---
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.