resnet50_rvl-cdip – AI Model by jordyvl | AlphaNeural AI

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jordyvl/
resnet50_rvl-cdip

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image-classification

generated_from_trainer

microsoft/resnet-50

autotrain_compatible

endpoints_compatible

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resnet50_rvl-cdip

This model is a fine-tuned version of microsoft/resnet-50 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8368
Accuracy: 0.7503
Brier Loss: 0.3458
Nll: 3.2289
F1 Micro: 0.7503
F1 Macro: 0.5224
Ece: 0.0166
Aurc: 0.0739

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: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 1024
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Brier Loss	Nll	F1 Micro	F1 Macro	Ece	Aurc
No log	1.0	312	3.9686	0.1994	0.9069	6.9377	0.1994	0.0036	0.0434	0.6332
5.3078	2.0	625	1.5040	0.5644	0.5696	4.9767	0.5644	0.0480	0.0330	0.2052
5.3078	3.0	937	1.1500	0.6602	0.4588	4.0574	0.6602	0.1527	0.0193	0.1309
1.3983	4.0	1250	1.0174	0.6961	0.4132	3.6856	0.6961	0.2658	0.0184	0.1053
1.0466	5.0	1562	0.9439	0.7167	0.3862	3.5182	0.7167	0.3477	0.0150	0.0921
1.0466	6.0	1875	0.9042	0.7302	0.3717	3.3972	0.7302	0.3345	0.0160	0.0854
0.9333	7.0	2187	0.8713	0.7395	0.3593	3.3567	0.7395	0.4236	0.0162	0.0801
0.8757	8.0	2500	0.8550	0.7444	0.3531	3.2398	0.7444	0.4113	0.0150	0.0772
0.8757	9.0	2812	0.8389	0.7487	0.3468	3.1800	0.7487	0.4613	0.0149	0.0745
0.8509	9.98	3120	0.8368	0.7503	0.3458	3.2289	0.7503	0.5224	0.0166	0.0739

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.2.0.dev20231112+cu118
Datasets 2.14.5
Tokenizers 0.14.1