Image Classification
Transformers
TensorBoard
Safetensors
mobilenet_v2
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("A2H0H0R1/mobilenet_v2_1.0_224-plant-disease2")
model = AutoModelForImageClassification.from_pretrained("A2H0H0R1/mobilenet_v2_1.0_224-plant-disease2", device_map="auto")Quick Links
mobilenet_v2_1.0_224-plant-disease2
This model is a fine-tuned version of google/mobilenet_v2_1.0_224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1510
- Accuracy: 0.9639
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 100
- eval_batch_size: 100
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 400
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.2171 | 1.0 | 158 | 1.0595 | 0.8188 |
| 0.4082 | 2.0 | 316 | 0.3154 | 0.9387 |
| 0.295 | 3.0 | 474 | 0.2191 | 0.9555 |
| 0.2266 | 4.0 | 633 | 0.1747 | 0.9595 |
| 0.2168 | 5.0 | 791 | 0.2135 | 0.9499 |
| 0.2091 | 5.99 | 948 | 0.1510 | 0.9639 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for A2H0H0R1/mobilenet_v2_1.0_224-plant-disease2
Base model
google/mobilenet_v2_1.0_224Dataset used to train A2H0H0R1/mobilenet_v2_1.0_224-plant-disease2
Space using A2H0H0R1/mobilenet_v2_1.0_224-plant-disease2 1
Evaluation results
- Accuracy on imagefolderself-reported0.964
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="A2H0H0R1/mobilenet_v2_1.0_224-plant-disease2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")