Upload folder using huggingface_hub
Browse files- .gitattributes +12 -0
- .gitignore +22 -0
- LICENSE +21 -0
- README.md +369 -3
- assets/architecture.png +3 -0
- assets/data_1.png +3 -0
- assets/data_2.png +3 -0
- assets/data_3.png +3 -0
- assets/data_4.png +3 -0
- assets/data_5.png +3 -0
- assets/data_6.png +3 -0
- assets/finetune_detail.png +3 -0
- assets/teasser.png +3 -0
- assets/training_metrics.png +3 -0
- assets/val_sample_early.jpg +3 -0
- assets/val_sample_final.jpg +3 -0
- config.yaml +93 -0
- finetuned/courtkeynet_finetuned.safetensors +3 -0
- finetuned/training_log.txt +785 -0
- pretrained/courtkeynet_base.safetensors +3 -0
- pretrained/training_log.txt +1026 -0
.gitattributes
CHANGED
|
@@ -33,3 +33,15 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 36 |
+
assets/architecture.png filter=lfs diff=lfs merge=lfs -text
|
| 37 |
+
assets/data_1.png filter=lfs diff=lfs merge=lfs -text
|
| 38 |
+
assets/data_2.png filter=lfs diff=lfs merge=lfs -text
|
| 39 |
+
assets/data_3.png filter=lfs diff=lfs merge=lfs -text
|
| 40 |
+
assets/data_4.png filter=lfs diff=lfs merge=lfs -text
|
| 41 |
+
assets/data_5.png filter=lfs diff=lfs merge=lfs -text
|
| 42 |
+
assets/data_6.png filter=lfs diff=lfs merge=lfs -text
|
| 43 |
+
assets/finetune_detail.png filter=lfs diff=lfs merge=lfs -text
|
| 44 |
+
assets/teasser.png filter=lfs diff=lfs merge=lfs -text
|
| 45 |
+
assets/training_metrics.png filter=lfs diff=lfs merge=lfs -text
|
| 46 |
+
assets/val_sample_early.jpg filter=lfs diff=lfs merge=lfs -text
|
| 47 |
+
assets/val_sample_final.jpg filter=lfs diff=lfs merge=lfs -text
|
.gitignore
ADDED
|
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Python
|
| 2 |
+
__pycache__/
|
| 3 |
+
*.py[cod]
|
| 4 |
+
*.egg-info/
|
| 5 |
+
*.egg
|
| 6 |
+
|
| 7 |
+
# IDE
|
| 8 |
+
.vscode/
|
| 9 |
+
.idea/
|
| 10 |
+
|
| 11 |
+
# OS
|
| 12 |
+
.DS_Store
|
| 13 |
+
Thumbs.db
|
| 14 |
+
|
| 15 |
+
# Temporary files
|
| 16 |
+
*.tmp
|
| 17 |
+
*.bak
|
| 18 |
+
|
| 19 |
+
# Training artifacts (not needed on HF)
|
| 20 |
+
wandb/
|
| 21 |
+
runs/
|
| 22 |
+
*.log
|
LICENSE
ADDED
|
@@ -0,0 +1,21 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
MIT License
|
| 2 |
+
|
| 3 |
+
Copyright (c) 2025 Adithya N Raj
|
| 4 |
+
|
| 5 |
+
Permission is hereby granted, free of charge, to any person obtaining a copy
|
| 6 |
+
of this software and associated documentation files (the "Software"), to deal
|
| 7 |
+
in the Software without restriction, including without limitation the rights
|
| 8 |
+
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
| 9 |
+
copies of the Software, and to permit persons to whom the Software is
|
| 10 |
+
furnished to do so, subject to the following conditions:
|
| 11 |
+
|
| 12 |
+
The above copyright notice and this permission notice shall be included in all
|
| 13 |
+
copies or substantial portions of the Software.
|
| 14 |
+
|
| 15 |
+
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
| 16 |
+
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
| 17 |
+
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
| 18 |
+
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
| 19 |
+
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
| 20 |
+
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
| 21 |
+
SOFTWARE.
|
README.md
CHANGED
|
@@ -1,3 +1,369 @@
|
|
| 1 |
-
---
|
| 2 |
-
|
| 3 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language: en
|
| 3 |
+
license: mit
|
| 4 |
+
tags:
|
| 5 |
+
- court-detection
|
| 6 |
+
- keypoint-detection
|
| 7 |
+
- sports-analysis
|
| 8 |
+
- badminton
|
| 9 |
+
- tennis
|
| 10 |
+
- pytorch
|
| 11 |
+
- computer-vision
|
| 12 |
+
- octave-convolution
|
| 13 |
+
- geometric-constraints
|
| 14 |
+
- polar-transform
|
| 15 |
+
datasets:
|
| 16 |
+
- adithyanraj03/CourtKeyNet-Dataset
|
| 17 |
+
metrics:
|
| 18 |
+
- pck
|
| 19 |
+
- iou
|
| 20 |
+
pipeline_tag: keypoint-detection
|
| 21 |
+
library_name: pytorch
|
| 22 |
+
model-index:
|
| 23 |
+
- name: CourtKeyNet
|
| 24 |
+
results:
|
| 25 |
+
- task:
|
| 26 |
+
type: keypoint-detection
|
| 27 |
+
name: Court Keypoint Detection
|
| 28 |
+
dataset:
|
| 29 |
+
name: CourtKeyNet Badminton Court Dataset
|
| 30 |
+
type: custom
|
| 31 |
+
metrics:
|
| 32 |
+
- name: PCK
|
| 33 |
+
type: pck
|
| 34 |
+
value: 99.99
|
| 35 |
+
- name: IoU
|
| 36 |
+
type: iou
|
| 37 |
+
value: 99.67
|
| 38 |
+
---
|
| 39 |
+
|
| 40 |
+
<div align="center">
|
| 41 |
+
<img src="assets/teasser.png" alt="CourtKeyNet Banner" width="100%">
|
| 42 |
+
|
| 43 |
+
# CourtKeyNet: A Novel Octave-Based Architecture for Precision Court Detection
|
| 44 |
+
|
| 45 |
+
**Adithya N Raj**
|
| 46 |
+
|
| 47 |
+
[](https://www.sciencedirect.com/science/article/pii/S2666827026000496)
|
| 48 |
+
[](https://github.com/adithyanraj03/CourtKeyNet)
|
| 49 |
+
[](https://github.com/adithyanraj03/Paper_09_Data-Set_CourtKeyNet)
|
| 50 |
+
[](LICENSE)
|
| 51 |
+
[](https://huggingface.co/docs/safetensors)
|
| 52 |
+
|
| 53 |
+
</div>
|
| 54 |
+
|
| 55 |
+
---
|
| 56 |
+
|
| 57 |
+
## Model Summary
|
| 58 |
+
|
| 59 |
+
**CourtKeyNet** is a lightweight deep learning architecture for precision court keypoint detection in sports videos. It detects 4 corner keypoints of badminton and tennis courts from a single image, enabling downstream applications like court homography estimation, match statistics generation, and automated broadcasting systems.
|
| 60 |
+
|
| 61 |
+
Published in [Machine Learning with Applications (Elsevier)](https://www.sciencedirect.com/science/article/pii/S2666827026000496), 2026.
|
| 62 |
+
|
| 63 |
+
### Key Features
|
| 64 |
+
|
| 65 |
+
- π― **99.99% PCK** and **99.67% IoU** on the fine-tuned model
|
| 66 |
+
- β‘ **Lightweight and fast** β efficient architecture for real-time use
|
| 67 |
+
- π¬ **Octave Feature Extractor** β Multi-frequency feature decomposition for capturing fine court details and global structural context
|
| 68 |
+
- π± **Polar Transform Attention** β Boundary detection in polar coordinates for precise court line localization
|
| 69 |
+
- π **Geometric Consistency Loss** β Ensures structurally valid quadrilateral outputs
|
| 70 |
+
- πΈ Supports both **badminton** and **tennis** court detection
|
| 71 |
+
|
| 72 |
+
---
|
| 73 |
+
|
| 74 |
+
## Model Variants
|
| 75 |
+
|
| 76 |
+
| Variant | File | Format | Val Loss | PCK (%) | IoU (%) | Description |
|
| 77 |
+
|---------|------|--------|----------|---------|---------|-------------|
|
| 78 |
+
| **CourtKeyNet-Base** | `pretrained/courtkeynet_base.safetensors` | SafeTensors | 0.0977 | 99.09 | 94.33 | Pre-trained on 140k+ images from scratch |
|
| 79 |
+
| **CourtKeyNet-Finetuned** β | `finetuned/courtkeynet_finetuned.safetensors` | SafeTensors | 0.0013 | 99.99 | 99.67 | Fine-tuned on 7k+ clean annotated images |
|
| 80 |
+
|
| 81 |
+
> **Recommendation:** Use the **fine-tuned** variant for best results. The pre-trained base model is provided for researchers who wish to fine-tune on their own custom court datasets.
|
| 82 |
+
|
| 83 |
+
---
|
| 84 |
+
|
| 85 |
+
## Quick Start
|
| 86 |
+
|
| 87 |
+
### 1. Download Weights
|
| 88 |
+
|
| 89 |
+
```bash
|
| 90 |
+
# Install huggingface CLI
|
| 91 |
+
pip install "huggingface_hub[cli]"
|
| 92 |
+
|
| 93 |
+
# Download the full repository
|
| 94 |
+
huggingface-cli download Cracked-ANJ/CourtKeyNet --local-dir ./courtkeynet-weights
|
| 95 |
+
|
| 96 |
+
# Or download only the fine-tuned model (recommended)
|
| 97 |
+
huggingface-cli download Cracked-ANJ/CourtKeyNet finetuned/courtkeynet_finetuned.safetensors --local-dir ./courtkeynet-weights
|
| 98 |
+
```
|
| 99 |
+
|
| 100 |
+
### 2. Run Inference
|
| 101 |
+
|
| 102 |
+
For inference, training, and fine-tuning scripts, use the full source code from the **[GitHub Repository](https://github.com/adithyanraj03/CourtKeyNet)**:
|
| 103 |
+
|
| 104 |
+
```bash
|
| 105 |
+
# Clone the source code
|
| 106 |
+
git clone https://github.com/adithyanraj03/CourtKeyNet.git
|
| 107 |
+
cd CourtKeyNet
|
| 108 |
+
|
| 109 |
+
# Install dependencies
|
| 110 |
+
pip install -r requirements.txt
|
| 111 |
+
|
| 112 |
+
# Run Inference Studio (GUI)
|
| 113 |
+
cd courtkeynet
|
| 114 |
+
python inference.py
|
| 115 |
+
```
|
| 116 |
+
|
| 117 |
+
> The Inference Studio provides a complete GUI with image/video/webcam support, confidence scoring, and real-time visualization.
|
| 118 |
+
|
| 119 |
+
---
|
| 120 |
+
|
| 121 |
+
## Architecture
|
| 122 |
+
|
| 123 |
+
<div align="center">
|
| 124 |
+
<img src="assets/architecture.png" alt="CourtKeyNet Architecture" width="80%">
|
| 125 |
+
</div>
|
| 126 |
+
|
| 127 |
+
|
| 128 |
+
|
| 129 |
+
<details>
|
| 130 |
+
<summary style="font-size: 1.2em; font-weight: bold; cursor: pointer;">π How Confidence Detection Works (Visual Explanation)</summary>
|
| 131 |
+
|
| 132 |
+
### How Confidence Detection Works
|
| 133 |
+
|
| 134 |
+
The model (`CourtKeyNet`) works like this:
|
| 135 |
+
|
| 136 |
+

|
| 137 |
+
|
| 138 |
+
**Problem**: It has no "court detector" β it assumes every image IS a court!
|
| 139 |
+
|
| 140 |
+
---
|
| 141 |
+
|
| 142 |
+
#### What the Model Actually Outputs Internally
|
| 143 |
+
|
| 144 |
+
When you run `model(image)`, it returns a dictionary with these components:
|
| 145 |
+
|
| 146 |
+
```python
|
| 147 |
+
outputs = {
|
| 148 |
+
'heatmaps': Tensor[B, 4, 160, 160], # 4 gaussian peaks (one per corner)
|
| 149 |
+
'kpts_init': Tensor[B, 4, 2], # Initial keypoints from heatmaps
|
| 150 |
+
'kpts_refined': Tensor[B, 4, 2], # Final refined keypoints
|
| 151 |
+
'features': Tensor[B, 256, 20, 20] # Feature maps (optional)
|
| 152 |
+
}
|
| 153 |
+
```
|
| 154 |
+
|
| 155 |
+
#### Visualization of Heatmap Output
|
| 156 |
+
|
| 157 |
+
For a **real court image**:
|
| 158 |
+
```text
|
| 159 |
+
Heatmap for Corner 0 (Top-Left):
|
| 160 |
+
```
|
| 161 |
+

|
| 162 |
+
|
| 163 |
+
For a **non-court image** (e.g., random person):
|
| 164 |
+
```text
|
| 165 |
+
Heatmap for Corner 0:
|
| 166 |
+
```
|
| 167 |
+

|
| 168 |
+
|
| 169 |
+
---
|
| 170 |
+
|
| 171 |
+
#### 3 Confidence Metrics
|
| 172 |
+
|
| 173 |
+
##### 1οΈβ£ **Heatmap Peak Confidence** (Primary Signal)
|
| 174 |
+
|
| 175 |
+
**What it measures**: How "peaky" the heatmap is
|
| 176 |
+
|
| 177 |
+
```python
|
| 178 |
+
max_values = heatmaps.max(dim=(2,3)) # Find highest value in each heatmap
|
| 179 |
+
conf_heatmap = max_values.mean() # Average across 4 corners
|
| 180 |
+
```
|
| 181 |
+
|
| 182 |
+
**Visual comparison**:
|
| 183 |
+
|
| 184 |
+

|
| 185 |
+
|
| 186 |
+
---
|
| 187 |
+
|
| 188 |
+
##### 2οΈβ£ **Heatmap Entropy** (Uncertainty)
|
| 189 |
+
|
| 190 |
+
**What it measures**: How "spread out" the probability is
|
| 191 |
+
|
| 192 |
+
```python
|
| 193 |
+
# Entropy = -Ξ£(p * log(p))
|
| 194 |
+
# Low entropy = focused (good)
|
| 195 |
+
# High entropy = random noise (bad)
|
| 196 |
+
```
|
| 197 |
+
|
| 198 |
+
**Visual comparison**:
|
| 199 |
+
|
| 200 |
+

