vit-base-patch32-224-in21-leicester_binary Open Source Model - Precise Binary Classification for Efficient Image Categorization

Vit Base Patch32 224 In21 Leicester Binary

Developed by davanstrien

A binary image classification model based on the Google Vision Transformer (ViT) architecture, fine-tuned on a specific dataset to achieve high-precision classification

Image Classification

Transformers

Open Source License:Apache-2.0 #High-precision image classification #ViT architecture #Dedicated to binary classification

Downloads 15

Release Time : 12/6/2022

Model Overview

This is a vision Transformer model based on the ViT architecture, specifically fine-tuned for binary classification tasks and performs excellently on the evaluation set (F1 score of 0.9873).

Model Features

High-precision classification

Achieves an F1 score of 0.9873 on the evaluation set, showing excellent performance

Based on ViT architecture

Adopts the Vision Transformer architecture and uses the self-attention mechanism to process images

Efficient fine-tuning

Fine-tunes based on a pre-trained model, saving training resources

Model Capabilities

Image classification

Binary classification task processing

Visual feature extraction

Use Cases

Medical image analysis

Lesion detection

Used to identify specific lesion features in medical images

Industrial quality inspection

Defective product detection

Identifies defective products on the production line

🚀 vit-base-patch32-224-in21-leicester_binary

This model is a fine - tuned version of google/vit-base-patch32-224-in21k for image classification tasks, achieving high F1 scores on the evaluation set.

🚀 Quick Start

This model is a fine - tuned version of google/vit-base-patch32-224-in21k on the davanstrien/leicester_loaded_annotations_binary dataset. It achieves the following results on the evaluation set:

Loss: 0.0628
F1: 0.9873

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e - 05
train_batch_size: 64
eval_batch_size: 128
seed: 1337
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: linear
num_epochs: 40.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1
No log	1.0	7	0.4529	0.8608
0.5024	2.0	14	0.3989	0.8608
0.3533	3.0	21	0.3741	0.8608
0.3533	4.0	28	0.3161	0.8608
0.285	5.0	35	0.2824	0.8608
0.2491	6.0	42	0.2701	0.8608
0.2491	7.0	49	0.2062	0.9114
0.2032	8.0	56	0.2050	0.9494
0.157	9.0	63	0.2013	0.9494
0.1127	10.0	70	0.1960	0.9367
0.1127	11.0	77	0.1417	0.9494
0.0903	12.0	84	0.1307	0.9494
0.0922	13.0	91	0.0870	0.9873
0.0922	14.0	98	0.2048	0.9241
0.0595	15.0	105	0.1204	0.9620
0.0527	16.0	112	0.2553	0.9367
0.0527	17.0	119	0.1675	0.9367
0.0477	18.0	126	0.2265	0.9241
0.0411	19.0	133	0.1901	0.9367
0.0299	20.0	140	0.2423	0.9241
0.0299	21.0	147	0.0639	0.9873
0.0487	22.0	154	0.1255	0.9494
0.0359	23.0	161	0.1213	0.9494
0.0359	24.0	168	0.0727	0.9747
0.0302	25.0	175	0.1116	0.9494
0.0304	26.0	182	0.1062	0.9494
0.0304	27.0	189	0.2097	0.9241
0.0274	28.0	196	0.1276	0.9494
0.0291	29.0	203	0.0967	0.9494
0.0202	30.0	210	0.0765	0.9747
0.0202	31.0	217	0.0628	0.9873
0.0232	32.0	224	0.1388	0.9494
0.0264	33.0	231	0.1062	0.9494
0.0264	34.0	238	0.1320	0.9494
0.0219	35.0	245	0.1528	0.9494
0.0194	36.0	252	0.1746	0.9494
0.0194	37.0	259	0.1609	0.9494
0.0204	38.0	266	0.1482	0.9494
0.0217	39.0	273	0.1522	0.9494
0.0216	40.0	280	0.1499	0.9494

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.12.1+cu113
Datasets 2.7.1
Tokenizers 0.13.2

📄 License

The model is released under the Apache - 2.0 license.

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