Emotion Image Classification V2
E
Emotion Image Classification V2
Developed by jhoppanne
A fine-tuned emotion image classification model based on Google's ViT model, achieving an accuracy of 59.38% on the validation set.
Downloads 2,176
Release Time : 5/30/2024
Model Overview
This model is a fine-tuned emotion image classification model based on Google's ViT architecture, primarily used for classifying and recognizing emotions in images.
Model Features
Based on ViT Architecture
Uses Google's Vision Transformer (ViT) as the base model, offering excellent image feature extraction capabilities.
Emotion Classification
Fine-tuned specifically for image emotion recognition tasks, suitable for analyzing emotional content in images.
Moderate Accuracy
Achieves an accuracy of 59.38% on the validation set, suitable for general emotion recognition applications.
Model Capabilities
Image classification
Emotion recognition
Visual feature extraction
Use Cases
Social Media Analysis
User-uploaded Image Emotion Analysis
Analyzes the emotional tendencies in images uploaded by social media users
Can recognize 59.38% of emotion categories
Market Research
Ad Image Emotional Impact Assessment
Evaluates the emotional reactions elicited by advertisement images
đ Emotion-Image-Classification-V2
This model is a fine - tuned version of [google/vit - base - patch16 - 224 - in21k](https://huggingface.co/google/vit - base - patch16 - 224 - in21k) for image emotion classification, achieving certain accuracy on the imagefolder dataset.
đ Quick Start
This model is a fine - tuned version of [google/vit - base - patch16 - 224 - in21k](https://huggingface.co/google/vit - base - patch16 - 224 - in21k) on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.2748
- Accuracy: 0.5938
⨠Features
- Based on the pre - trained model [google/vit - base - patch16 - 224 - in21k](https://huggingface.co/google/vit - base - patch16 - 224 - in21k), it can perform emotion classification on images.
- Achieved an accuracy of 0.5938 on the evaluation set.
đ Documentation
Model Information
Property | Details |
---|---|
Model Type | Fine - tuned version of [google/vit - base - patch16 - 224 - in21k](https://huggingface.co/google/vit - base - patch16 - 224 - in21k) |
Training Data | imagefolder dataset |
Metrics | Accuracy |
Training Procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7e - 07
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
- lr_scheduler_type: linear
- num_epochs: 1750
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 1.0 | 20 | 2.0722 | 0.1875 |
No log | 2.0 | 40 | 2.0667 | 0.2062 |
No log | 3.0 | 60 | 2.0633 | 0.1938 |
No log | 4.0 | 80 | 2.0682 | 0.1938 |
No log | 5.0 | 100 | 2.0627 | 0.2 |
No log | 6.0 | 120 | 2.0618 | 0.2062 |
No log | 7.0 | 140 | 2.0573 | 0.2062 |
No log | 8.0 | 160 | 2.0589 | 0.2062 |
No log | 9.0 | 180 | 2.0585 | 0.1938 |
No log | 10.0 | 200 | 2.0576 | 0.1875 |
No log | 11.0 | 220 | 2.0578 | 0.2125 |
No log | 12.0 | 240 | 2.0498 | 0.2625 |
No log | 13.0 | 260 | 2.0542 | 0.2062 |
No log | 14.0 | 280 | 2.0534 | 0.2 |
No log | 15.0 | 300 | 2.0474 | 0.2562 |
No log | 16.0 | 320 | 2.0513 | 0.225 |
No log | 17.0 | 340 | 2.0472 | 0.2062 |
No log | 18.0 | 360 | 2.0465 | 0.2562 |
No log | 19.0 | 380 | 2.0379 | 0.275 |
No log | 20.0 | 400 | 2.0416 | 0.2375 |
No log | 21.0 | 420 | 2.0442 | 0.2687 |
No log | 22.0 | 440 | 2.0399 | 0.2313 |
No log | 23.0 | 460 | 2.0358 | 0.2625 |
No log | 24.0 | 480 | 2.0316 | 0.2437 |
2.0458 | 25.0 | 500 | 2.0314 | 0.2687 |
2.0458 | 26.0 | 520 | 2.0382 | 0.2437 |
