Adaptive Edge Plant Model
This repository hosts the lightweight Edge Classifier (MobileNetV4-Conv-Medium) for the Adaptive Edge-Cloud Plant Disease Diagnosis framework. The model features: Evidential Deep Learning (EDL) Head to calculate...
Fuente del modelo
- Tipo
- Checkpoint
- Tamaño del archivo
- 25,0–25,1 MB
- Versión
- vRevision 3ea6e11
- Actualizado
- 3 oct 2026
- Publicador
- @Arko007
- Descargas
- 0
- Fuente
- Hugging Face
Descripción de la fuente
MobileNetV4 Edge Plant Classifier (with EDL & GRL)
This repository hosts the lightweight Edge Classifier (MobileNetV4-Conv-Medium) for the Adaptive Edge-Cloud Plant Disease Diagnosis framework. The model features:
- Evidential Deep Learning (EDL) Head to calculate epistemic uncertainty (vacuity $u$) in a single forward pass.
- Gradient Reversal Layer (GRL) Domain Adaptation to reconcile lab-to-field domain shifts.
- Conformal Temperature Scaling configuration parameters to provide distribution-free confidence guarantees.
1. Mathematical and Framework Documentation
A complete mathematical report detailing the Evidential Deep Learning (EDL), Unsupervised Domain Adaptation (UDA), and Conformal Calibration models is compiled as a PDF and available in this repository: 👉 Read the Mathematical Report (PDF)
2. Model Architecture and Training Details
- Model Type: MobileNetV4-Conv-Medium with Evidential Classification Head & Domain Discriminator
- Number of Classes: 88 (spanning various crop types including Apple, Tomato, Wheat, Soybean, Sugarcane, Tea, etc.)
- Optimization Strategy: Trained from scratch with all layers unfrozen to adapt features to the plant classification domain.
- Optimizer: AdamW (Learning Rate: $10^{-3}$, Weight Decay: $10^{-3}$)
- Loss Function: Multi-Task Evidential Loss ($\mathcal{L}{mse} + \lambda_t \mathcal{L}{kl}$) with auxiliary Cross-Entropy ($\gamma = 0.1$) to prevent gradient vanishing.
- Domain Adaptation: Unsupervised Domain Adaptation (UDA) with Gradient Reversal Layer (GRL) mapping source (lab) to target (field) domains.
Training & Validation Loss Curves
Edge Loss Curve
Accuracy & Validation Vacuity Dynamics
Edge Accuracy Curve
3. Convergence Metrics Summary
The model was trained for 10 epochs on Kaggle GPU environments using a stratified dataset split (30,103 training images, 5,347 validation images):
| Epoch | Train Cls Loss | Train Dom Loss | Train Accuracy (%) | Validation Loss | Validation Accuracy (%) | Val Avg Vacuity ($u$) |
|---|---|---|---|---|---|---|
| 1 | 2.0984 | 0.0245 | 67.60% | 1.5441 | 81.49% | 0.9606 |
| 2 | 1.5398 | 0.0119 | 81.84% | 1.5426 | 82.20% | 0.9557 |
| 3 | 1.4037 | 0.0110 | 85.19% | 1.4969 | 83.47% | 0.9433 |
| 4 | 1.3306 | 0.0118 | 86.85% | 1.2157 | 88.87% | 0.8990 |
| 5 | 1.2750 | 0.0122 | 87.91% | 1.2090 | 88.82% | 0.9011 |
| 6 | 1.2260 | 0.0201 | 88.87% | 1.1313 | 90.27% | 0.8808 |
| 7 | 1.1976 | 0.0199 | 89.49% | 1.0826 | 91.61% | 0.8664 |
| 8 | 1.1430 | 0.0199 | 90.36% | 1.1093 | 90.55% | 0.8598 |
| 9 | 1.1305 | 0.0212 | 90.40% | 0.9965 | 92.23% | 0.8219 |
| 10 | 1.1171 | 0.0213 | 90.99% | 1.0341 | 91.97% | 0.8321 |
Note: The best-performing checkpoint was recorded at Epoch 9 with 92.23% validation accuracy and the lowest average evidential uncertainty (vacuity = 0.8219).
4. Collaborative Gating Mechanism
The Edge classifier is designed to run locally on resource-constrained devices. It makes predictions and computes the epistemic uncertainty (vacuity $u$) in a single forward pass.
- If vacuity exceeds the threshold ($u > au_{vac}$) OR maximum calibrated conformal confidence is below the threshold ($p_{max} < au_{conf}$), the diagnostic request is offloaded to the heavy cloud model (
ConvNeXt-Large).
