swin-Mistral-RSICD-without-captioning

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.0422
• Accuracy: 78.97
• Bleu-1: 0.6446
• Bleu-2: 0.4755
• Bleu-3: 0.3697
• Bleu-4: 0.2980
• Meteor: 0.4785
• Rouge-l: 0.4851
• Cider: 0.7973

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: 0.0001
• train_batch_size: 64
• eval_batch_size: 64
• seed: 50
• 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_steps: 1024
• num_epochs: 128
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Bleu-1	Bleu-2	Bleu-3	Bleu-4	Meteor	Rouge-l	Cider
No log	1.0	768	1.2315	78.69	0.6486	0.4711	0.3607	0.2865	0.4869	0.4876	0.7916
1.0998	2.0	1536	1.3782	78.57	0.6544	0.4871	0.3823	0.3102	0.4958	0.4946	0.8501
0.6008	3.0	2304	1.5682	78.73	0.6506	0.4740	0.3656	0.2930	0.4871	0.4882	0.8296
0.4133	4.0	3072	1.7126	78.65	0.6489	0.4727	0.3677	0.2974	0.4891	0.4905	0.8388
0.4133	5.0	3840	1.8513	78.64	0.6413	0.4682	0.3605	0.2891	0.4772	0.4832	0.8148
0.3178	6.0	4608	1.9415	78.73	0.6392	0.4684	0.3635	0.2937	0.4856	0.4836	0.8231
0.2793	7.0	5376	2.0422	78.97	0.6446	0.4755	0.3697	0.2980	0.4785	0.4851	0.7973

Framework versions

• Transformers 5.12.1
• Pytorch 2.12.0+cu130
• Datasets 5.0.0
• Tokenizers 0.22.2