README.md

metadata

library_name: transformers
license: apache-2.0
base_model: Lalith47/custom-cloud-model
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: custom-cloud-model
    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: 0.689587414264679

custom-cloud-model

This model is a fine-tuned version of Lalith47/custom-cloud-model on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 1.1373
• Accuracy: 0.6896

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.273	1.0	32	2.1875	0.1631
1.8349	2.0	64	1.7155	0.4479
1.4566	3.0	96	1.3455	0.5717
1.1832	4.0	128	1.1565	0.5855
1.0294	5.0	160	1.0912	0.6365
0.9185	6.0	192	1.0261	0.6523
0.7852	7.0	224	0.9980	0.6405
0.7867	8.0	256	0.9890	0.6306
0.6186	9.0	288	0.9861	0.6582
0.6408	10.0	320	0.9740	0.6523
0.5585	11.0	352	0.9828	0.6621
0.5316	12.0	384	0.9386	0.6739
0.4884	13.0	416	0.9325	0.6346
0.466	14.0	448	0.9182	0.6739
0.4018	15.0	480	0.9588	0.6660
0.3776	16.0	512	0.9305	0.6778
0.3858	17.0	544	0.9876	0.6582
0.3312	18.0	576	1.0370	0.6287
0.3323	19.0	608	0.9705	0.6817
0.2896	20.0	640	0.9784	0.6876
0.2703	21.0	672	0.9497	0.6916
0.2521	22.0	704	1.0588	0.6601
0.2079	23.0	736	1.0287	0.6582
0.2371	24.0	768	0.9888	0.6739
0.2604	25.0	800	1.0015	0.6621
0.1952	26.0	832	1.0272	0.6837
0.2373	27.0	864	1.0004	0.6778
0.2195	28.0	896	1.0871	0.6601
0.198	29.0	928	1.0482	0.6817
0.1681	30.0	960	1.0531	0.6798
0.219	31.0	992	1.0627	0.6699
0.1801	32.0	1024	1.0884	0.6582
0.2065	33.0	1056	1.1099	0.6660
0.1526	34.0	1088	1.0921	0.6582
0.1632	35.0	1120	1.0851	0.6817
0.1548	36.0	1152	1.1042	0.6758
0.1712	37.0	1184	1.1042	0.6719
0.1393	38.0	1216	1.1022	0.6660
0.1487	39.0	1248	1.1125	0.6817
0.1425	40.0	1280	1.0962	0.6876
0.1521	41.0	1312	1.0355	0.6857
0.115	42.0	1344	1.1091	0.6562
0.1353	43.0	1376	1.1370	0.6817
0.1542	44.0	1408	1.1130	0.6798
0.1399	45.0	1440	1.1029	0.6876
0.0943	46.0	1472	1.1108	0.6876
0.1367	47.0	1504	1.0329	0.6974
0.1559	48.0	1536	1.0935	0.6719
0.148	49.0	1568	1.0697	0.6896
0.1013	50.0	1600	1.1373	0.6896

Framework versions

• Transformers 4.57.1
• Pytorch 2.9.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.1