|
| 201 |
+
|
| 202 |
+
---
|
| 203 |
+
|
| 204 |
+
##### 3οΈβ£ **Geometric Validity** (Shape Check)
|
| 205 |
+
|
| 206 |
+
**What it checks**: Does the quad look like a real court?
|
| 207 |
+
|
| 208 |
+
```text
|
| 209 |
+
Checklist:
|
| 210 |
+
β Are corners in correct positions? (TL upper-left, BR lower-right)
|
| 211 |
+
β Is the quad convex? (no crossed lines)
|
| 212 |
+
β Is the area reasonable? (not too tiny, not entire image)
|
| 213 |
+
β Is aspect ratio court-like? (not a thin line)
|
| 214 |
+
```
|
| 215 |
+
|
| 216 |
+
**Visual examples**:
|
| 217 |
+
|
| 218 |
+

|
| 219 |
+
|
| 220 |
+
</details>
|
| 221 |
+
|
| 222 |
+
---
|
| 223 |
+
|
| 224 |
+
## Training Details
|
| 225 |
+
|
| 226 |
+
### Pre-training (from scratch)
|
| 227 |
+
|
| 228 |
+
| Parameter | Value |
|
| 229 |
+
|-----------|-------|
|
| 230 |
+
| **Dataset** | 140,000+ badminton & tennis court images |
|
| 231 |
+
| **Epochs** | 158 (early stopping, patience=20) |
|
| 232 |
+
| **Best Epoch** | 138 |
|
| 233 |
+
| **Optimizer** | AdamW |
|
| 234 |
+
| **Learning Rate** | 5e-5 (cosine schedule) |
|
| 235 |
+
| **Batch Size** | 47 |
|
| 236 |
+
| **Image Size** | 640Γ640 |
|
| 237 |
+
| **Mixed Precision** | β FP16 |
|
| 238 |
+
| **Hardware** | NVIDIA RTX 5090 (32GB) |
|
| 239 |
+
|
| 240 |
+
### Fine-tuning
|
| 241 |
+
|
| 242 |
+
| Parameter | Value |
|
| 243 |
+
|-----------|-------|
|
| 244 |
+
| **Base Model** | Pre-trained CourtKeyNet-Base (epoch 138) |
|
| 245 |
+
| **Dataset** | 7,000+ precisely annotated clean images |
|
| 246 |
+
| **Epochs** | 79 (early stopping, patience=20) |
|
| 247 |
+
| **Best Epoch** | 59 |
|
| 248 |
+
| **Learning Rate** | 1e-4 |
|
| 249 |
+
| **Batch Size** | 48 |
|
| 250 |
+
| **Geometric Loss** | Enabled (edge + diagonal + angle constraints) |
|
| 251 |
+
|
| 252 |
+
---
|
| 253 |
+
|
| 254 |
+
## Evaluation Results
|
| 255 |
+
|
| 256 |
+
### Fine-tuned Model (Recommended)
|
| 257 |
+
|
| 258 |
+
| Metric | Score |
|
| 259 |
+
|--------|-------|
|
| 260 |
+
| **PCK** (Percentage of Correct Keypoints) | **99.99%** |
|
| 261 |
+
| **IoU** (Intersection over Union) | **99.67%** |
|
| 262 |
+
| **Validation Loss** | **0.0013** |
|
| 263 |
+
|
| 264 |
+
### Pre-trained Base Model
|
| 265 |
+
|
| 266 |
+
| Metric | Score |
|
| 267 |
+
|--------|-------|
|
| 268 |
+
| **PCK** | 99.09% |
|
| 269 |
+
| **IoU** | 94.33% |
|
| 270 |
+
| **Validation Loss** | 0.0977 |
|
| 271 |
+
|
| 272 |
+
### Training Metrics
|
| 273 |
+
|
| 274 |
+
<div align="center">
|
| 275 |
+
<img src="assets/training_metrics.png" alt="Training Metrics" width="100%">
|
| 276 |
+
<p><em>Pre-training metrics across 158 epochs showing loss convergence and PCK/IoU progression</em></p>
|
| 277 |
+
</div>
|
| 278 |
+
|
| 279 |
+
<div align="center">
|
| 280 |
+
<img src="assets/finetune_detail.png" alt="Fine-tuning Detail" width="100%">
|
| 281 |
+
<p><em>Fine-tuning convergence: rapid loss decrease with PCK reaching 99.99% and IoU reaching 99.67%</em></p>
|
| 282 |
+
</div>
|
| 283 |
+
|
| 284 |
+
### Validation Samples
|
| 285 |
+
|
| 286 |
+
<div align="center">
|
| 287 |
+
<table>
|
| 288 |
+
<tr>
|
| 289 |
+
<td align="center"><strong>Epoch 1 (Early Fine-tuning)</strong></td>
|
| 290 |
+
<td align="center"><strong>Epoch 79 (Final)</strong></td>
|
| 291 |
+
</tr>
|
| 292 |
+
<tr>
|
| 293 |
+
<td><img src="assets/val_sample_early.jpg" alt="Early Validation" width="100%"></td>
|
| 294 |
+
<td><img src="assets/val_sample_final.jpg" alt="Final Validation" width="100%"></td>
|
| 295 |
+
</tr>
|
| 296 |
+
</table>
|
| 297 |
+
<p><em>Red = Predicted court boundary | Green = Ground truth | Yellow dots = Keypoints</em></p>
|
| 298 |
+
</div>
|
| 299 |
+
|
| 300 |
+
---
|
| 301 |
+
|
| 302 |
+
## Fine-tuning on Custom Data
|
| 303 |
+
|
| 304 |
+
To fine-tune CourtKeyNet on your own court dataset (any sport):
|
| 305 |
+
|
| 306 |
+
```bash
|
| 307 |
+
# 1. Clone the source code
|
| 308 |
+
git clone https://github.com/adithyanraj03/CourtKeyNet.git
|
| 309 |
+
cd CourtKeyNet
|
| 310 |
+
|
| 311 |
+
# 2. Download weights
|
| 312 |
+
huggingface-cli download Cracked-ANJ/CourtKeyNet pretrained/courtkeynet_base.pt --local-dir ./weights
|
| 313 |
+
|
| 314 |
+
# 3. Fine-tune
|
| 315 |
+
cd courtkeynet
|
| 316 |
+
python finetune.py
|
| 317 |
+
```
|
| 318 |
+
|
| 319 |
+
> **Note:** Fine-tuning requires only **5,000β7,000** clean annotated images. Training from scratch requires **140,000+** images.
|
| 320 |
+
|
| 321 |
+
---
|
| 322 |
+
|
| 323 |
+
## Intended Use
|
| 324 |
+
|
| 325 |
+
### β
Appropriate Uses
|
| 326 |
+
- Badminton and tennis court detection in sports videos
|
| 327 |
+
- Court homography estimation for tactical analysis
|
| 328 |
+
- Generating match statistics from broadcast footage
|
| 329 |
+
- Automated camera calibration for sports broadcasting
|
| 330 |
+
- Academic research in sports video analysis
|
| 331 |
+
|
| 332 |
+
### β οΈ Limitations
|
| 333 |
+
- Trained specifically on badminton and tennis courts; other court types may require fine-tuning
|
| 334 |
+
- Expects single-court images; multi-court scenes are not supported
|
| 335 |
+
- Performance may degrade on heavily occluded courts or extreme camera angles
|
| 336 |
+
- The model detects 4 outer corners only (not inner court lines)
|
| 337 |
+
|
| 338 |
+
---
|
| 339 |
+
|
| 340 |
+
## Citation
|
| 341 |
+
|
| 342 |
+
If you use CourtKeyNet in your research, please cite:
|
| 343 |
+
|
| 344 |
+
**Paper:** [CourtKeyNet: A novel octave-based architecture for precision badminton court detection with geometric constraints](https://www.sciencedirect.com/science/article/pii/S2666827026000496)
|
| 345 |
+
**DOI:** [10.1016/j.mlwa.2026.100884](https://doi.org/10.1016/j.mlwa.2026.100884)
|
| 346 |
+
|
| 347 |
+
```bibtex
|
| 348 |
+
@article{NRAJ2026100884,
|
| 349 |
+
title = {CourtKeyNet: A novel octave-based architecture for precision badminton court detection with geometric constraints},
|
| 350 |
+
journal = {Machine Learning with Applications},
|
| 351 |
+
volume = {24},
|
| 352 |
+
pages = {100884},
|
| 353 |
+
year = {2026},
|
| 354 |
+
issn = {2666-8270},
|
| 355 |
+
doi = {https://doi.org/10.1016/j.mlwa.2026.100884},
|
| 356 |
+
url = {https://www.sciencedirect.com/science/article/pii/S2666827026000496},
|
| 357 |
+
author = {Adithya N Raj and Prethija G.}
|
| 358 |
+
}
|
| 359 |
+
```
|
| 360 |
+
|
| 361 |
+
---
|
| 362 |
+
|
| 363 |
+
## License
|
| 364 |
+
|
| 365 |
+
This project is released under the [MIT License](LICENSE), suitable for both academic and commercial use.
|
| 366 |
+
|
| 367 |
+
## Contact
|
| 368 |
+
|
| 369 |
+
For questions or collaboration opportunities: **adithyanraj03@gmail.com**
|
assets/architecture.png
ADDED
|
Git LFS Details
|
assets/data_1.png
ADDED
|
Git LFS Details
|
assets/data_2.png
ADDED
|
Git LFS Details
|
assets/data_3.png
ADDED
|
Git LFS Details
|
assets/data_4.png
ADDED
|
Git LFS Details
|
assets/data_5.png
ADDED
|
Git LFS Details
|
assets/data_6.png
ADDED
|
Git LFS Details
|
assets/finetune_detail.png
ADDED
|
Git LFS Details
|
assets/teasser.png
ADDED
|
Git LFS Details
|
assets/training_metrics.png
ADDED
|
Git LFS Details
|
assets/val_sample_early.jpg
ADDED
|
Git LFS Details
|
assets/val_sample_final.jpg
ADDED
|
Git LFS Details
|
config.yaml
ADDED
|
@@ -0,0 +1,93 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Model Configuration (Manuscript Section 4)
|
| 2 |
+
model:
|
| 3 |
+
name: "CourtKeyNet"
|
| 4 |
+
|
| 5 |
+
# Octave Feature Extractor
|
| 6 |
+
ofe:
|
| 7 |
+
channels_per_band: 64 # C for high/mid/low paths
|
| 8 |
+
stem_channels: 64
|
| 9 |
+
|
| 10 |
+
# Polar Transform Attention
|
| 11 |
+
pta:
|
| 12 |
+
enabled: true
|
| 13 |
+
radial_bins: 64 # Manuscript says fine-grained polar grid
|
| 14 |
+
angular_bins: 128
|
| 15 |
+
|
| 16 |
+
# Keypoint Localization
|
| 17 |
+
heatmap_sigma: 8.0 # Increased from 2.0 to help convergence
|
| 18 |
+
num_keypoints: 4
|
| 19 |
+
feature_dim: 128
|
| 20 |
+
|
| 21 |
+
# Transformer Refinement
|
| 22 |
+
transformer:
|
| 23 |
+
num_layers: 2
|
| 24 |
+
num_heads: 4
|
| 25 |
+
dim_feedforward: 512
|
| 26 |
+
dropout: 0.1
|
| 27 |
+
|
| 28 |
+
# Quadrilateral Constraint Module
|
| 29 |
+
qcm:
|
| 30 |
+
hidden_dims: [64, 128]
|
| 31 |
+
output_dim: 128
|
| 32 |
+
|
| 33 |
+
# Training Configuration
|
| 34 |
+
train:
|
| 35 |
+
epochs: 300
|
| 36 |
+
batch_size: 47
|
| 37 |
+
num_workers: 4
|
| 38 |
+
imgsz: 640
|
| 39 |
+
early_stopping_patience: 20 # Stop if no improvement for N epochs
|
| 40 |
+
|
| 41 |
+
# Optimizer (Manuscript: AdamW)
|
| 42 |
+
optimizer: "adamw"
|
| 43 |
+
lr0: 0.00005 # Reduced further to prevent divergence with high loss weights
|
| 44 |
+
lrf: 0.01 # Final LR multiplier
|
| 45 |
+
weight_decay: 0.0005
|
| 46 |
+
momentum: 0.937
|
| 47 |
+
|
| 48 |
+
# LR Scheduler
|
| 49 |
+
warmup_epochs: 5
|
| 50 |
+
scheduler: "cosine"
|
| 51 |
+
|
| 52 |
+
# Loss Weights (Manuscript Eq. 47)
|
| 53 |
+
# Geometric losses disabled initially (set to 0) to let model learn keypoints first
|
| 54 |
+
loss_weights:
|
| 55 |
+
keypoint: 10.0 # Primary loss for coordinate accuracy
|
| 56 |
+
heatmap: 20.0 # Heatmap supervision
|
| 57 |
+
edge: 0.0 # Disabled initially - enable after model converges
|
| 58 |
+
diagonal: 0.0 # Disabled initially
|
| 59 |
+
angle: 0.0 # Disabled initially
|
| 60 |
+
|
| 61 |
+
# Geometric loss warmup (linearly scale geo losses over N epochs)
|
| 62 |
+
# Only relevant when edge/diagonal/angle > 0
|
| 63 |
+
# geo_warmup_epochs: 10 #enabled in finetune only
|
| 64 |
+
|
| 65 |
+
# Training Tricks
|
| 66 |
+
mixed_precision: true
|
| 67 |
+
grad_clip: 1.0
|
| 68 |
+
ema_decay: 0.9999
|
| 69 |
+
|
| 70 |
+
# Checkpointing
|
| 71 |
+
save_interval: 5
|
| 72 |
+
save_best: true
|
| 73 |
+
|
| 74 |
+
# Paths
|
| 75 |
+
project: "runs/courtkeynet"
|
| 76 |
+
name: "exp"
|
| 77 |
+
|
| 78 |
+
# Validation
|
| 79 |
+
val:
|
| 80 |
+
batch_size: 32
|
| 81 |
+
interval: 1 # Validate every N epochs
|
| 82 |
+
|
| 83 |
+
# Weights & Biases Configuration
|
| 84 |
+
wandb:
|
| 85 |
+
enabled: true
|
| 86 |
+
project: "CourtKeyNet"
|
| 87 |
+
entity: null # Set to your wandb username or team name, or leave null for default
|
| 88 |
+
name: CourtKeyNet # Run name (auto-generated if null)
|
| 89 |
+
tags: ["badminton", "court-detection", "keypoint"]
|
| 90 |
+
log_freq: 100 # Log every N batches
|
| 91 |
+
|
| 92 |
+
# Device
|
| 93 |
+
device: "cuda" # or "cpu"
|
finetuned/courtkeynet_finetuned.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bcee559a41a54198110120931f7e0aa1d56aa83ab7307e86c4819029346cef57
|
| 3 |
+
size 4977840
|
finetuned/training_log.txt
ADDED
|
@@ -0,0 +1,785 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
(gpu) PS C:\Research\CourtKeyNet> python .\finetune.py
|
| 2 |
+
============================================================
|
| 3 |
+
CourtKeyNet Fine-tuning Script
|
| 4 |
+
Clean Dataset: 7k+ perfectly annotated images
|
| 5 |
+
============================================================
|
| 6 |
+
|
| 7 |
+
Selected pretrained weights: C:/Research/CourtKeyNet/runs/courtkeynet/exp_20260121_223500_wijqjzd9/best.pt
|
| 8 |
+
Clean dataset: C:\Research\Datasets\Badminton_court\7k+Clean_Dataset
|
| 9 |
+
Using device: cuda
|
| 10 |
+
wandb: Currently logged in as: adithyanraj03 to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
|
| 11 |
+
wandb: Tracking run with wandb version 0.23.1
|
| 12 |
+
wandb: Run data is saved locally in C:\Research\CourtKeyNet\wandb\run-20260129_061557-0fnvysj4
|
| 13 |
+
wandb: Run `wandb offline` to turn off syncing.
|
| 14 |
+
wandb: Syncing run Finetune_01/29_06:15
|
| 15 |
+
wandb: View project at https://wandb.ai/adithyanraj03/courtkeynet
|
| 16 |
+
wandb: View run at https://wandb.ai/adithyanraj03/courtkeynet/runs/0fnvysj4
|
| 17 |
+
wandb: Detected [huggingface_hub.inference] in use.
|
| 18 |
+
wandb: Use W&B Weave for improved LLM call tracing. Install Weave with `pip install weave` then add `import weave` to the top of your script.
|
| 19 |
+
wandb: For more information, check out the docs at: https://weave-docs.wandb.ai/
|
| 20 |
+
Weights & Biases initialized: https://wandb.ai/adithyanraj03/courtkeynet/runs/0fnvysj4
|
| 21 |
+
[train] Found 58123 images
|
| 22 |
+
[valid] Found 16826 images
|
| 23 |
+
[finetune-train] Found 58123 images
|
| 24 |
+
[finetune-valid] Found 16826 images
|
| 25 |
+
|
| 26 |
+
Loading pretrained weights...
|
| 27 |
+
Loaded from epoch 138, best_val_loss=0.09765742771935054
|
| 28 |
+
Model parameters: 1,238,856
|
| 29 |
+
Fine-tuning LR: 0.0001
|
| 30 |
+
Checkpoints will be saved to: runs\courtkeynet_finetune\finetune_20260129_061601_0fnvysj4
|
| 31 |
+
|
| 32 |
+
============================================================
|
| 33 |
+
Starting Fine-tuning...
