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swadhindas324/
vit-resnet-mistral-UCM-with-6-captioning

Name: vit-resnet-mistral-UCM-with-6-captioning
Availability: InStock

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vit-resnet-mistral-UCM-with-6-captioning

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

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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: 0.1
num_epochs: 128
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Accuracy	Bleu-1	Bleu-2	Bleu-3	Bleu-4	Meteor	Rouge-l	Cider
No log	1.0	148	0.6525	72.44	0.4161	0.2862	0.2100	0.1579	0.3185	0.3524	0.3543
No log	2.0	296	0.4699	72.04	0.3006	0.2063	0.1356	0.0928	0.2190	0.2487	0.3328
No log	3.0	444	0.4241	69.49	0.6099	0.5201	0.4547	0.4062	0.5566	0.5662	1.8487
No log	4.0	592	0.3574	73.82	0.8252	0.7590	0.7044	0.6550	0.7922	0.7774	3.1653
No log	5.0	740	0.3450	74.32	0.8303	0.7708	0.7234	0.6839	0.8120	0.7997	3.3390
No log	6.0	888	0.3526	73.73	0.8354	0.7809	0.7320	0.6873	0.8240	0.8097	3.3430
0.4526	7.0	1036	0.3386	75.3	0.8675	0.8149	0.7701	0.7265	0.8324	0.8221	3.5520
0.4526	8.0	1184	0.3406	74.36	0.8483	0.7916	0.7413	0.6939	0.8240	0.8143	3.4696
0.4526	9.0	1332	0.3469	75.03	0.8722	0.8238	0.7791	0.7372	0.8516	0.8399	3.6168
0.4526	10.0	1480	0.3508	74.49	0.8306	0.7708	0.7189	0.6695	0.8017	0.7883	3.3304
0.4526	11.0	1628	0.3615	73.88	0.8335	0.7750	0.7273	0.6856	0.8285	0.8037	3.4000
0.4526	12.0	1776	0.3587	74.48	0.8575	0.8056	0.7610	0.7200	0.8391	0.8285	3.5049
0.4526	13.0	1924	0.3813	73.39	0.8329	0.7704	0.7217	0.6773	0.8120	0.7901	3.3758
0.1651	14.0	2072	0.3911	74.63	0.8251	0.7637	0.7121	0.6630	0.7868	0.7718	3.1622
0.1651	15.0	2220	0.3852	75.56	0.8487	0.7913	0.7427	0.6978	0.8102	0.8043	3.5665
0.1651	16.0	2368	0.3852	74.65	0.8612	0.8114	0.7680	0.7275	0.8469	0.8302	3.6155
0.1651	17.0	2516	0.3938	74.52	0.8553	0.7969	0.7477	0.7016	0.8381	0.8160	3.4153
0.1651	18.0	2664	0.3973	74.71	0.8535	0.7968	0.7480	0.7030	0.8326	0.8204	3.4378
0.1651	19.0	2812	0.4134	74.75	0.8578	0.8009	0.7539	0.7105	0.8301	0.8174	3.4426