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: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9300411343574524

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: 0.3349
• Accuracy: 0.9300

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
No log	1.0	8	0.4142	0.9198
0.0016	2.0	16	0.3421	0.9218
0.0043	3.0	24	0.3281	0.9095
0.0034	4.0	32	0.2599	0.9362
0.0025	5.0	40	0.3839	0.9156
0.0025	6.0	48	0.3061	0.9321
0.0051	7.0	56	0.4106	0.9239
0.0041	8.0	64	0.4680	0.9095
0.0073	9.0	72	0.4199	0.9074
0.0037	10.0	80	0.3102	0.9300
0.0037	11.0	88	0.4335	0.9136
0.0147	12.0	96	0.5457	0.9033
0.0269	13.0	104	0.3233	0.9300
0.0032	14.0	112	0.4310	0.9115
0.0022	15.0	120	0.3929	0.9012
0.0022	16.0	128	0.3390	0.9259
0.0106	17.0	136	0.3798	0.9136
0.0229	18.0	144	0.3951	0.9115
0.0163	19.0	152	0.2882	0.9403
0.0025	20.0	160	0.2788	0.9321
0.0025	21.0	168	0.3919	0.9136
0.0163	22.0	176	0.4097	0.9342
0.0118	23.0	184	0.3226	0.9362
0.0028	24.0	192	0.4010	0.9198
0.0112	25.0	200	0.3082	0.9527
0.0112	26.0	208	0.3728	0.9300
0.0211	27.0	216	0.3696	0.9362
0.0018	28.0	224	0.3142	0.9259
0.0012	29.0	232	0.3075	0.9300
0.0061	30.0	240	0.3983	0.9300
0.0061	31.0	248	0.3352	0.9362
0.0165	32.0	256	0.3700	0.9342
0.0025	33.0	264	0.3901	0.9177
0.0207	34.0	272	0.3874	0.9342
0.0094	35.0	280	0.3643	0.9280
0.0094	36.0	288	0.2842	0.9321
0.0182	37.0	296	0.3285	0.9342
0.0031	38.0	304	0.3639	0.9300
0.0246	39.0	312	0.3357	0.9342
0.0203	40.0	320	0.3219	0.9259
0.0203	41.0	328	0.3186	0.9342
0.0154	42.0	336	0.3418	0.9300
0.0161	43.0	344	0.3163	0.9424
0.0097	44.0	352	0.3202	0.9383
0.0126	45.0	360	0.3835	0.9321
0.0126	46.0	368	0.3396	0.9403
0.0043	47.0	376	0.3481	0.9383
0.0369	48.0	384	0.3826	0.9321
0.0057	49.0	392	0.3538	0.9342
0.0355	50.0	400	0.3349	0.9300

Framework versions

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