|
| 34 |
+
============================================================
|
| 35 |
+
|
| 36 |
+
Epoch 0: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [20:27<00:00, 1.01s/it, loss=0.0173]
|
| 37 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:00<00:00, 4.36it/s]
|
| 38 |
+
|
| 39 |
+
Epoch 1/100
|
| 40 |
+
Train Loss: 0.0586
|
| 41 |
+
Val Loss: 0.0181
|
| 42 |
+
Val PCK: 0.9943
|
| 43 |
+
Val IoU: 0.9833
|
| 44 |
+
β Saved best model (val_loss=0.0181)
|
| 45 |
+
Epoch 1: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:02<00:00, 2.01it/s, loss=0.00963]
|
| 46 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:00<00:00, 4.38it/s]
|
| 47 |
+
|
| 48 |
+
Epoch 2/100
|
| 49 |
+
Train Loss: 0.0132
|
| 50 |
+
Val Loss: 0.0098
|
| 51 |
+
Val PCK: 0.9976
|
| 52 |
+
Val IoU: 0.9901
|
| 53 |
+
β Saved best model (val_loss=0.0098)
|
| 54 |
+
Epoch 2: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:00<00:00, 2.01it/s, loss=0.0109]
|
| 55 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:00<00:00, 4.38it/s]
|
| 56 |
+
|
| 57 |
+
Epoch 3/100
|
| 58 |
+
Train Loss: 0.0106
|
| 59 |
+
Val Loss: 0.0117
|
| 60 |
+
Val PCK: 0.9964
|
| 61 |
+
Val IoU: 0.9896
|
| 62 |
+
No improvement for 1/20 epochs
|
| 63 |
+
Epoch 3: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:02<00:00, 2.01it/s, loss=0.00971]
|
| 64 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:00<00:00, 4.38it/s]
|
| 65 |
+
|
| 66 |
+
Epoch 4/100
|
| 67 |
+
Train Loss: 0.0087
|
| 68 |
+
Val Loss: 0.0076
|
| 69 |
+
Val PCK: 0.9983
|
| 70 |
+
Val IoU: 0.9924
|
| 71 |
+
β Saved best model (val_loss=0.0076)
|
| 72 |
+
Epoch 4: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:01<00:00, 2.01it/s, loss=0.00753]
|
| 73 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [01:59<00:00, 4.39it/s]
|
| 74 |
+
|
| 75 |
+
Epoch 5/100
|
| 76 |
+
Train Loss: 0.0084
|
| 77 |
+
Val Loss: 0.0066
|
| 78 |
+
Val PCK: 0.9983
|
| 79 |
+
Val IoU: 0.9932
|
| 80 |
+
β Saved best model (val_loss=0.0066)
|
| 81 |
+
Epoch 5: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [31:39<00:00, 1.57s/it, loss=0.00898]
|
| 82 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:15<00:00, 1.40it/s]
|
| 83 |
+
|
| 84 |
+
Epoch 6/100
|
| 85 |
+
Train Loss: 0.0069
|
| 86 |
+
Val Loss: 0.0060
|
| 87 |
+
Val PCK: 0.9982
|
| 88 |
+
Val IoU: 0.9933
|
| 89 |
+
β Saved best model (val_loss=0.0060)
|
| 90 |
+
Epoch 6: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [32:04<00:00, 1.59s/it, loss=0.00831]
|
| 91 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:21<00:00, 1.38it/s]
|
| 92 |
+
|
| 93 |
+
Epoch 7/100
|
| 94 |
+
Train Loss: 0.0070
|
| 95 |
+
Val Loss: 0.0057
|
| 96 |
+
Val PCK: 0.9986
|
| 97 |
+
Val IoU: 0.9937
|
| 98 |
+
β Saved best model (val_loss=0.0057)
|
| 99 |
+
Epoch 7: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [18:15<00:00, 1.10it/s, loss=0.00608]
|
| 100 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [01:59<00:00, 4.40it/s]
|
| 101 |
+
|
| 102 |
+
Epoch 8/100
|
| 103 |
+
Train Loss: 0.0065
|
| 104 |
+
Val Loss: 0.0064
|
| 105 |
+
Val PCK: 0.9980
|
| 106 |
+
Val IoU: 0.9936
|
| 107 |
+
No improvement for 1/20 epochs
|
| 108 |
+
Epoch 8: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [11:27<00:00, 1.76it/s, loss=0.00956]
|
| 109 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [01:59<00:00, 4.39it/s]
|
| 110 |
+
|
| 111 |
+
Epoch 9/100
|
| 112 |
+
Train Loss: 0.0069
|
| 113 |
+
Val Loss: 0.0069
|
| 114 |
+
Val PCK: 0.9985
|
| 115 |
+
Val IoU: 0.9930
|
| 116 |
+
No improvement for 2/20 epochs
|
| 117 |
+
Epoch 9: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:01<00:00, 2.01it/s, loss=0.00876]
|
| 118 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [01:59<00:00, 4.39it/s]
|
| 119 |
+
|
| 120 |
+
Epoch 10/100
|
| 121 |
+
Train Loss: 0.0062
|
| 122 |
+
Val Loss: 0.0076
|
| 123 |
+
Val PCK: 0.9982
|
| 124 |
+
Val IoU: 0.9932
|
| 125 |
+
No improvement for 3/20 epochs
|
| 126 |
+
Epoch 10: 100%|βββββββββββββββββββββββββοΏ½οΏ½ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:00<00:00, 2.02it/s, loss=0.00628]
|
| 127 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:00<00:00, 4.38it/s]
|
| 128 |
+
|
| 129 |
+
Epoch 11/100
|
| 130 |
+
Train Loss: 0.0059
|
| 131 |
+
Val Loss: 0.0051
|
| 132 |
+
Val PCK: 0.9989
|
| 133 |
+
Val IoU: 0.9946
|
| 134 |
+
β Saved best model (val_loss=0.0051)
|
| 135 |
+
Epoch 11: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:00<00:00, 2.01it/s, loss=0.0072]
|
| 136 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [01:59<00:00, 4.38it/s]
|
| 137 |
+
|
| 138 |
+
Epoch 12/100
|
| 139 |
+
Train Loss: 0.0064
|
| 140 |
+
Val Loss: 0.0048
|
| 141 |
+
Val PCK: 0.9988
|
| 142 |
+
Val IoU: 0.9950
|
| 143 |
+
β Saved best model (val_loss=0.0048)
|
| 144 |
+
Epoch 12: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:02<00:00, 2.01it/s, loss=0.00574]
|
| 145 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [01:59<00:00, 4.39it/s]
|
| 146 |
+
|
| 147 |
+
Epoch 13/100
|
| 148 |
+
Train Loss: 0.0057
|
| 149 |
+
Val Loss: 0.0053
|
| 150 |
+
Val PCK: 0.9989
|
| 151 |
+
Val IoU: 0.9946
|
| 152 |
+
No improvement for 1/20 epochs
|
| 153 |
+
Epoch 13: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:01<00:00, 2.01it/s, loss=0.00631]
|
| 154 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:00<00:00, 4.38it/s]
|
| 155 |
+
|
| 156 |
+
Epoch 14/100
|
| 157 |
+
Train Loss: 0.0059
|
| 158 |
+
Val Loss: 0.0043
|
| 159 |
+
Val PCK: 0.9990
|
| 160 |
+
Val IoU: 0.9951
|
| 161 |
+
β Saved best model (val_loss=0.0043)
|
| 162 |
+
Epoch 14: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:01<00:00, 2.01it/s, loss=0.00544]
|
| 163 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:00<00:00, 4.37it/s]
|
| 164 |
+
|
| 165 |
+
Epoch 15/100
|
| 166 |
+
Train Loss: 0.0057
|
| 167 |
+
Val Loss: 0.0069
|
| 168 |
+
Val PCK: 0.9980
|
| 169 |
+
Val IoU: 0.9939
|
| 170 |
+
No improvement for 1/20 epochs
|
| 171 |
+
Epoch 15: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:01<00:00, 2.01it/s, loss=0.00487]
|
| 172 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [01:59<00:00, 4.39it/s]
|
| 173 |
+
|
| 174 |
+
Epoch 16/100
|
| 175 |
+
Train Loss: 0.0061
|
| 176 |
+
Val Loss: 0.0047
|
| 177 |
+
Val PCK: 0.9990
|
| 178 |
+
Val IoU: 0.9949
|
| 179 |
+
No improvement for 2/20 epochs
|
| 180 |
+
Epoch 16: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [09:59<00:00, 2.02it/s, loss=0.00493]
|
| 181 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:00<00:00, 4.38it/s]
|
| 182 |
+
|
| 183 |
+
Epoch 17/100
|
| 184 |
+
Train Loss: 0.0058
|
| 185 |
+
Val Loss: 0.0035
|
| 186 |
+
Val PCK: 0.9989
|
| 187 |
+
Val IoU: 0.9953
|
| 188 |
+
β Saved best model (val_loss=0.0035)
|
| 189 |
+
Epoch 17: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:00<00:00, 2.02it/s, loss=0.00418]
|
| 190 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [01:59<00:00, 4.40it/s]
|
| 191 |
+
|
| 192 |
+
Epoch 18/100
|
| 193 |
+
Train Loss: 0.0051
|
| 194 |
+
Val Loss: 0.0032
|
| 195 |
+
Val PCK: 0.9991
|
| 196 |
+
Val IoU: 0.9954
|
| 197 |
+
β Saved best model (val_loss=0.0032)
|
| 198 |
+
Epoch 18: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:02<00:00, 2.01it/s, loss=0.00595]
|
| 199 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [01:59<00:00, 4.39it/s]
|
| 200 |
+
|
| 201 |
+
Epoch 19/100
|
| 202 |
+
Train Loss: 0.0048
|
| 203 |
+
Val Loss: 0.0040
|
| 204 |
+
Val PCK: 0.9994
|
| 205 |
+
Val IoU: 0.9954
|
| 206 |
+
No improvement for 1/20 epochs
|
| 207 |
+
Epoch 19: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:00<00:00, 2.01it/s, loss=0.00491]
|
| 208 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [01:59<00:00, 4.39it/s]
|
| 209 |
+
|
| 210 |
+
Epoch 20/100
|
| 211 |
+
Train Loss: 0.0046
|
| 212 |
+
Val Loss: 0.0030
|
| 213 |
+
Val PCK: 0.9994
|
| 214 |
+
Val IoU: 0.9955
|
| 215 |
+
β Saved best model (val_loss=0.0030)
|
| 216 |
+
Epoch 20: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:01<00:00, 2.01it/s, loss=0.00482]
|
| 217 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:00<00:00, 4.38it/s]
|
| 218 |
+
|
| 219 |
+
Epoch 21/100
|
| 220 |
+
Train Loss: 0.0047
|
| 221 |
+
Val Loss: 0.0041
|
| 222 |
+
Val PCK: 0.9989
|
| 223 |
+
Val IoU: 0.9950
|
| 224 |
+
No improvement for 1/20 epochs
|
| 225 |
+
Epoch 21: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:00<00:00, 2.02it/s, loss=0.00384]
|
| 226 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:00<00:00, 4.38it/s]
|
| 227 |
+
|
| 228 |
+
Epoch 22/100
|
| 229 |
+
Train Loss: 0.0046
|
| 230 |
+
Val Loss: 0.0047
|
| 231 |
+
Val PCK: 0.9987
|
| 232 |
+
Val IoU: 0.9946
|
| 233 |
+
No improvement for 2/20 epochs
|
| 234 |
+
Epoch 22: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:00<00:00, 2.01it/s, loss=0.00503]
|
| 235 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββοΏ½οΏ½οΏ½βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [01:59<00:00, 4.39it/s]
|
| 236 |
+
|
| 237 |
+
Epoch 23/100
|
| 238 |
+
Train Loss: 0.0048
|
| 239 |
+
Val Loss: 0.0049
|
| 240 |
+
Val PCK: 0.9986
|
| 241 |
+
Val IoU: 0.9946
|
| 242 |
+
No improvement for 3/20 epochs
|
| 243 |
+
Epoch 23: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:00<00:00, 2.02it/s, loss=0.00434]
|
| 244 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [01:59<00:00, 4.39it/s]
|
| 245 |
+
|
| 246 |
+
Epoch 24/100
|
| 247 |
+
Train Loss: 0.0049
|
| 248 |
+
Val Loss: 0.0026
|
| 249 |
+
Val PCK: 0.9995
|
| 250 |
+
Val IoU: 0.9958
|
| 251 |
+
β Saved best model (val_loss=0.0026)
|
| 252 |
+
Epoch 24: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:01<00:00, 2.01it/s, loss=0.00425]
|
| 253 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:00<00:00, 4.38it/s]
|
| 254 |
+
|
| 255 |
+
Epoch 25/100
|
| 256 |
+
Train Loss: 0.0042
|
| 257 |
+
Val Loss: 0.0029
|
| 258 |
+
Val PCK: 0.9993
|
| 259 |
+
Val IoU: 0.9957
|
| 260 |
+
No improvement for 1/20 epochs
|
| 261 |
+
Epoch 25: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:00<00:00, 2.01it/s, loss=0.00602]
|
| 262 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:00<00:00, 4.38it/s]
|
| 263 |
+
|
| 264 |
+
Epoch 26/100
|
| 265 |
+
Train Loss: 0.0053
|
| 266 |
+
Val Loss: 0.0035
|
| 267 |
+
Val PCK: 0.9995
|
| 268 |
+
Val IoU: 0.9955
|
| 269 |
+
No improvement for 2/20 epochs
|
| 270 |
+
Epoch 26: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:00<00:00, 2.01it/s, loss=0.00537]
|
| 271 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:00<00:00, 4.37it/s]
|
| 272 |
+
|
| 273 |
+
Epoch 27/100
|
| 274 |
+
Train Loss: 0.0044
|
| 275 |
+
Val Loss: 0.0026
|
| 276 |
+
Val PCK: 0.9994
|
| 277 |
+
Val IoU: 0.9958
|
| 278 |
+
β Saved best model (val_loss=0.0026)
|
| 279 |
+
Epoch 27: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:00<00:00, 2.01it/s, loss=0.0028]
|
| 280 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [01:59<00:00, 4.40it/s]
|
| 281 |
+
|
| 282 |
+
Epoch 28/100
|
| 283 |
+
Train Loss: 0.0040
|
| 284 |
+
Val Loss: 0.0025
|
| 285 |
+
Val PCK: 0.9994
|
| 286 |
+
Val IoU: 0.9960
|
| 287 |
+
β Saved best model (val_loss=0.0025)
|
| 288 |
+
Epoch 28: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:01<00:00, 2.01it/s, loss=0.00418]
|
| 289 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββοΏ½οΏ½οΏ½| 526/526 [01:59<00:00, 4.39it/s]
|
| 290 |
+
|
| 291 |
+
Epoch 29/100
|
| 292 |
+
Train Loss: 0.0040
|
| 293 |
+
Val Loss: 0.0031
|
| 294 |
+
Val PCK: 0.9992
|
| 295 |
+
Val IoU: 0.9956
|
| 296 |
+
No improvement for 1/20 epochs
|
| 297 |
+
Epoch 29: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:00<00:00, 2.02it/s, loss=0.00418]
|
| 298 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [05:13<00:00, 1.68it/s]
|
| 299 |
+
|
| 300 |
+
Epoch 30/100
|
| 301 |
+
Train Loss: 0.0052
|
| 302 |
+
Val Loss: 0.0031
|
| 303 |
+
Val PCK: 0.9991
|
| 304 |
+
Val IoU: 0.9958
|
| 305 |
+
No improvement for 2/20 epochs
|
| 306 |
+
Epoch 30: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [32:20<00:00, 1.60s/it, loss=0.00388]
|
| 307 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:18<00:00, 1.39it/s]
|
| 308 |
+
|
| 309 |
+
Epoch 31/100
|
| 310 |
+
Train Loss: 0.0040
|
| 311 |
+
Val Loss: 0.0020
|
| 312 |
+
Val PCK: 0.9994
|
| 313 |
+
Val IoU: 0.9962
|
| 314 |
+
β Saved best model (val_loss=0.0020)
|
| 315 |
+
Epoch 31: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [32:20<00:00, 1.60s/it, loss=0.0036]
|
| 316 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:22<00:00, 1.38it/s]
|
| 317 |
+
|
| 318 |
+
Epoch 32/100
|
| 319 |
+
Train Loss: 0.0041
|
| 320 |
+
Val Loss: 0.0025
|
| 321 |
+
Val PCK: 0.9993
|
| 322 |
+
Val IoU: 0.9960
|
| 323 |
+
No improvement for 1/20 epochs
|
| 324 |
+
