README.md

metadata

library_name: transformers
license: other
base_model: google/mobilenet_v2_1.0_224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: mobilenet_v2_1.0_224-finetuned-plantdisease
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9781976744186046

mobilenet_v2_1.0_224-finetuned-plantdisease

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: 0.0841
• Accuracy: 0.9782

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: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.7982	0.9983	145	1.9825	0.4036
0.6137	1.9966	290	1.1130	0.6415
0.4176	2.9948	435	0.4887	0.8469
0.3107	4.0	581	0.3414	0.8944
0.2255	4.9983	726	0.2732	0.9123
0.1833	5.9966	871	0.7462	0.7582
0.2062	6.9948	1016	0.3771	0.8803
0.1657	8.0	1162	0.4718	0.8542
0.1427	8.9983	1307	0.4902	0.8474
0.1598	9.9966	1452	0.2229	0.9273
0.1504	10.9948	1597	0.3021	0.8973
0.1456	12.0	1743	0.2422	0.9225
0.119	12.9983	1888	0.2836	0.9021
0.114	13.9966	2033	0.2038	0.9293
0.1378	14.9948	2178	0.2173	0.9239
0.1249	16.0	2324	0.2467	0.9186
0.1504	16.9983	2469	0.2322	0.9254
0.0972	17.9966	2614	0.0841	0.9782
0.1293	18.9948	2759	0.1512	0.9467
0.1072	19.9656	2900	0.1663	0.9448

Framework versions

• Transformers 4.44.2
• Pytorch 2.5.0+cu121
• Datasets 3.1.0
• Tokenizers 0.19.1