardware**: Use of **Gradient Accumulation** (Effective Batch 2) to overcome the 9.7 GB VRAM limit (NVIDIA A100). |
| - **Overfitting**: Identified after iteration 1,085,000, where performance became unstable. |
| - **Augmentation (End of Round)**: Expansion to 4,932 images in this phase resulted in performance drop (-0.74 dB), indicating that the model "specialized" in the smaller dataset rejected highly transformed data. |
|
|
| ##### **Round 2: Full Dataset Training** |
| Training restarted with the dataset of 4,932 images from the base checkpoint. |
| - **Total Duration**: 47h 57m 16s. |
| - **🏆 Maximum Result**: **17.81 dB** (Reached at 925,000 iterations). |
| - **Advantage**: Converged 14.7% faster than incremental phases. |
| - **Risk**: Abrupt collapse behavior after reaching the peak, requiring manual and early selection of checkpoints (*early stopping*). |
|
|
| **SR3 Verdict**: Training initiated with the full dataset was superior in absolute metrics, although Round 1 provided fundamental knowledge about memory limitations and the model's sensitivity to augmentation. |