Epoch 32: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [32:20<00:00, 1.60s/it, loss=0.00352]
|
| 325 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:16<00:00, 1.40it/s]
|
| 326 |
+
|
| 327 |
+
Epoch 33/100
|
| 328 |
+
Train Loss: 0.0039
|
| 329 |
+
Val Loss: 0.0023
|
| 330 |
+
Val PCK: 0.9992
|
| 331 |
+
Val IoU: 0.9960
|
| 332 |
+
No improvement for 2/20 epochs
|
| 333 |
+
Epoch 33: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [32:14<00:00, 1.60s/it, loss=0.00426]
|
| 334 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:19<00:00, 1.39it/s]
|
| 335 |
+
|
| 336 |
+
Epoch 34/100
|
| 337 |
+
Train Loss: 0.0045
|
| 338 |
+
Val Loss: 0.0022
|
| 339 |
+
Val PCK: 0.9996
|
| 340 |
+
Val IoU: 0.9961
|
| 341 |
+
No improvement for 3/20 epochs
|
| 342 |
+
Epoch 34: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [32:17<00:00, 1.60s/it, loss=0.00387]
|
| 343 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:20<00:00, 1.38it/s]
|
| 344 |
+
|
| 345 |
+
Epoch 35/100
|
| 346 |
+
Train Loss: 0.0038
|
| 347 |
+
Val Loss: 0.0022
|
| 348 |
+
Val PCK: 0.9995
|
| 349 |
+
Val IoU: 0.9960
|
| 350 |
+
No improvement for 4/20 epochs
|
| 351 |
+
Epoch 35: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [32:10<00:00, 1.60s/it, loss=0.00448]
|
| 352 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:16<00:00, 1.40it/s]
|
| 353 |
+
|
| 354 |
+
Epoch 36/100
|
| 355 |
+
Train Loss: 0.0037
|
| 356 |
+
Val Loss: 0.0058
|
| 357 |
+
Val PCK: 0.9976
|
| 358 |
+
Val IoU: 0.9940
|
| 359 |
+
No improvement for 5/20 epochs
|
| 360 |
+
Epoch 36: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [32:08<00:00, 1.59s/it, loss=0.00335]
|
| 361 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:16<00:00, 1.40it/s]
|
| 362 |
+
|
| 363 |
+
Epoch 37/100
|
| 364 |
+
Train Loss: 0.0042
|
| 365 |
+
Val Loss: 0.0023
|
| 366 |
+
Val PCK: 0.9996
|
| 367 |
+
Val IoU: 0.9959
|
| 368 |
+
No improvement for 6/20 epochs
|
| 369 |
+
Epoch 37: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [32:11<00:00, 1.60s/it, loss=0.00228]
|
| 370 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:17<00:00, 1.39it/s]
|
| 371 |
+
|
| 372 |
+
Epoch 38/100
|
| 373 |
+
Train Loss: 0.0042
|
| 374 |
+
Val Loss: 0.0020
|
| 375 |
+
Val PCK: 0.9995
|
| 376 |
+
Val IoU: 0.9961
|
| 377 |
+
No improvement for 7/20 epochs
|
| 378 |
+
Epoch 38: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [32:03<00:00, 1.59s/it, loss=0.00321]
|
| 379 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:22<00:00, 1.38it/s]
|
| 380 |
+
|
| 381 |
+
Epoch 39/100
|
| 382 |
+
Train Loss: 0.0037
|
| 383 |
+
Val Loss: 0.0021
|
| 384 |
+
Val PCK: 0.9995
|
| 385 |
+
Val IoU: 0.9961
|
| 386 |
+
No improvement for 8/20 epochs
|
| 387 |
+
Epoch 39: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [31:56<00:00, 1.58s/it, loss=0.00433]
|
| 388 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:17<00:00, 1.39it/s]
|
| 389 |
+
|
| 390 |
+
Epoch 40/100
|
| 391 |
+
Train Loss: 0.0038
|
| 392 |
+
Val Loss: 0.0026
|
| 393 |
+
Val PCK: 0.9996
|
| 394 |
+
Val IoU: 0.9961
|
| 395 |
+
No improvement for 9/20 epochs
|
| 396 |
+
Epoch 40: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [31:56<00:00, 1.58s/it, loss=0.00479]
|
| 397 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:16<00:00, 1.40it/s]
|
| 398 |
+
|
| 399 |
+
Epoch 41/100
|
| 400 |
+
Train Loss: 0.0036
|
| 401 |
+
Val Loss: 0.0022
|
| 402 |
+
Val PCK: 0.9993
|
| 403 |
+
Val IoU: 0.9961
|
| 404 |
+
No improvement for 10/20 epochs
|
| 405 |
+
Epoch 41: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββοΏ½οΏ½βββββββββββββββ| 1210/1210 [10:01<00:00, 2.01it/s, loss=0.00441]
|
| 406 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:00<00:00, 4.36it/s]
|
| 407 |
+
|
| 408 |
+
Epoch 42/100
|
| 409 |
+
Train Loss: 0.0037
|
| 410 |
+
Val Loss: 0.0022
|
| 411 |
+
Val PCK: 0.9996
|
| 412 |
+
Val IoU: 0.9962
|
| 413 |
+
No improvement for 11/20 epochs
|
| 414 |
+
Epoch 42: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:00<00:00, 2.02it/s, loss=0.00236]
|
| 415 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:00<00:00, 4.37it/s]
|
| 416 |
+
|
| 417 |
+
Epoch 43/100
|
| 418 |
+
Train Loss: 0.0036
|
| 419 |
+
Val Loss: 0.0019
|
| 420 |
+
Val PCK: 0.9995
|
| 421 |
+
Val IoU: 0.9963
|
| 422 |
+
β Saved best model (val_loss=0.0019)
|
| 423 |
+
Epoch 43: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:00<00:00, 2.02it/s, loss=0.00302]
|
| 424 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [03:08<00:00, 2.79it/s]
|
| 425 |
+
|
| 426 |
+
Epoch 44/100
|
| 427 |
+
Train Loss: 0.0036
|
| 428 |
+
Val Loss: 0.0026
|
| 429 |
+
Val PCK: 0.9993
|
| 430 |
+
Val IoU: 0.9961
|
| 431 |
+
No improvement for 1/20 epochs
|
| 432 |
+
Epoch 44: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [32:04<00:00, 1.59s/it, loss=0.00309]
|
| 433 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:19<00:00, 1.39it/s]
|
| 434 |
+
|
| 435 |
+
Epoch 45/100
|
| 436 |
+
Train Loss: 0.0035
|
| 437 |
+
Val Loss: 0.0020
|
| 438 |
+
Val PCK: 0.9994
|
| 439 |
+
Val IoU: 0.9963
|
| 440 |
+
No improvement for 2/20 epochs
|
| 441 |
+
Epoch 45: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [31:41<00:00, 1.57s/it, loss=0.00345]
|
| 442 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:15<00:00, 1.40it/s]
|
| 443 |
+
|
| 444 |
+
Epoch 46/100
|
| 445 |
+
Train Loss: 0.0035
|
| 446 |
+
Val Loss: 0.0019
|
| 447 |
+
Val PCK: 0.9996
|
| 448 |
+
Val IoU: 0.9962
|
| 449 |
+
No improvement for 3/20 epochs
|
| 450 |
+
Epoch 46: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:57<00:00, 1.68s/it, loss=0.00432]
|
| 451 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:04<00:00, 1.44it/s]
|
| 452 |
+
|
| 453 |
+
Epoch 47/100
|
| 454 |
+
Train Loss: 0.0036
|
| 455 |
+
Val Loss: 0.0024
|
| 456 |
+
Val PCK: 0.9996
|
| 457 |
+
Val IoU: 0.9961
|
| 458 |
+
No improvement for 4/20 epochs
|
| 459 |
+
Epoch 47: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [17:26<00:00, 1.16it/s, loss=0.0044]
|
| 460 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:08<00:00, 4.10it/s]
|
| 461 |
+
|
| 462 |
+
Epoch 48/100
|
| 463 |
+
Train Loss: 0.0034
|
| 464 |
+
Val Loss: 0.0017
|
| 465 |
+
Val PCK: 0.9996
|
| 466 |
+
Val IoU: 0.9964
|
| 467 |
+
β Saved best model (val_loss=0.0017)
|
| 468 |
+
Epoch 48: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:32<00:00, 1.91it/s, loss=0.00291]
|
| 469 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:07<00:00, 4.11it/s]
|
| 470 |
+
|
| 471 |
+
Epoch 49/100
|
| 472 |
+
Train Loss: 0.0035
|
| 473 |
+
Val Loss: 0.0018
|
| 474 |
+
Val PCK: 0.9996
|
| 475 |
+
Val IoU: 0.9964
|
| 476 |
+
No improvement for 1/20 epochs
|
| 477 |
+
Epoch 49: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:31<00:00, 1.92it/s, loss=0.00238]
|
| 478 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:07<00:00, 4.11it/s]
|
| 479 |
+
|
| 480 |
+
Epoch 50/100
|
| 481 |
+
Train Loss: 0.0035
|
| 482 |
+
Val Loss: 0.0022
|
| 483 |
+
Val PCK: 0.9993
|
| 484 |
+
Val IoU: 0.9962
|
| 485 |
+
No improvement for 2/20 epochs
|
| 486 |
+
Epoch 50: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:32<00:00, 1.91it/s, loss=0.00336]
|
| 487 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:07<00:00, 4.12it/s]
|
| 488 |
+
|
| 489 |
+
Epoch 51/100
|
| 490 |
+
Train Loss: 0.0035
|
| 491 |
+
Val Loss: 0.0023
|
| 492 |
+
Val PCK: 0.9995
|
| 493 |
+
Val IoU: 0.9962
|
| 494 |
+
No improvement for 3/20 epochs
|
| 495 |
+
Epoch 51: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:32<00:00, 1.91it/s, loss=0.0035]
|
| 496 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:07<00:00, 4.12it/s]
|
| 497 |
+
|
| 498 |
+
Epoch 52/100
|
| 499 |
+
Train Loss: 0.0035
|
| 500 |
+
Val Loss: 0.0018
|
| 501 |
+
Val PCK: 0.9997
|
| 502 |
+
Val IoU: 0.9964
|
| 503 |
+
No improvement for 4/20 epochs
|
| 504 |
+
Epoch 52: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:32<00:00, 1.91it/s, loss=0.00261]
|
| 505 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [02:07<00:00, 4.12it/s]
|
| 506 |
+
|
| 507 |
+
Epoch 53/100
|
| 508 |
+
Train Loss: 0.0034
|
| 509 |
+
Val Loss: 0.0015
|
| 510 |
+
Val PCK: 0.9998
|
| 511 |
+
Val IoU: 0.9966
|
| 512 |
+
β Saved best model (val_loss=0.0015)
|
| 513 |
+
Epoch 53: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [10:31<00:00, 1.92it/s, loss=0.00357]
|
| 514 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββοΏ½οΏ½ββββββββββ| 526/526 [02:08<00:00, 4.10it/s]
|
| 515 |
+
|
| 516 |
+
Epoch 54/100
|
| 517 |
+
Train Loss: 0.0034
|
| 518 |
+
Val Loss: 0.0015
|
| 519 |
+
Val PCK: 0.9998
|
| 520 |
+
Val IoU: 0.9966
|
| 521 |
+
No improvement for 1/20 epochs
|
| 522 |
+
Epoch 54: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [19:13<00:00, 1.05it/s, loss=0.00175]
|
| 523 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:10<00:00, 1.42it/s]
|
| 524 |
+
|
| 525 |
+
Epoch 55/100
|
| 526 |
+
Train Loss: 0.0033
|
| 527 |
+
Val Loss: 0.0016
|
| 528 |
+
Val PCK: 0.9995
|
| 529 |
+
Val IoU: 0.9965
|
| 530 |
+
No improvement for 2/20 epochs
|
| 531 |
+
Epoch 55: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:46<00:00, 1.67s/it, loss=0.00286]
|
| 532 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:03<00:00, 1.45it/s]
|
| 533 |
+
|
| 534 |
+
Epoch 56/100
|
| 535 |
+
Train Loss: 0.0032
|
| 536 |
+
Val Loss: 0.0017
|
| 537 |
+
Val PCK: 0.9996
|
| 538 |
+
Val IoU: 0.9964
|
| 539 |
+
No improvement for 3/20 epochs
|
| 540 |
+
Epoch 56: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:44<00:00, 1.67s/it, loss=0.00308]
|
| 541 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:06<00:00, 1.44it/s]
|
| 542 |
+
|
| 543 |
+
Epoch 57/100
|
| 544 |
+
Train Loss: 0.0033
|
| 545 |
+
Val Loss: 0.0018
|
| 546 |
+
Val PCK: 0.9996
|
| 547 |
+
Val IoU: 0.9965
|
| 548 |
+
No improvement for 4/20 epochs
|
| 549 |
+
Epoch 57: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:40<00:00, 1.67s/it, loss=0.00391]
|
| 550 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:10<00:00, 1.42it/s]
|
| 551 |
+
|
| 552 |
+
Epoch 58/100
|
| 553 |
+
Train Loss: 0.0032
|
| 554 |
+
Val Loss: 0.0016
|
| 555 |
+
Val PCK: 0.9996
|
| 556 |
+
Val IoU: 0.9965
|
| 557 |
+
No improvement for 5/20 epochs
|
| 558 |
+
Epoch 58: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:43<00:00, 1.67s/it, loss=0.00334]
|
| 559 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:07<00:00, 1.43it/s]
|
| 560 |
+
|
| 561 |
+
Epoch 59/100
|
| 562 |
+
Train Loss: 0.0032
|
| 563 |
+
Val Loss: 0.0013
|
| 564 |
+
Val PCK: 0.9999
|
| 565 |
+
Val IoU: 0.9966
|
| 566 |
+
β Saved best model (val_loss=0.0013)
|
| 567 |
+
Epoch 59: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:37<00:00, 1.67s/it, loss=0.00275]
|
| 568 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:07<00:00, 1.43it/s]
|
| 569 |
+
|
| 570 |
+
Epoch 60/100
|
| 571 |
+
Train Loss: 0.0033
|
| 572 |
+
Val Loss: 0.0014
|
| 573 |
+
Val PCK: 0.9998
|
| 574 |
+
Val IoU: 0.9967
|
| 575 |
+
No improvement for 1/20 epochs
|
| 576 |
+
Epoch 60: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:48<00:00, 1.68s/it, loss=0.00286]
|
| 577 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:07<00:00, 1.43it/s]
|
| 578 |
+
|
| 579 |
+
Epoch 61/100
|
| 580 |
+
Train Loss: 0.0032
|
| 581 |
+
Val Loss: 0.0019
|
| 582 |
+
Val PCK: 0.9997
|
| 583 |
+
Val IoU: 0.9964
|
| 584 |
+
No improvement for 2/20 epochs
|
| 585 |
+
Epoch 61: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:47<00:00, 1.68s/it, loss=0.00446]
|
| 586 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:06<00:00, 1.43it/s]
|
| 587 |
+
|
| 588 |
+
Epoch 62/100
|
| 589 |
+
Train Loss: 0.0032
|
| 590 |
+
Val Loss: 0.0015
|
| 591 |
+
Val PCK: 0.9998
|
| 592 |
+
Val IoU: 0.9966
|
| 593 |
+
No improvement for 3/20 epochs
|
| 594 |
+
Epoch 62: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:44<00:00, 1.67s/it, loss=0.00267]
|
| 595 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:05<00:00, 1.44it/s]
|
| 596 |
+
|
| 597 |
+
Epoch 63/100
|
| 598 |
+
Train Loss: 0.0032
|
| 599 |
+
Val Loss: 0.0014
|
| 600 |
+
Val PCK: 0.9997
|
| 601 |
+
Val IoU: 0.9966
|
| 602 |
+
No improvement for 4/20 epochs
|
| 603 |
+
Epoch 63: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:44<00:00, 1.67s/it, loss=0.00254]
|
| 604 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:07<00:00, 1.43it/s]
|
| 605 |
+
|
| 606 |
+
Epoch 64/100
|
| 607 |
+
Train Loss: 0.0031
|
| 608 |
+
Val Loss: 0.0015
|
| 609 |
+
Val PCK: 0.9998
|
| 610 |
+
Val IoU: 0.9966
|
| 611 |
+
No improvement for 5/20 epochs
|
| 612 |
+
Epoch 64: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:44<00:00, 1.67s/it, loss=0.00331]
|
| 613 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:07<00:00, 1.43it/s]