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 3 oct
Artefactos del modelo
12 artefactos
Artefactos del modelo
12 artefactoscheckpoints/mobilenetv4_edge_best.pth
pth · 25,1 MB · SHA-256 42757262e578…7343 · Hugging Face
Descargarcheckpoints/mobilenetv4_edge_epoch_1.pth
pth · 25,1 MB · SHA-256 bbbead22d93a…f930 · Hugging Face
Descargarcheckpoints/mobilenetv4_edge_epoch_10.pth
pth · 25,1 MB · SHA-256 2f0d0e5497a0…d483 · Hugging Face
Descargarcheckpoints/mobilenetv4_edge_epoch_2.pth
pth · 25,1 MB · SHA-256 88efa5fe5354…57d1 · Hugging Face
Descargarcheckpoints/mobilenetv4_edge_epoch_3.pth
pth · 25,1 MB · SHA-256 77ca2823a3de…34fd · Hugging Face
Descargarcheckpoints/mobilenetv4_edge_epoch_4.pth
pth · 25,1 MB · SHA-256 e1d5b2012d02…3c2a · Hugging Face
Descargarcheckpoints/mobilenetv4_edge_epoch_5.pth
pth · 25,1 MB · SHA-256 750b8fefcf9e…e315 · Hugging Face
Descargarcheckpoints/mobilenetv4_edge_epoch_6.pth
pth · 25,1 MB · SHA-256 5d8f97b1ac1e…4174 · Hugging Face
Descargarcheckpoints/mobilenetv4_edge_epoch_7.pth
pth · 25,1 MB · SHA-256 1dd2664e68cc…6d05 · Hugging Face
Descargarcheckpoints/mobilenetv4_edge_epoch_8.pth
pth · 25,1 MB · SHA-256 15112bc467fb…a22c · Hugging Face
Descargarcheckpoints/mobilenetv4_edge_epoch_9.pth
pth · 25,1 MB · SHA-256 42757262e578…7343 · Hugging Face
Descargarmobilenetv4_edge_best.safetensors
safetensors · 25,0 MB · SHA-256 cd2121b8cf06…55b9 · Hugging Face
DescargarExtractos de fuentes
3 extractos
Extractos de fuentes
3 extractos--- language: - en license: mit tags: - agriculture - plant-pathology - mobilenetv4 - evidential-deep-learning - domain-adaptation - edge-cloud metrics: - accuracy pipeline_tag: image-classification model-index: - name: mobilenetv4_edge_best results: - task: type: image-classification name: Image Classification dataset: name: Plant Disease Classification Merged Dataset type: plant-disease-classification-merged-dataset metrics: - type: accuracy value: 92.23 name: Validation Accuracy --- # MobileNetV4 Edge Plant Classifier (with EDL & GRL) This repository hosts the lightweight **Edge Classifier (MobileNetV4-Conv-Medium)** for the *Adaptive Edge-Cloud Plant Disease Diagnosis* framework. The model features: 1. **Evidential Deep Learning (EDL) Head** to calculate epistemic uncertainty (vacuity $u$) in a single forward pass. 2. **Gradient Reversal Layer (GRL) Domain Adaptation** to reconcile lab-to-field domain shifts. 3. **Conformal Temperature Scaling** configuration parameters to provide distribution-free confidence guarantees. ## 1. Mathematical and Framework Documentation A complete mathematical report detailing the Evidential Deep Learning (EDL), Unsupervised Domain Adaptation (UDA), and Conformal Calibration models is compiled as a PDF and available in this repository: 👉 **[Read the Mathematical Report (PDF)](https://huggingface.co/Arko007/adaptive-edge-plant-model/blob/main/model_report.pdf)** --- ## 2. Model Architecture and Training Details - **Model Type**: MobileNetV4-Conv-Medium with Evidential Classification Head & Domain Discriminator - **Number of Classes**: 88 (spanning various crop types including Apple, Tomato, Wheat, Soybean, Sugarcane, Tea, etc.) - **Optimization Strategy**: Trained from scratch with all layers unfrozen to adapt features to the plant classification domain. - **Optimizer**: AdamW (Learning Rate: $10^{-3}$, Weight Decay: $10^{-3}$) - **Loss Function**: Multi-Task Evidential Loss ($\mathcal{L}_{mse} + \lambda_t \mathcal{L}_{kl}$) with auxiliary Cross-Entropy ($\gamma = 0.1$) to prevent gradient vanishing. - **Domain Adaptation**: Unsupervised Domain Adaptation (UDA) with Gradient Reversal Layer (GRL) mapping source (lab) to target (field) domains. ### Training & Validation Loss Curves  ### Accuracy &...
Source context: 0 downloads · 0 likes · Pipeline image-classification · Repo Arko007/adaptive-edge-plant-model
Source context: 0 downloads · 0 likes · Pipeline image-classification · Repo Arko007/adaptive-edge-plant-model
