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aedes-egg-weights
vicpantoja2/aedes-egg-weights
aedes-egg-weights is a machine learning model from vicpantoja2. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
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Updated Jun 17, 2026
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At a glance
- License
- mit
- Access
- Public
- Created
- Jan 16, 2026
- Updated
- Jun 17, 2026
- SHA
- 3a38b047
Datasets
Model card
From the Hugging Face model README
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.
- License
- mit
- Languages
- en
- Created
- Jan 16, 2026
- Updated
- Jun 17, 2026