|
| 614 |
+
|
| 615 |
+
Epoch 65/100
|
| 616 |
+
Train Loss: 0.0031
|
| 617 |
+
Val Loss: 0.0015
|
| 618 |
+
Val PCK: 0.9996
|
| 619 |
+
Val IoU: 0.9967
|
| 620 |
+
No improvement for 6/20 epochs
|
| 621 |
+
Epoch 65: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:41<00:00, 1.67s/it, loss=0.00192]
|
| 622 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:04<00:00, 1.44it/s]
|
| 623 |
+
|
| 624 |
+
Epoch 66/100
|
| 625 |
+
Train Loss: 0.0031
|
| 626 |
+
Val Loss: 0.0014
|
| 627 |
+
Val PCK: 0.9997
|
| 628 |
+
Val IoU: 0.9967
|
| 629 |
+
No improvement for 7/20 epochs
|
| 630 |
+
Epoch 66: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββοΏ½οΏ½ββββββββββββββββββββββββ| 1210/1210 [33:51<00:00, 1.68s/it, loss=0.00263]
|
| 631 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:07<00:00, 1.43it/s]
|
| 632 |
+
|
| 633 |
+
Epoch 67/100
|
| 634 |
+
Train Loss: 0.0030
|
| 635 |
+
Val Loss: 0.0015
|
| 636 |
+
Val PCK: 0.9995
|
| 637 |
+
Val IoU: 0.9966
|
| 638 |
+
No improvement for 8/20 epochs
|
| 639 |
+
Epoch 67: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:48<00:00, 1.68s/it, loss=0.00237]
|
| 640 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:09<00:00, 1.42it/s]
|
| 641 |
+
|
| 642 |
+
Epoch 68/100
|
| 643 |
+
Train Loss: 0.0031
|
| 644 |
+
Val Loss: 0.0015
|
| 645 |
+
Val PCK: 0.9995
|
| 646 |
+
Val IoU: 0.9967
|
| 647 |
+
No improvement for 9/20 epochs
|
| 648 |
+
Epoch 68: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:46<00:00, 1.68s/it, loss=0.00166]
|
| 649 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:07<00:00, 1.43it/s]
|
| 650 |
+
|
| 651 |
+
Epoch 69/100
|
| 652 |
+
Train Loss: 0.0031
|
| 653 |
+
Val Loss: 0.0015
|
| 654 |
+
Val PCK: 0.9996
|
| 655 |
+
Val IoU: 0.9966
|
| 656 |
+
No improvement for 10/20 epochs
|
| 657 |
+
Epoch 69: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:54<00:00, 1.68s/it, loss=0.00324]
|
| 658 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:06<00:00, 1.43it/s]
|
| 659 |
+
|
| 660 |
+
Epoch 70/100
|
| 661 |
+
Train Loss: 0.0030
|
| 662 |
+
Val Loss: 0.0015
|
| 663 |
+
Val PCK: 0.9996
|
| 664 |
+
Val IoU: 0.9966
|
| 665 |
+
No improvement for 11/20 epochs
|
| 666 |
+
Epoch 70: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:48<00:00, 1.68s/it, loss=0.00276]
|
| 667 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:07<00:00, 1.43it/s]
|
| 668 |
+
|
| 669 |
+
Epoch 71/100
|
| 670 |
+
Train Loss: 0.0030
|
| 671 |
+
Val Loss: 0.0015
|
| 672 |
+
Val PCK: 0.9996
|
| 673 |
+
Val IoU: 0.9967
|
| 674 |
+
No improvement for 12/20 epochs
|
| 675 |
+
Epoch 71: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:54<00:00, 1.68s/it, loss=0.00347]
|
| 676 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:03<00:00, 1.45it/s]
|
| 677 |
+
|
| 678 |
+
Epoch 72/100
|
| 679 |
+
Train Loss: 0.0030
|
| 680 |
+
Val Loss: 0.0015
|
| 681 |
+
Val PCK: 0.9998
|
| 682 |
+
Val IoU: 0.9967
|
| 683 |
+
No improvement for 13/20 epochs
|
| 684 |
+
Epoch 72: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:52<00:00, 1.68s/it, loss=0.00197]
|
| 685 |
+
Validation: 100%|βββββββββββββββββββοΏ½οΏ½ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:07<00:00, 1.43it/s]
|
| 686 |
+
|
| 687 |
+
Epoch 73/100
|
| 688 |
+
Train Loss: 0.0030
|
| 689 |
+
Val Loss: 0.0014
|
| 690 |
+
Val PCK: 0.9996
|
| 691 |
+
Val IoU: 0.9967
|
| 692 |
+
No improvement for 14/20 epochs
|
| 693 |
+
Epoch 73: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:54<00:00, 1.68s/it, loss=0.0027]
|
| 694 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:06<00:00, 1.43it/s]
|
| 695 |
+
|
| 696 |
+
Epoch 74/100
|
| 697 |
+
Train Loss: 0.0030
|
| 698 |
+
Val Loss: 0.0014
|
| 699 |
+
Val PCK: 0.9997
|
| 700 |
+
Val IoU: 0.9967
|
| 701 |
+
No improvement for 15/20 epochs
|
| 702 |
+
Epoch 74: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:48<00:00, 1.68s/it, loss=0.00273]
|
| 703 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:07<00:00, 1.43it/s]
|
| 704 |
+
|
| 705 |
+
Epoch 75/100
|
| 706 |
+
Train Loss: 0.0030
|
| 707 |
+
Val Loss: 0.0014
|
| 708 |
+
Val PCK: 0.9996
|
| 709 |
+
Val IoU: 0.9966
|
| 710 |
+
No improvement for 16/20 epochs
|
| 711 |
+
Epoch 75: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:46<00:00, 1.67s/it, loss=0.00345]
|
| 712 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:05<00:00, 1.44it/s]
|
| 713 |
+
|
| 714 |
+
Epoch 76/100
|
| 715 |
+
Train Loss: 0.0029
|
| 716 |
+
Val Loss: 0.0017
|
| 717 |
+
Val PCK: 0.9996
|
| 718 |
+
Val IoU: 0.9966
|
| 719 |
+
No improvement for 17/20 epochs
|
| 720 |
+
Epoch 76: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:43<00:00, 1.67s/it, loss=0.00317]
|
| 721 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:05<00:00, 1.44it/s]
|
| 722 |
+
|
| 723 |
+
Epoch 77/100
|
| 724 |
+
Train Loss: 0.0029
|
| 725 |
+
Val Loss: 0.0014
|
| 726 |
+
Val PCK: 0.9997
|
| 727 |
+
Val IoU: 0.9967
|
| 728 |
+
No improvement for 18/20 epochs
|
| 729 |
+
Epoch 77: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:44<00:00, 1.67s/it, loss=0.00289]
|
| 730 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 526/526 [06:07<00:00, 1.43it/s]
|
| 731 |
+
|
| 732 |
+
Epoch 78/100
|
| 733 |
+
Train Loss: 0.0030
|
| 734 |
+
Val Loss: 0.0014
|
| 735 |
+
Val PCK: 0.9996
|
| 736 |
+
Val IoU: 0.9967
|
| 737 |
+
No improvement for 19/20 epochs
|
| 738 |
+
Epoch 78: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1210/1210 [33:53<00:00, 1.68s/it, loss=0.00345]
|
| 739 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββοΏ½οΏ½ββββββββββββββ| 526/526 [06:04<00:00, 1.44it/s]
|
| 740 |
+
|
| 741 |
+
Epoch 79/100
|
| 742 |
+
Train Loss: 0.0029
|
| 743 |
+
Val Loss: 0.0013
|
| 744 |
+
Val PCK: 0.9997
|
| 745 |
+
Val IoU: 0.9967
|
| 746 |
+
No improvement for 20/20 epochs
|
| 747 |
+
|
| 748 |
+
β‘ Early stopping triggered after 79 epochs!
|
| 749 |
+
wandb:
|
| 750 |
+
wandb: Run history:
|
| 751 |
+
wandb: batch ββββ
βββ
ββββββββββββ
ββ
ββββββββββ
βββββ
βββ
β
|
| 752 |
+
wandb: epoch βββββββββββββββββββββ
β
β
β
β
β
ββββββββββββββ
|
| 753 |
+
wandb: finetune/epoch_loss ββββββββββββββββββββββββββββββββββββββββ
|
| 754 |
+
wandb: finetune/l_angle ββββββββββββββββββββββββββββββββββββββββ
|
| 755 |
+
wandb: finetune/l_diag ββββββββββββββββ
ββββββββββββββββββββββββ
|
| 756 |
+
wandb: finetune/l_edge ββββββββββββββββββ
ββββββββββββββββββββββ
|
| 757 |
+
wandb: finetune/l_hm ββββββ
β
βββββββββββββββββββββββββββββββββ
|
| 758 |
+
wandb: finetune/l_kpt ββββββββββββββββββββββββββββββββββββββββ
|
| 759 |
+
wandb: finetune/loss βββββββββββββββββ
βββββββββββββββββββββββ
|
| 760 |
+
wandb: finetune/lr ββ
ββββββββββββββββ
β
β
ββββββββββββββββββββ
|
| 761 |
+
wandb: +5 ...
|
| 762 |
+
wandb:
|
| 763 |
+
wandb: Run summary:
|
| 764 |
+
wandb: batch 1200
|
| 765 |
+
wandb: best_epoch 59
|
| 766 |
+
wandb: best_val_loss 0.0013
|
| 767 |
+
wandb: epoch 79
|
| 768 |
+
wandb: finetune/epoch_loss 0.00295
|
| 769 |
+
wandb: finetune/l_angle 2e-05
|
| 770 |
+
wandb: finetune/l_diag 0.0
|
| 771 |
+
wandb: finetune/l_edge 1e-05
|
| 772 |
+
wandb: finetune/l_hm 3e-05
|
| 773 |
+
wandb: finetune/l_kpt 2e-05
|
| 774 |
+
wandb: +7 ...
|
| 775 |
+
wandb:
|
| 776 |
+
wandb: View run Finetune_01/29_06:15 at: https://wandb.ai/adithyanraj03/courtkeynet/runs/0fnvysj4
|
| 777 |
+
wandb: View project at: https://wandb.ai/adithyanraj03/courtkeynet
|
| 778 |
+
wandb: Synced 5 W&B file(s), 80 media file(s), 2 artifact file(s) and 0 other file(s)
|
| 779 |
+
wandb: Find logs at: .\wandb\run-20260129_061557-0fnvysj4\logs
|
| 780 |
+
|
| 781 |
+
============================================================
|
| 782 |
+
Fine-tuning Complete!
|
| 783 |
+
Best val loss: 0.0013
|
| 784 |
+
Weights saved to: runs\courtkeynet_finetune\finetune_20260129_061601_0fnvysj4
|
| 785 |
+
============================================================
|
pretrained/courtkeynet_base.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ca621ccca7341ebe9d93e0cb04d09703487836dcca8a9723a8589273b8a99630
|
| 3 |
+
size 4977848
|
pretrained/training_log.txt
ADDED
|
@@ -0,0 +1,1026 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
Train Loss: 0.0851
|
| 2 |
+
Val Loss: 0.1043
|
| 3 |
+
Val PCK: 0.9900
|
| 4 |
+
Val IoU: 0.9398
|
| 5 |
+
No improvement for 1/20 epochs
|
| 6 |
+
Epoch 48: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [19:36<00:00, 2.00it/s, loss=0.0841]
|
| 7 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:28<00:00, 5.25it/s]
|
| 8 |
+
|
| 9 |
+
Epoch 49/300
|
| 10 |
+
Train Loss: 0.0846
|
| 11 |
+
Val Loss: 0.1037
|
| 12 |
+
Val PCK: 0.9902
|
| 13 |
+
Val IoU: 0.9401
|
| 14 |
+
No improvement for 2/20 epochs
|
| 15 |
+
Epoch 49: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [19:23<00:00, 2.03it/s, loss=0.0846]
|
| 16 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:28<00:00, 5.29it/s]
|
| 17 |
+
|
| 18 |
+
Epoch 50/300
|
| 19 |
+
Train Loss: 0.0845
|
| 20 |
+
Val Loss: 0.1036
|
| 21 |
+
Val PCK: 0.9901
|
| 22 |
+
Val IoU: 0.9408
|
| 23 |
+
Saved best model (val_loss=0.1036)
|
| 24 |
+
Epoch 50: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [19:31<00:00, 2.01it/s, loss=0.086]
|
| 25 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:31<00:00, 5.07it/s]
|
| 26 |
+
|
| 27 |
+
Epoch 51/300
|
| 28 |
+
Train Loss: 0.0849
|
| 29 |
+
Val Loss: 0.1043
|
| 30 |
+
Val PCK: 0.9901
|
| 31 |
+
Val IoU: 0.9394
|
| 32 |
+
No improvement for 1/20 epochs
|
| 33 |
+
Epoch 51: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [19:17<00:00, 2.04it/s, loss=0.0833]
|
| 34 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:28<00:00, 5.26it/s]
|
| 35 |
+
|
| 36 |
+
Epoch 52/300
|
| 37 |
+
Train Loss: 0.0840
|
| 38 |
+
Val Loss: 0.1040
|
| 39 |
+
Val PCK: 0.9905
|
| 40 |
+
Val IoU: 0.9404
|
| 41 |
+
No improvement for 2/20 epochs
|
| 42 |
+
Epoch 52: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [19:30<00:00, 2.01it/s, loss=0.083]
|
| 43 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:27<00:00, 5.32it/s]
|
| 44 |
+
|
| 45 |
+
Epoch 53/300
|
| 46 |
+
Train Loss: 0.0838
|
| 47 |
+
Val Loss: 0.1037
|
| 48 |
+
Val PCK: 0.9896
|
| 49 |
+
Val Loss: 0.1045
|
| 50 |
+
Val PCK: 0.9897
|
| 51 |
+
Val IoU: 0.9396
|
| 52 |
+
No improvement for 4/20 epochs
|
| 53 |
+
Epoch 54: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [19:26<00:00, 2.02it/s, loss=0.0849]
|
| 54 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:30<00:00, 5.16it/s]
|
| 55 |
+
|
| 56 |
+
Epoch 55/300
|
| 57 |
+
Train Loss: 0.0829
|
| 58 |
+
Val Loss: 0.1067
|
| 59 |
+
Val PCK: 0.9901
|
| 60 |
+
Val IoU: 0.9382
|
| 61 |
+
No improvement for 5/20 epochs
|
| 62 |
+
Epoch 55: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [19:28<00:00, 2.02it/s, loss=0.0827]
|
| 63 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:27<00:00, 5.34it/s]
|
| 64 |
+
|
| 65 |
+
Epoch 56/300
|
| 66 |
+
Train Loss: 0.0826
|
| 67 |
+
Val Loss: 0.1044
|
| 68 |
+
Val PCK: 0.9893
|
| 69 |
+
Val IoU: 0.9396
|
| 70 |
+
No improvement for 6/20 epochs
|
| 71 |
+
Epoch 56: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [19:17<00:00, 2.04it/s, loss=0.0837]
|
| 72 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:18<00:00, 5.90it/s]
|
| 73 |
+
|
| 74 |
+
Epoch 57/300
|
| 75 |
+
Train Loss: 0.0825
|
| 76 |
+
Val Loss: 0.1036
|
| 77 |
+
Val PCK: 0.9909
|
| 78 |
+
Val IoU: 0.9401
|
| 79 |
+
No improvement for 7/20 epochs
|
| 80 |
+
Epoch 57: 13%|βββββββββββ | 300/2357 [02:36<17:02, 2.01it/s, loss=0.08Epoch 57: 13%|βββββββββββ | 300/2357 [02:37<17:02, 2.01it/s, loss=0.08Epoch 57: 13%EpEpoch 57: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [19:20<00:00, 2.03it/s, loss=0.0822]
|
| 81 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:29<00:00, 5.20it/s]
|
| 82 |
+
|
| 83 |
+
Epoch 58/300
|
| 84 |
+
Train Loss: 0.0825
|
| 85 |
+
Val Loss: 0.1024
|
| 86 |
+
Val PCK: 0.9901
|
| 87 |
+
Val IoU: 0.9409
|
| 88 |
+
Saved best model (val_loss=0.1024)
|
| 89 |
+
Epoch 58: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [19:33<00:00, 2.01it/s, loss=0.0814]
|
| 90 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:31<00:00, 5.11it/s]
|
| 91 |
+
|
| 92 |
+
Epoch 59/300
|
| 93 |
+
Train Loss: 0.0819
|
| 94 |
+
Val Loss: 0.1019
|
| 95 |
+
Val PCK: 0.9909
|
| 96 |
+
Val IoU: 0.9416
|
| 97 |
+
Saved best model (val_loss=0.1019)
|
| 98 |
+
Epoch 59: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:35<00:00, 2.11it/s, loss=0.0809]
|
| 99 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:22<00:00, 5.62it/s]
|
| 100 |
+
|
| 101 |
+
Epoch 60/300
|
| 102 |
+
Train Loss: 0.0819
|
| 103 |
+
Val Loss: 0.1028
|
| 104 |
+
Val PCK: 0.9907
|
| 105 |
+
Val IoU: 0.9403
|
| 106 |
+
No improvement for 1/20 epochs
|
| 107 |
+
Epoch 60: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:50<00:00, 2.08it/s, loss=0.0836]
|
| 108 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:19<00:00, 5.85it/s]
|
| 109 |
+
|
| 110 |
+
Epoch 61/300
|
| 111 |
+
Train Loss: 0.0815
|
| 112 |
+
Val Loss: 0.1050
|
| 113 |
+
Val PCK: 0.9889
|
| 114 |
+
Val IoU: 0.9397
|
| 115 |
+
No improvement for 2/20 epochs
|
| 116 |
+
Epoch 61: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:54<00:00, 2.08it/s, loss=0.0805]
|
| 117 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:19<00:00, 5.86it/s]
|
| 118 |
+
|
| 119 |
+
Epoch 62/300
|
| 120 |
+
Train Loss: 0.0813
|
| 121 |
+
Val Loss: 0.1039
|
| 122 |
+
Val PCK: 0.9883
|
| 123 |
+
Val IoU: 0.9402
|
| 124 |
+
No improvement for 3/20 epochs
|
| 125 |
+
Epoch 62: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:15<00:00, 2.15it/s, loss=0.0814]
|
| 126 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:04<00:00, 7.24it/s]
|
| 127 |
+
|
| 128 |
+
Epoch 63/300
|
| 129 |
+
Train Loss: 0.0806
|
| 130 |
+
Val Loss: 0.1029
|
| 131 |
+
Val PCK: 0.9901
|
| 132 |
+
Val IoU: 0.9409
|
| 133 |
+
No improvement for 4/20 epochs
|
| 134 |
+
Epoch 63: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:37<00:00, 2.69it/s, loss=0.0826]
|
| 135 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:05<00:00, 7.17it/s]
|
| 136 |
+
|
| 137 |
+
Epoch 64/300
|
| 138 |
+
Train Loss: 0.0809
|
| 139 |
+
Val Loss: 0.1032
|
| 140 |
+
Val PCK: 0.9888
|
| 141 |
+
Val IoU: 0.9407
|
| 142 |
+
No improvement for 5/20 epochs
|
| 143 |
+
Epoch 64: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:36<00:00, 2.69it/s, loss=0.0811]
|
| 144 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:04<00:00, 7.27it/s]
|
| 145 |
+
|
| 146 |
+
Epoch 65/300
|
| 147 |
+
Train Loss: 0.0803
|
| 148 |
+
Val Loss: 0.1012
|
| 149 |
+
Val PCK: 0.9907
|
| 150 |
+
Val IoU: 0.9411
|
| 151 |
+
Saved best model (val_loss=0.1012)
|
| 152 |
+
Epoch 65: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:35<00:00, 2.69it/s, loss=0.082]
|
| 153 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:04<00:00, 7.25it/s]
|
| 154 |
+
|
| 155 |
+
Epoch 66/300
|
| 156 |
+
Train Loss: 0.0801
|
| 157 |
+
Val Loss: 0.1036
|
| 158 |
+
Val PCK: 0.9888
|
| 159 |
+
Val IoU: 0.9404
|
| 160 |
+
No improvement for 1/20 epochs
|
| 161 |
+
Epoch 66: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:35<00:00, 2.69it/s, loss=0.0805]
|
| 162 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:04<00:00, 7.22it/s]
|
| 163 |
+
|
| 164 |
+
Epoch 67/300
|
| 165 |
+
Train Loss: 0.0799
|
| 166 |
+
Val Loss: 0.1013
|
| 167 |
+
Val PCK: 0.9907
|
| 168 |
+
Val IoU: 0.9416
|
| 169 |
+
No improvement for 2/20 epochs
|
| 170 |
+
Epoch 67: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:36<00:00, 2.69it/s, loss=0.0801]
|
| 171 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:04<00:00, 7.22it/s]
|
| 172 |
+
|
| 173 |
+
Epoch 68/300
|
| 174 |
+
Train Loss: 0.0795
|
| 175 |
+
Val Loss: 0.1016
|
| 176 |
+
Val PCK: 0.9901
|
| 177 |
+
Val IoU: 0.9412
|
| 178 |
+
No improvement for 3/20 epochs
|
| 179 |
+
Epoch 68: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:36<00:00, 2.69it/s, loss=0.0796]
|
| 180 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:04<00:00, 7.24it/s]
|
| 181 |
+
|
| 182 |
+
Epoch 69/300
|
| 183 |
+
Train Loss: 0.0792
|
| 184 |
+
Val Loss: 0.1022
|
| 185 |
+
Val PCK: 0.9901
|
| 186 |
+
Val IoU: 0.9413
|
| 187 |
+
No improvement for 4/20 epochs
|
| 188 |
+
Epoch 69: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [17:34<00:00, 2.23it/s, loss=0.0802]
|
| 189 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:19<00:00, 5.87it/s]
|
| 190 |
+
|
| 191 |
+
Epoch 70/300
|
| 192 |
+
Train Loss: 0.0791
|
| 193 |
+
Val Loss: 0.1022
|
| 194 |
+
Val PCK: 0.9914
|
| 195 |
+
Val IoU: 0.9411
|
| 196 |
+
No improvement for 5/20 epochs
|
| 197 |
+
Epoch 70: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:46<00:00, 2.09it/s, loss=0.0795]
|
| 198 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:19<00:00, 5.87it/s]
|
| 199 |
+
|
| 200 |
+
Epoch 71/300
|
| 201 |
+
Train Loss: 0.0791
|
| 202 |
+
Val Loss: 0.1043
|
| 203 |
+
Val PCK: 0.9896
|
| 204 |
+
Val IoU: 0.9403
|
| 205 |
+
No improvement for 6/20 epochs
|
| 206 |
+
Epoch 71: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:43<00:00, 2.10it/s, loss=0.0793]
|
| 207 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:19<00:00, 5.86it/s]
|
| 208 |
+
|
| 209 |
+
Epoch 72/300
|
| 210 |
+
Train Loss: 0.0791
|
| 211 |
+
Val Loss: 0.1025
|
| 212 |
+
Val PCK: 0.9895
|
| 213 |
+
Val IoU: 0.9410
|
| 214 |
+
No improvement for 7/20 epochs
|
| 215 |
+
Epoch 72: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:44<00:00, 2.10it/s, loss=0.0791]
|
| 216 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:19<00:00, 5.88it/s]
|
| 217 |
+
|
| 218 |
+
Epoch 73/300
|
| 219 |
+
Train Loss: 0.0785
|
| 220 |
+
Val Loss: 0.1025
|
| 221 |
+
Val PCK: 0.9898
|
| 222 |
+
Val IoU: 0.9405
|
| 223 |
+
No improvement for 8/20 epochs
|
| 224 |
+
Epoch 73: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:46<00:00, 2.09it/s, loss=0.0834]
|
| 225 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:19<00:00, 5.88it/s]
|
| 226 |
+
|
| 227 |
+
Epoch 74/300
|
| 228 |
+
Train Loss: 0.0788
|
| 229 |
+
Val Loss: 0.1035
|
| 230 |
+
Val PCK: 0.9894
|
| 231 |
+
Val IoU: 0.9403
|
| 232 |
+
No improvement for 9/20 epochs
|
| 233 |
+
Epoch 74: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:44<00:00, 2.10it/s, loss=0.0771]
|
| 234 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:18<00:00, 5.90it/s]
|
| 235 |
+
|
| 236 |
+
Epoch 75/300
|
| 237 |
+
Train Loss: 0.0784
|
| 238 |
+
Val Loss: 0.1013
|
| 239 |
+
Val PCK: 0.9898
|
| 240 |
+
Val IoU: 0.9416
|
| 241 |
+
No improvement for 10/20 epochs
|
| 242 |
+
Epoch 75: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:46<00:00, 2.09it/s, loss=0.0777]
|
| 243 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:19<00:00, 5.89it/s]
|
| 244 |
+
|
| 245 |
+
Epoch 76/300
|
| 246 |
+
Train Loss: 0.0784
|
| 247 |
+
Val Loss: 0.1025
|
| 248 |
+
Val PCK: 0.9906
|
| 249 |
+
Val IoU: 0.9410
|
| 250 |
+
No improvement for 11/20 epochs
|
| 251 |
+
Epoch 76: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:45<00:00, 2.09it/s, loss=0.0778]
|
| 252 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:18<00:00, 5.90it/s]
|
| 253 |
+
|
| 254 |
+
Epoch 77/300
|
| 255 |
+
Train Loss: 0.0778
|
| 256 |
+
Val Loss: 0.1008
|
| 257 |
+
Val PCK: 0.9914
|
| 258 |
+
Val IoU: 0.9418
|
| 259 |
+
Saved best model (val_loss=0.1008)
|
| 260 |
+
Epoch 77: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:45<00:00, 2.09it/s, loss=0.0765]
|
| 261 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:19<00:00, 5.90it/s]
|
| 262 |
+
|
| 263 |
+
Epoch 78/300
|
| 264 |
+
Train Loss: 0.0772
|
| 265 |
+
Val Loss: 0.1011
|
| 266 |
+
Val PCK: 0.9914
|
| 267 |
+
Val IoU: 0.9415
|
| 268 |
+
No improvement for 1/20 epochs
|
| 269 |
+
Epoch 78: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:45<00:00, 2.09it/s, loss=0.0772]
|
| 270 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:19<00:00, 5.88it/s]
|
| 271 |
+
|
| 272 |
+
Epoch 79/300
|
| 273 |
+
Train Loss: 0.0777
|
| 274 |
+
Val Loss: 0.1007
|
| 275 |
+
Val PCK: 0.9910
|
| 276 |
+
Val IoU: 0.9417
|
| 277 |
+
Saved best model (val_loss=0.1007)
|
| 278 |
+
Epoch 79: 100%|βββββββββββοΏ½οΏ½ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:44<00:00, 2.10it/s, loss=0.0768]
|
| 279 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:19<00:00, 5.89it/s]
|
| 280 |
+
|
| 281 |
+
Epoch 80/300
|
| 282 |
+
Train Loss: 0.0776
|
| 283 |
+
Val Loss: 0.1001
|
| 284 |
+
Val PCK: 0.9916
|
| 285 |
+
Val IoU: 0.9419
|
| 286 |
+
Saved best model (val_loss=0.1001)
|
| 287 |
+
Epoch 80: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:44<00:00, 2.10it/s, loss=0.0783]
|
| 288 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:19<00:00, 5.89it/s]
|
| 289 |
+
|
| 290 |
+
Epoch 81/300
|
| 291 |
+
Train Loss: 0.0778
|
| 292 |
+
Val Loss: 0.0998
|
| 293 |
+
Val PCK: 0.9912
|
| 294 |
+
Val IoU: 0.9426
|
| 295 |
+
Saved best model (val_loss=0.0998)
|
| 296 |
+
Epoch 81: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:44<00:00, 2.10it/s, loss=0.0773]
|
| 297 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:19<00:00, 5.88it/s]
|
| 298 |
+
|
| 299 |
+
Epoch 82/300
|
| 300 |
+
Train Loss: 0.0766
|
| 301 |
+
Val Loss: 0.1029
|
| 302 |
+
Val PCK: 0.9907
|
| 303 |
+
Val IoU: 0.9398
|
| 304 |
+
No improvement for 1/20 epochs
|
| 305 |
+
Epoch 82: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:44<00:00, 2.10it/s, loss=0.0763]
|
| 306 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:19<00:00, 5.90it/s]
|
| 307 |
+
|
| 308 |
+
Epoch 83/300
|
| 309 |
+
Train Loss: 0.0767
|
| 310 |
+
Val Loss: 0.1020
|
| 311 |
+
Val PCK: 0.9900
|
| 312 |
+
Val IoU: 0.9412
|
| 313 |
+
No improvement for 2/20 epochs
|
| 314 |
+
Epoch 83: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:43<00:00, 2.10it/s, loss=0.0799]
|
| 315 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:19<00:00, 5.88it/s]
|
| 316 |
+
|
| 317 |
+
Epoch 84/300
|
| 318 |
+
Train Loss: 0.0770
|
| 319 |
+
Val Loss: 0.1022
|
| 320 |
+
Val PCK: 0.9905
|
| 321 |
+
Val IoU: 0.9409
|
| 322 |
+
No improvement for 3/20 epochs
|
| 323 |
+
Epoch 84: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [18:43<00:00, 2.10it/s, loss=0.0766]
|
| 324 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:18<00:00, 5.90it/s]
|
| 325 |
+
|
| 326 |
+
Epoch 85/300
|
| 327 |
+
Train Loss: 0.0766
|
| 328 |
+
Val Loss: 0.1006
|
| 329 |
+
Val PCK: 0.9917
|
| 330 |
+
Val IoU: 0.9418
|
| 331 |
+
No improvement for 4/20 epochs
|
| 332 |
+
Epoch 85: 100%|ββββββββββββββββββββββββββββββββββββββοΏ½οΏ½βββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [19:51<00:00, 1.98it/s, loss=0.0764]
|
| 333 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.46it/s]
|
| 334 |
+
|
| 335 |
+
Epoch 86/300
|
| 336 |
+
Train Loss: 0.0766
|
| 337 |
+
Val Loss: 0.1012
|
| 338 |
+
Val PCK: 0.9906
|
| 339 |
+
Val IoU: 0.9419
|
| 340 |
+
No improvement for 5/20 epochs
|
| 341 |
+
Epoch 86: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.65it/s, loss=0.076]
|
| 342 |
+
Validation: 0%| | 0/466 [00:00<?, ?it/s]C:\Users\rajur\miniconda3\envs\gpu\Lib\site-packages\albumentations\check_version.py:147: UserWarning: Error fetching version info The read operation timed out
|
| 343 |
+
data = fetch_version_info()
|
| 344 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:08<00:00, 6.83it/s]
|
| 345 |
+
|
| 346 |
+
Epoch 87/300
|
| 347 |
+
Train Loss: 0.0764
|
| 348 |
+
Val Loss: 0.1021
|
| 349 |
+
Val PCK: 0.9904
|
| 350 |
+
Val IoU: 0.9415
|
| 351 |
+
No improvement for 6/20 epochs
|
| 352 |
+
Epoch 87: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0807]
|
| 353 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.46it/s]
|
| 354 |
+
|
| 355 |
+
Epoch 88/300
|
| 356 |
+
Train Loss: 0.0761
|
| 357 |
+
Val Loss: 0.1008
|
| 358 |
+
Val PCK: 0.9911
|
| 359 |
+
Val IoU: 0.9421
|
| 360 |
+
No improvement for 7/20 epochs
|
| 361 |
+
Epoch 88: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0754]
|
| 362 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.47it/s]
|
| 363 |
+
|
| 364 |
+
Epoch 89/300
|
| 365 |
+
Train Loss: 0.0760
|
| 366 |
+
Val Loss: 0.1008
|
| 367 |
+
Val PCK: 0.9904
|
| 368 |
+
Val IoU: 0.9416
|
| 369 |
+
No improvement for 8/20 epochs
|
| 370 |
+
Epoch 89: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:52<00:00, 2.64it/s, loss=0.0742]
|
| 371 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.42it/s]
|
| 372 |
+
|
| 373 |
+
Epoch 90/300
|
| 374 |
+
Train Loss: 0.0757
|
| 375 |
+
Val Loss: 0.1011
|
| 376 |
+
Val PCK: 0.9888
|
| 377 |
+
Val IoU: 0.9419
|
| 378 |
+
No improvement for 9/20 epochs
|
| 379 |
+
Epoch 90: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0749]
|
| 380 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.41it/s]
|
| 381 |
+
|
| 382 |
+
Epoch 91/300
|
| 383 |
+
Train Loss: 0.0755
|
| 384 |
+
Val Loss: 0.1005
|
| 385 |
+
Val PCK: 0.9899
|
| 386 |
+
Val IoU: 0.9420
|
| 387 |
+
No improvement for 10/20 epochs
|
| 388 |
+
Epoch 91: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0765]
|
| 389 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.41it/s]
|
| 390 |
+
|
| 391 |
+
Epoch 92/300
|
| 392 |
+
Train Loss: 0.0757
|
| 393 |
+
Val Loss: 0.1001
|
| 394 |
+
Val PCK: 0.9904
|
| 395 |
+
Val IoU: 0.9420
|
| 396 |
+
No improvement for 11/20 epochs
|
| 397 |
+
Epoch 92: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0755]
|
| 398 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.40it/s]
|
| 399 |
+
|
| 400 |
+
Epoch 93/300
|
| 401 |
+
Train Loss: 0.0752
|
| 402 |
+
Val Loss: 0.1013
|
| 403 |
+
Val PCK: 0.9897
|
| 404 |
+
Val IoU: 0.9416
|
| 405 |
+
No improvement for 12/20 epochs
|
| 406 |
+
Epoch 93: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0754]
|
| 407 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.43it/s]
|
| 408 |
+
|
| 409 |
+
Epoch 94/300
|
| 410 |
+
Train Loss: 0.0754
|
| 411 |
+
Val Loss: 0.1005
|
| 412 |
+
Val PCK: 0.9904
|
| 413 |
+
Val IoU: 0.9414
|
| 414 |
+
No improvement for 13/20 epochs
|
| 415 |
+
Epoch 94: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0755]
|
| 416 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.44it/s]
|
| 417 |
+
|
| 418 |
+
Epoch 95/300
|
| 419 |
+
Train Loss: 0.0754
|
| 420 |
+
Val Loss: 0.1008
|
| 421 |
+
Val PCK: 0.9907
|
| 422 |
+
Val IoU: 0.9418
|
| 423 |
+
No improvement for 14/20 epochs
|
| 424 |
+
Epoch 95: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0731]
|
| 425 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:03<00:00, 7.39it/s]
|
| 426 |
+
|
| 427 |
+
Epoch 96/300
|
| 428 |
+
Train Loss: 0.0749
|
| 429 |
+
Val Loss: 0.1001
|
| 430 |
+
Val PCK: 0.9915
|
| 431 |
+
Val IoU: 0.9417
|
| 432 |
+
No improvement for 15/20 epochs
|
| 433 |
+
Epoch 96: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0767]
|
| 434 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.40it/s]
|
| 435 |
+
|
| 436 |
+
Epoch 97/300
|
| 437 |
+
Train Loss: 0.0747
|
| 438 |
+
Val Loss: 0.1010
|
| 439 |
+
Val PCK: 0.9899
|
| 440 |
+
Val IoU: 0.9420
|
| 441 |
+
No improvement for 16/20 epochs
|
| 442 |
+
Epoch 97: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0747]
|
| 443 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.42it/s]
|
| 444 |
+
|
| 445 |
+
Epoch 98/300
|
| 446 |
+
Train Loss: 0.0744
|
| 447 |
+
Val Loss: 0.1005
|
| 448 |
+
Val PCK: 0.9905
|
| 449 |
+
Val IoU: 0.9418
|
| 450 |
+
No improvement for 17/20 epochs
|
| 451 |
+
Epoch 98: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0737]
|
| 452 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.42it/s]
|
| 453 |
+
|
| 454 |
+
Epoch 99/300
|
| 455 |
+
Train Loss: 0.0744
|
| 456 |
+
Val Loss: 0.0991
|
| 457 |
+
Val PCK: 0.9914
|
| 458 |
+
Val IoU: 0.9428
|
| 459 |
+
Saved best model (val_loss=0.0991)
|
| 460 |
+
Epoch 99: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0734]
|
| 461 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.41it/s]
|
| 462 |
+
|
| 463 |
+
Epoch 100/300
|
| 464 |
+
Train Loss: 0.0746
|
| 465 |
+
Val Loss: 0.0994
|
| 466 |
+
Val PCK: 0.9916
|
| 467 |
+
Val IoU: 0.9421
|
| 468 |
+
No improvement for 1/20 epochs
|
| 469 |
+
Epoch 100: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0759]
|
| 470 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.41it/s]
|
| 471 |
+
|
| 472 |
+
Epoch 101/300
|
| 473 |
+
Train Loss: 0.0746
|
| 474 |
+
Val Loss: 0.1013
|
| 475 |
+
Val PCK: 0.9898
|
| 476 |
+
Val IoU: 0.9415
|
| 477 |
+
No improvement for 2/20 epochs
|
| 478 |
+
Epoch 101: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0741]
|
| 479 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.43it/s]
|
| 480 |
+
|
| 481 |
+
Epoch 102/300
|
| 482 |
+
Train Loss: 0.0743
|
| 483 |
+
Val Loss: 0.0985
|
| 484 |
+
Val PCK: 0.9914
|
| 485 |
+
Val IoU: 0.9429
|
| 486 |
+
Saved best model (val_loss=0.0985)
|
| 487 |
+
Epoch 102: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0743]
|
| 488 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.42it/s]
|
| 489 |
+
|
| 490 |
+
Epoch 103/300
|
| 491 |
+
Train Loss: 0.0741
|
| 492 |
+
Val Loss: 0.0995
|
| 493 |
+
Val PCK: 0.9910
|
| 494 |
+
Val IoU: 0.9423
|
| 495 |
+
No improvement for 1/20 epochs
|
| 496 |
+
Epoch 103: 100%|ββββοΏ½οΏ½ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0753]
|
| 497 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.40it/s]
|
| 498 |
+
|
| 499 |
+
Epoch 104/300
|
| 500 |
+
Train Loss: 0.0740
|
| 501 |
+
Val Loss: 0.0994
|
| 502 |
+
Val PCK: 0.9913
|
| 503 |
+
Val IoU: 0.9426
|
| 504 |
+
No improvement for 2/20 epochs
|
| 505 |
+
Epoch 104: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0748]
|
| 506 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:03<00:00, 7.38it/s]
|
| 507 |
+
|
| 508 |
+
Epoch 105/300
|
| 509 |
+
Train Loss: 0.0738
|
| 510 |
+
Val Loss: 0.1003
|
| 511 |
+
Val PCK: 0.9918
|
| 512 |
+
Val IoU: 0.9418
|
| 513 |
+
No improvement for 3/20 epochs
|
| 514 |
+
Epoch 105: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0725]
|
| 515 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.42it/s]
|
| 516 |
+
|
| 517 |
+
Epoch 106/300
|
| 518 |
+
Train Loss: 0.0737
|
| 519 |
+
Val Loss: 0.0994
|
| 520 |
+
Val PCK: 0.9909
|
| 521 |
+
Val IoU: 0.9426
|
| 522 |
+
No improvement for 4/20 epochs
|
| 523 |
+
Epoch 106: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0737]
|
| 524 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:03<00:00, 7.39it/s]
|
| 525 |
+
|
| 526 |
+
Epoch 107/300
|
| 527 |
+
Train Loss: 0.0735
|
| 528 |
+
Val Loss: 0.0991
|
| 529 |
+
Val PCK: 0.9918
|
| 530 |
+
Val IoU: 0.9425
|
| 531 |
+
No improvement for 5/20 epochs
|
| 532 |
+
Epoch 107: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0746]
|
| 533 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.41it/s]
|
| 534 |
+
|
| 535 |
+
Epoch 108/300
|
| 536 |
+
Train Loss: 0.0734
|
| 537 |
+
Val Loss: 0.0998
|
| 538 |
+
Val PCK: 0.9908
|
| 539 |
+
Val IoU: 0.9421
|
| 540 |
+
No improvement for 6/20 epochs
|
| 541 |
+
Epoch 108: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0731]
|
| 542 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.42it/s]
|
| 543 |
+
|
| 544 |
+
Epoch 109/300
|
| 545 |
+
Train Loss: 0.0733
|
| 546 |
+
Val Loss: 0.0999
|
| 547 |
+
Val PCK: 0.9914
|
| 548 |
+
Val IoU: 0.9421
|
| 549 |
+
No improvement for 7/20 epochs
|
| 550 |
+
Epoch 109: 100%|βββββββββββββββββββββββββββββββββββοΏ½οΏ½ββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:52<00:00, 2.64it/s, loss=0.073]
|
| 551 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:03<00:00, 7.39it/s]
|
| 552 |
+
|
| 553 |
+
Epoch 110/300
|
| 554 |
+
Train Loss: 0.0733
|
| 555 |
+
Val Loss: 0.0993
|
| 556 |
+
Val PCK: 0.9905
|
| 557 |
+
Val IoU: 0.9425
|
| 558 |
+
No improvement for 8/20 epochs
|
| 559 |
+
Epoch 110: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0715]
|
| 560 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.43it/s]
|
| 561 |
+
|
| 562 |
+
Epoch 111/300
|
| 563 |
+
Train Loss: 0.0731
|
| 564 |
+
Val Loss: 0.0994
|
| 565 |
+
Val PCK: 0.9905
|
| 566 |
+
Val IoU: 0.9423
|
| 567 |
+
No improvement for 9/20 epochs
|
| 568 |
+
Epoch 111: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0724]
|
| 569 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.41it/s]
|
| 570 |
+
|
| 571 |
+
Epoch 112/300
|
| 572 |
+
Train Loss: 0.0730
|
| 573 |
+
Val Loss: 0.0989
|
| 574 |
+
Val PCK: 0.9911
|
| 575 |
+
Val IoU: 0.9427
|
| 576 |
+
No improvement for 10/20 epochs
|
| 577 |
+
Epoch 112: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0738]
|
| 578 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:03<00:00, 7.38it/s]
|
| 579 |
+
|
| 580 |
+
Epoch 113/300
|
| 581 |
+
Train Loss: 0.0733
|
| 582 |
+
Val Loss: 0.1001
|
| 583 |
+
Val PCK: 0.9907
|
| 584 |
+
Val IoU: 0.9416
|
| 585 |
+
No improvement for 11/20 epochs
|
| 586 |
+
Epoch 113: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.073]
|
| 587 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.44it/s]
|
| 588 |
+
|
| 589 |
+
Epoch 114/300
|
| 590 |
+
Train Loss: 0.0726
|
| 591 |
+
Val Loss: 0.0991
|
| 592 |
+
Val PCK: 0.9915
|
| 593 |
+
Val IoU: 0.9425
|
| 594 |
+
No improvement for 12/20 epochs
|
| 595 |
+
Epoch 114: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0733]
|
| 596 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.44it/s]
|
| 597 |
+
|
| 598 |
+
Epoch 115/300
|
| 599 |
+
Train Loss: 0.0728
|
| 600 |
+
Val Loss: 0.0989
|
| 601 |
+
Val PCK: 0.9907
|
| 602 |
+
Val IoU: 0.9430
|
| 603 |
+
No improvement for 13/20 epochs
|
| 604 |
+
Epoch 115: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0741]
|
| 605 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.44it/s]
|
| 606 |
+
|
| 607 |
+
Epoch 116/300
|
| 608 |
+
Train Loss: 0.0726
|
| 609 |
+
Val Loss: 0.0991
|
| 610 |
+
Val PCK: 0.9915
|
| 611 |
+
Val IoU: 0.9425
|
| 612 |
+
No improvement for 14/20 epochs
|
| 613 |
+
Epoch 116: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0735]
|
| 614 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.43it/s]
|
| 615 |
+
|
| 616 |
+
Epoch 117/300
|
| 617 |
+
Train Loss: 0.0724
|
| 618 |
+
Val Loss: 0.1004
|
| 619 |
+
Val PCK: 0.9909
|
| 620 |
+
Val IoU: 0.9416
|
| 621 |
+
No improvement for 15/20 epochs
|
| 622 |
+
Epoch 117: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:52<00:00, 2.64it/s, loss=0.0732]
|
| 623 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.46it/s]
|
| 624 |
+
|
| 625 |
+
Epoch 118/300
|
| 626 |
+
Train Loss: 0.0723
|
| 627 |
+
Val Loss: 0.0978
|
| 628 |
+
Val PCK: 0.9920
|
| 629 |
+
Val IoU: 0.9430
|
| 630 |
+
Saved best model (val_loss=0.0978)
|
| 631 |
+
Epoch 118: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0722]
|
| 632 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.44it/s]
|
| 633 |
+
|
| 634 |
+
Epoch 119/300
|
| 635 |
+
Train Loss: 0.0721
|
| 636 |
+
Val Loss: 0.0998
|
| 637 |
+
Val PCK: 0.9910
|
| 638 |
+
Val IoU: 0.9422
|
| 639 |
+
No improvement for 1/20 epochs
|
| 640 |
+
Epoch 119: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:52<00:00, 2.64it/s, loss=0.0725]
|
| 641 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.46it/s]
|
| 642 |
+
|
| 643 |
+
Epoch 120/300
|
| 644 |
+
Train Loss: 0.0720
|
| 645 |
+
Val Loss: 0.0994
|
| 646 |
+
Val PCK: 0.9904
|
| 647 |
+
Val IoU: 0.9420
|
| 648 |
+
No improvement for 2/20 epochs
|
| 649 |
+
Epoch 120: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0724]
|
| 650 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.47it/s]
|
| 651 |
+
|
| 652 |
+
Epoch 121/300
|
| 653 |
+
Train Loss: 0.0722
|
| 654 |
+
Val Loss: 0.1002
|
| 655 |
+
Val PCK: 0.9916
|
| 656 |
+
Val IoU: 0.9422
|
| 657 |
+
No improvement for 3/20 epochs
|
| 658 |
+
Epoch 121: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0701]
|
| 659 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.45it/s]
|
| 660 |
+
|
| 661 |
+
Epoch 122/300
|
| 662 |
+
Train Loss: 0.0717
|
| 663 |
+
Val Loss: 0.0989
|
| 664 |
+
Val PCK: 0.9906
|
| 665 |
+
Val IoU: 0.9424
|
| 666 |
+
No improvement for 4/20 epochs
|
| 667 |
+
Epoch 122: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0696]
|
| 668 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.46it/s]
|
| 669 |
+
|
| 670 |
+
Epoch 123/300
|
| 671 |
+
Train Loss: 0.0720
|
| 672 |
+
Val Loss: 0.0997
|
| 673 |
+
Val PCK: 0.9911
|
| 674 |
+
Val IoU: 0.9424
|
| 675 |
+
No improvement for 5/20 epochs
|
| 676 |
+
Epoch 123: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0722]
|
| 677 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.45it/s]
|
| 678 |
+
|
| 679 |
+
Epoch 124/300
|
| 680 |
+
Train Loss: 0.0719
|
| 681 |
+
Val Loss: 0.0995
|
| 682 |
+
Val PCK: 0.9910
|
| 683 |
+
Val IoU: 0.9423
|
| 684 |
+
No improvement for 6/20 epochs
|
| 685 |
+
Epoch 124: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0716]
|
| 686 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.43it/s]
|
| 687 |
+
|
| 688 |
+
Epoch 125/300
|
| 689 |
+
Train Loss: 0.0716
|
| 690 |
+
Val Loss: 0.0988
|
| 691 |
+
Val PCK: 0.9916
|
| 692 |
+
Val IoU: 0.9427
|
| 693 |
+
No improvement for 7/20 epochs
|
| 694 |
+
Epoch 125: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:52<00:00, 2.64it/s, loss=0.071]
|
| 695 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.43it/s]
|
| 696 |
+
|
| 697 |
+
Epoch 126/300
|
| 698 |
+
Train Loss: 0.0717
|
| 699 |
+
Val Loss: 0.0996
|
| 700 |
+
Val PCK: 0.9909
|
| 701 |
+
Val IoU: 0.9422
|
| 702 |
+
No improvement for 8/20 epochs
|
| 703 |
+
Epoch 126: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:52<00:00, 2.64it/s, loss=0.0703]
|
| 704 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.42it/s]
|
| 705 |
+
|
| 706 |
+
Epoch 127/300
|
| 707 |
+
Train Loss: 0.0712
|
| 708 |
+
Val Loss: 0.0988
|
| 709 |
+
Val PCK: 0.9906
|
| 710 |
+
Val IoU: 0.9425
|
| 711 |
+
No improvement for 9/20 epochs
|
| 712 |
+
Epoch 127: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0729]
|
| 713 |
+
Validation: 100%|βββββββββββοΏ½οΏ½βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.43it/s]
|
| 714 |
+
|
| 715 |
+
Epoch 128/300
|
| 716 |
+
Train Loss: 0.0714
|
| 717 |
+
Val Loss: 0.0983
|
| 718 |
+
Val PCK: 0.9913
|
| 719 |
+
Val IoU: 0.9430
|
| 720 |
+
No improvement for 10/20 epochs
|
| 721 |
+
Epoch 128: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.07]
|
| 722 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.44it/s]
|
| 723 |
+
|
| 724 |
+
Epoch 129/300
|
| 725 |
+
Train Loss: 0.0712
|
| 726 |
+
Val Loss: 0.0993
|
| 727 |
+
Val PCK: 0.9904
|
| 728 |
+
Val IoU: 0.9423
|
| 729 |
+
No improvement for 11/20 epochs
|
| 730 |
+
Epoch 129: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0709]
|
| 731 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.41it/s]
|
| 732 |
+
|
| 733 |
+
Epoch 130/300
|
| 734 |
+
Train Loss: 0.0712
|
| 735 |
+
Val Loss: 0.0980
|
| 736 |
+
Val PCK: 0.9917
|
| 737 |
+
Val IoU: 0.9431
|
| 738 |
+
No improvement for 12/20 epochs
|
| 739 |
+
Epoch 130: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0716]
|
| 740 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.47it/s]
|
| 741 |
+
|
| 742 |
+
Epoch 131/300
|
| 743 |
+
Train Loss: 0.0711
|
| 744 |
+
Val Loss: 0.0992
|
| 745 |
+
Val PCK: 0.9909
|
| 746 |
+
Val IoU: 0.9426
|
| 747 |
+
No improvement for 13/20 epochs
|
| 748 |
+
Epoch 131: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0716]
|
| 749 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.47it/s]
|
| 750 |
+
|
| 751 |
+
Epoch 132/300
|
| 752 |
+
Train Loss: 0.0710
|
| 753 |
+
Val Loss: 0.0997
|
| 754 |
+
Val PCK: 0.9905
|
| 755 |
+
Val IoU: 0.9422
|
| 756 |
+
No improvement for 14/20 epochs
|
| 757 |
+
Epoch 132: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0713]
|
| 758 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.43it/s]
|
| 759 |
+
|
| 760 |
+
Epoch 133/300
|
| 761 |
+
Train Loss: 0.0710
|
| 762 |
+
Val Loss: 0.0994
|
| 763 |
+
Val PCK: 0.9908
|
| 764 |
+
Val IoU: 0.9424
|
| 765 |
+
No improvement for 15/20 epochs
|
| 766 |
+
Epoch 133: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.069]
|
| 767 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββοΏ½οΏ½ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.42it/s]
|
| 768 |
+
|
| 769 |
+
Epoch 134/300
|
| 770 |
+
Train Loss: 0.0709
|
| 771 |
+
Val Loss: 0.0993
|
| 772 |
+
Val PCK: 0.9913
|
| 773 |
+
Val IoU: 0.9423
|
| 774 |
+
No improvement for 16/20 epochs
|
| 775 |
+
Epoch 134: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0716]
|
| 776 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.43it/s]
|
| 777 |
+
|
| 778 |
+
Epoch 135/300
|
| 779 |
+
Train Loss: 0.0709
|
| 780 |
+
Val Loss: 0.0992
|
| 781 |
+
Val PCK: 0.9910
|
| 782 |
+
Val IoU: 0.9422
|
| 783 |
+
No improvement for 17/20 epochs
|
| 784 |
+
Epoch 135: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0716]
|
| 785 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.44it/s]
|
| 786 |
+
|
| 787 |
+
Epoch 136/300
|
| 788 |
+
Train Loss: 0.0708
|
| 789 |
+
Val Loss: 0.0986
|
| 790 |
+
Val PCK: 0.9900
|
| 791 |
+
Val IoU: 0.9427
|
| 792 |
+
No improvement for 18/20 epochs
|
| 793 |
+
Epoch 136: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0703]
|
| 794 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.48it/s]
|
| 795 |
+
|
| 796 |
+
Epoch 137/300
|
| 797 |
+
Train Loss: 0.0709
|
| 798 |
+
Val Loss: 0.0999
|
| 799 |
+
Val PCK: 0.9900
|
| 800 |
+
Val IoU: 0.9417
|
| 801 |
+
No improvement for 19/20 epochs
|
| 802 |
+
Epoch 137: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.071]
|
| 803 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.41it/s]
|
| 804 |
+
|
| 805 |
+
Epoch 138/300
|
| 806 |
+
Train Loss: 0.0704
|
| 807 |
+
Val Loss: 0.0977
|
| 808 |
+
Val PCK: 0.9909
|
| 809 |
+
Val IoU: 0.9433
|
| 810 |
+
Saved best model (val_loss=0.0977)
|
| 811 |
+
Epoch 138: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:55<00:00, 2.63it/s, loss=0.0702]
|
| 812 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.41it/s]
|
| 813 |
+
|
| 814 |
+
Epoch 139/300
|
| 815 |
+
Train Loss: 0.0705
|
| 816 |
+
Val Loss: 0.0989
|
| 817 |
+
Val PCK: 0.9908
|
| 818 |
+
Val IoU: 0.9426
|
| 819 |
+
No improvement for 1/20 epochs
|
| 820 |
+
Epoch 139: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:52<00:00, 2.64it/s, loss=0.0699]
|
| 821 |
+
Validation: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββοΏ½οΏ½ββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.42it/s]
|
| 822 |
+
|
| 823 |
+
Epoch 140/300
|
| 824 |
+
Train Loss: 0.0704
|
| 825 |
+
Val Loss: 0.0990
|
| 826 |
+
Val PCK: 0.9906
|
| 827 |
+
Val IoU: 0.9425
|
| 828 |
+
No improvement for 2/20 epochs
|
| 829 |
+
Epoch 140: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0694]
|
| 830 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:03<00:00, 7.38it/s]
|
| 831 |
+
|
| 832 |
+
Epoch 141/300
|
| 833 |
+
Train Loss: 0.0704
|
| 834 |
+
Val Loss: 0.0977
|
| 835 |
+
Val PCK: 0.9916
|
| 836 |
+
Val IoU: 0.9430
|
| 837 |
+
No improvement for 3/20 epochs
|
| 838 |
+
Epoch 141: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.069]
|
| 839 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.41it/s]
|
| 840 |
+
|
| 841 |
+
Epoch 142/300
|
| 842 |
+
Train Loss: 0.0704
|
| 843 |
+
Val Loss: 0.1005
|
| 844 |
+
Val PCK: 0.9905
|
| 845 |
+
Val IoU: 0.9416
|
| 846 |
+
No improvement for 4/20 epochs
|
| 847 |
+
Epoch 142: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0688]
|
| 848 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:03<00:00, 7.39it/s]
|
| 849 |
+
|
| 850 |
+
Epoch 143/300
|
| 851 |
+
Train Loss: 0.0701
|
| 852 |
+
Val Loss: 0.0989
|
| 853 |
+
Val PCK: 0.9909
|
| 854 |
+
Val IoU: 0.9428
|
| 855 |
+
No improvement for 5/20 epochs
|
| 856 |
+
Epoch 143: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0678]
|
| 857 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.41it/s]
|
| 858 |
+
|
| 859 |
+
Epoch 144/300
|
| 860 |
+
Train Loss: 0.0700
|
| 861 |
+
Val Loss: 0.0998
|
| 862 |
+
Val PCK: 0.9901
|
| 863 |
+
Val IoU: 0.9425
|
| 864 |
+
No improvement for 6/20 epochs
|
| 865 |
+
Epoch 144: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0724]
|
| 866 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:03<00:00, 7.38it/s]
|
| 867 |
+
|
| 868 |
+
Epoch 145/300
|
| 869 |
+
Train Loss: 0.0703
|
| 870 |
+
Val Loss: 0.0992
|
| 871 |
+
Val PCK: 0.9910
|
| 872 |
+
Val IoU: 0.9425
|
| 873 |
+
No improvement for 7/20 epochs
|
| 874 |
+
Epoch 145: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.65it/s, loss=0.0696]
|
| 875 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββοΏ½οΏ½οΏ½βββββ| 466/466 [01:02<00:00, 7.40it/s]
|
| 876 |
+
|
| 877 |
+
Epoch 146/300
|
| 878 |
+
Train Loss: 0.0699
|
| 879 |
+
Val Loss: 0.0993
|
| 880 |
+
Val PCK: 0.9919
|
| 881 |
+
Val IoU: 0.9422
|
| 882 |
+
No improvement for 8/20 epochs
|
| 883 |
+
Epoch 146: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0695]
|
| 884 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.41it/s]
|
| 885 |
+
|
| 886 |
+
Epoch 147/300
|
| 887 |
+
Train Loss: 0.0697
|
| 888 |
+
Val Loss: 0.0981
|
| 889 |
+
Val PCK: 0.9926
|
| 890 |
+
Val IoU: 0.9427
|
| 891 |
+
No improvement for 9/20 epochs
|
| 892 |
+
Epoch 147: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0677]
|
| 893 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.45it/s]
|
| 894 |
+
|
| 895 |
+
Epoch 148/300
|
| 896 |
+
Train Loss: 0.0697
|
| 897 |
+
Val Loss: 0.1006
|
| 898 |
+
Val PCK: 0.9901
|
| 899 |
+
Val IoU: 0.9420
|
| 900 |
+
No improvement for 10/20 epochs
|
| 901 |
+
Epoch 148: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0678]
|
| 902 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:03<00:00, 7.39it/s]
|
| 903 |
+
|
| 904 |
+
Epoch 149/300
|
| 905 |
+
Train Loss: 0.0696
|
| 906 |
+
Val Loss: 0.0984
|
| 907 |
+
Val PCK: 0.9907
|
| 908 |
+
Val IoU: 0.9431
|
| 909 |
+
No improvement for 11/20 epochs
|
| 910 |
+
Epoch 149: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0697]
|
| 911 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.42it/s]
|
| 912 |
+
|
| 913 |
+
Epoch 150/300
|
| 914 |
+
Train Loss: 0.0698
|
| 915 |
+
Val Loss: 0.0984
|
| 916 |
+
Val PCK: 0.9916
|
| 917 |
+
Val IoU: 0.9427
|
| 918 |
+
No improvement for 12/20 epochs
|
| 919 |
+
Epoch 150: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0694]
|
| 920 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.45it/s]
|
| 921 |
+
|
| 922 |
+
Epoch 151/300
|
| 923 |
+
Train Loss: 0.0696
|
| 924 |
+
Val Loss: 0.0982
|
| 925 |
+
Val PCK: 0.9915
|
| 926 |
+
Val IoU: 0.9430
|
| 927 |
+
No improvement for 13/20 epochs
|
| 928 |
+
Epoch 151: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0716]
|
| 929 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.42it/s]
|
| 930 |
+
|
| 931 |
+
Epoch 152/300
|
| 932 |
+
Train Loss: 0.0696
|
| 933 |
+
Val Loss: 0.0989
|
| 934 |
+
Val PCK: 0.9910
|
| 935 |
+
Val IoU: 0.9426
|
| 936 |
+
No improvement for 14/20 epochs
|
| 937 |
+
Epoch 152: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0682]
|
| 938 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.41it/s]
|
| 939 |
+
|
| 940 |
+
Epoch 153/300
|
| 941 |
+
Train Loss: 0.0691
|
| 942 |
+
Val Loss: 0.0979
|
| 943 |
+
Val PCK: 0.9912
|
| 944 |
+
Val IoU: 0.9431
|
| 945 |
+
No improvement for 15/20 epochs
|
| 946 |
+
Epoch 153: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.068]
|
| 947 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.42it/s]
|
| 948 |
+
|
| 949 |
+
Epoch 154/300
|
| 950 |
+
Train Loss: 0.0693
|
| 951 |
+
Val Loss: 0.1005
|
| 952 |
+
Val PCK: 0.9897
|
| 953 |
+
Val IoU: 0.9415
|
| 954 |
+
No improvement for 16/20 epochs
|
| 955 |
+
Epoch 154: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [15:00<00:00, 2.62it/s, loss=0.0708]
|
| 956 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.41it/s]
|
| 957 |
+
|
| 958 |
+
Epoch 155/300
|
| 959 |
+
Train Loss: 0.0694
|
| 960 |
+
Val Loss: 0.0981
|
| 961 |
+
Val PCK: 0.9912
|
| 962 |
+
Val IoU: 0.9430
|
| 963 |
+
No improvement for 17/20 epochs
|
| 964 |
+
Epoch 155: 100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.068]
|
| 965 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.41it/s]
|
| 966 |
+
|
| 967 |
+
Epoch 156/300
|
| 968 |
+
Train Loss: 0.0692
|
| 969 |
+
Val Loss: 0.0993
|
| 970 |
+
Val PCK: 0.9904
|
| 971 |
+
Val IoU: 0.9420
|
| 972 |
+
No improvement for 18/20 epochs
|
| 973 |
+
Epoch 156: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0708]
|
| 974 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:02<00:00, 7.43it/s]
|
| 975 |
+
|
| 976 |
+
Epoch 157/300
|
| 977 |
+
Train Loss: 0.0692
|
| 978 |
+
Val Loss: 0.0985
|
| 979 |
+
Val PCK: 0.9906
|
| 980 |
+
Val IoU: 0.9425
|
| 981 |
+
No improvement for 19/20 epochs
|
| 982 |
+
Epoch 157: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2357/2357 [14:51<00:00, 2.64it/s, loss=0.0712]
|
| 983 |
+
Validation: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 466/466 [01:03<00:00, 7.37it/s]
|
| 984 |
+
|
| 985 |
+
Epoch 158/300
|
| 986 |
+
Train Loss: 0.0691
|
| 987 |
+
Val Loss: 0.0980
|
| 988 |
+
Val PCK: 0.9914
|
| 989 |
+
Val IoU: 0.9430
|
| 990 |
+
No improvement for 20/20 epochs
|
| 991 |
+
|
| 992 |
+
Early stopping triggered after 158 epochs!
|
| 993 |
+
wandb:
|
| 994 |
+
wandb: Run history:
|
| 995 |
+
wandb: batch ββββ
β
β
ββββ
ββ
βββββββ
βββ
β
βββββββ
βββ
βββ
βββ
β
|
| 996 |
+
wandb: epoch ββββββββββββββββββββββββββ
β
β
β
β
ββββββββββ
|
| 997 |
+
wandb: lr βββββββββββββββββββββ
β
β
β
ββββββββββββββββ
|
| 998 |
+
wandb: model/parameters β
|
| 999 |
+
wandb: train/epoch_loss ββββββββββββββββββββββββββββββββββββββββ
|
| 1000 |
+
wandb: train/l_angle ββ
ββββββββββββββββββββββββββββββββββββββ
|
| 1001 |
+
wandb: train/l_diag ββββ
βββββββββββ
βββββββββ
ββββββββββββββββ
|
| 1002 |
+
wandb: train/l_edge ββββββββββββ
ββββββββββββββββββββββββββββ
|
| 1003 |
+
wandb: train/l_hm ββββββββββββββββββββββββββββββββββββββββ
|
| 1004 |
+
wandb: train/l_kpt ββββββββββββββββββββββββββββββββββββββββ
|
| 1005 |
+
wandb: +5 ...
|
| 1006 |
+
wandb:
|
| 1007 |
+
wandb: Run summary:
|
| 1008 |
+
wandb: batch 2300
|
| 1009 |
+
wandb: best_epoch 138
|
| 1010 |
+
wandb: best_val_loss 0.09766
|
| 1011 |
+
wandb: epoch 158
|
| 1012 |
+
wandb: lr 2e-05
|
| 1013 |
+
wandb: model/parameters 1238856
|
| 1014 |
+
wandb: train/epoch_loss 0.06909
|
| 1015 |
+
wandb: train/l_angle 0.01886
|
| 1016 |
+
wandb: train/l_diag 0.00304
|
| 1017 |
+
wandb: train/l_edge 0.00351
|
| 1018 |
+
wandb: +7 ...
|
| 1019 |
+
wandb:
|
| 1020 |
+
wandb: View run CourtKeyNet_01/21_22:34 at: https://wandb.ai/adithyanraj03/courtkeynet/runs/wijqjzd9
|
| 1021 |
+
wandb: View project at: https://wandb.ai/adithyanraj03/courtkeynet
|
| 1022 |
+
wandb: Synced 5 W&B file(s), 159 media file(s), 2 artifact file(s) and 0 other file(s)
|
| 1023 |
+
wandb: Find logs at: .\wandb\run-20260121_223455-wijqjzd9\logs
|
| 1024 |
+
|
| 1025 |
+
Training complete! Best val loss: 0.0977
|
| 1026 |
+
Weights saved to: runs\courtkeynet\exp_20260121_223500_wijqjzd9
|