2.0458 | 27.0 | 540 | 2.0246 | 0.275 |
2.0458 | 28.0 | 560 | 2.0211 | 0.3187 |
2.0458 | 29.0 | 580 | 2.0240 | 0.2625 |
2.0458 | 30.0 | 600 | 2.0198 | 0.2875 |
2.0458 | 31.0 | 620 | 2.0204 | 0.2625 |
2.0458 | 32.0 | 640 | 2.0210 | 0.275 |
2.0458 | 33.0 | 660 | 2.0046 | 0.3125 |
2.0458 | 34.0 | 680 | 2.0153 | 0.2625 |
2.0458 | 35.0 | 700 | 2.0098 | 0.2625 |
2.0458 | 36.0 | 720 | 2.0085 | 0.2812 |
2.0458 | 37.0 | 740 | 1.9978 | 0.3187 |
2.0458 | 38.0 | 760 | 1.9962 | 0.2938 |
2.0458 | 39.0 | 780 | 1.9955 | 0.3 |
2.0458 | 40.0 | 800 | 1.9923 | 0.3 |
2.0458 | 41.0 | 820 | 1.9913 | 0.3063 |
2.0458 | 42.0 | 840 | 1.9844 | 0.325 |
2.0458 | 43.0 | 860 | 1.9922 | 0.275 |
2.0458 | 44.0 | 880 | 1.9731 | 0.3187 |
2.0458 | 45.0 | 900 | 1.9833 | 0.3 |
2.0458 | 46.0 | 920 | 1.9763 | 0.3187 |
2.0458 | 47.0 | 940 | 1.9686 | 0.35 |
2.0458 | 48.0 | 960 | 1.9707 | 0.3187 |
2.0458 | 49.0 | 980 | 1.9631 | 0.3312 |
1.9677 | 50.0 | 1000 | 1.9561 | 0.2812 |
1.9677 | 51.0 | 1020 | 1.9527 | 0.3312 |
1.9677 | 52.0 | 1040 | 1.9447 | 0.3563 |
1.9677 | 53.0 | 1060 | 1.9542 | 0.35 |
1.9677 | 54.0 | 1080 | 1.9509 | 0.375 |
1.9677 | 55.0 | 1100 | 1.9378 | 0.3937 |
1.9677 | 56.0 | 1120 | 1.9431 | 0.3563 |
1.9677 | 57.0 | 1140 | 1.9397 | 0.3312 |
1.9677 | 58.0 | 1160 | 1.9322 | 0.4 |
1.9677 | 59.0 | 1180 | 1.9252 | 0.3688 |
1.9677 | 60.0 | 1200 | 1.9209 | 0.3063 |
1.9677 | 61.0 | 1220 | 1.9210 | 0.3563 |
1.9677 | 62.0 | 1240 | 1.9226 | 0.3688 |
1.9677 | 63.0 | 1260 | 1.9054 | 0.3688 |
1.9677 | 64.0 | 1280 | 1.9153 | 0.35 |
1.9677 | 65.0 | 1300 | 1.8993 | 0.3563 |
1.9677 | 66.0 | 1320 | 1.9008 | 0.375 |
1.9677 | 67.0 | 1340 | 1.9008 | 0.35 |
1.9677 | 68.0 | 1360 | 1.8866 | 0.3625 |
1.9677 | 69.0 | 1380 | 1.8771 | 0.3937 |
1.9677 | 70.0 | 1400 | 1.8758 | 0.4 |
1.9677 | 71.0 | 1420 | 1.8682 | 0.3937 |
1.9677 | 72.0 | 1440 | 1.8604 | 0.3688 |
1.9677 | 73.0 | 1460 | 1.8660 | 0.3937 |
1.9677 | 74.0 | 1480 | 1.8702 | 0.3375 |
1.8562 | 75.0 | 1500 | 1.8483 | 0.4313 |
1.8562 | 76.0 | 1520 | 1.8525 | 0.3875 |
1.8562 | 77.0 | 1540 | 1.8467 | 0.3937 |
1.8562 | 78.0 | 1560 | 1.8481 | 0.3812 |
1.8562 | 79.0 | 1580 | 1.8282 | 0.3812 |
1.8562 | 80.0 | 1600 | 1.8395 | 0.3875 |
1.8562 | 81.0 | 1620 | 1.8251 | 0.375 |
1.8562 | 82.0 | 1640 | 1.8215 | 0.4125 |
1.8562 | 83.0 | 1660 | 1.8179 | 0.3625 |
1.8562 | 84.0 | 1680 | 1.8130 | 0.4188 |
1.8562 | 85.0 | 1700 | 1.8066 | 0.4 |
1.8562 | 86.0 | 1720 | 1.7993 | 0.4062 |
1.8562 | 87.0 | 1740 | 1.7954 | 0.4188 |
1.8562 | 88.0 | 1760 | 1.7936 | 0.3937 |
1.8562 | 89.0 | 1780 | 1.7972 | 0.4188 |
1.8562 | 90.0 | 1800 | 1.7876 | 0.3937 |
1.8562 | 91.0 | 1820 | 1.7810 | 0.3937 |
1.8562 | 92.0 | 1840 | 1.7838 | 0.3937 |
1.8562 | 93.0 | 1860 | 1.7711 | 0.3812 |
1.8562 | 94.0 | 1880 | 1.7780 | 0.3688 |
1.8562 | 95.0 | 1900 | 1.7426 | 0.4062 |
1.8562 | 96.0 | 1920 | 1.7399 | 0.425 |
1.8562 | 97.0 | 1940 | 1.7561 | 0.375 |
1.8562 | 98.0 | 1960 | 1.7287 | 0.3937 |
1.8562 | 99.0 | 1980 | 1.7425 | 0.4437 |
1.7294 | 100.0 | 2000 | 1.7308 | 0.4188 |
1.7294 | 101.0 | 2020 | 1.7389 | 0.375 |
1.7294 | 102.0 | 2040 | 1.7249 | 0.4375 |
1.7294 | 103.0 | 2060 | 1.7297 | 0.4188 |
1.7294 | 104.0 | 2080 | 1.7361 | 0.3875 |
1.7294 | 105.0 | 2100 | 1.7188 | 0.4188 |
1.7294 | 106.0 | 2120 | 1.7181 | 0.4562 |
1.7294 | 107.0 | 2140 | 1.7044 | 0.425 |
1.7294 | 108.0 | 2160 | 1.7030 | 0.4188 |
1.7294 | 109.0 | 2180 | 1.7070 | 0.425 |
1.7294 | 110.0 | 2200 | 1.7006 | 0.4437 |
1.7294 | 111.0 | 2220 | 1.6862 | 0.4688 |
1.7294 | 112.0 | 2240 | 1.6881 | 0.4437 |
1.7294 | 113.0 | 2260 | 1.6798 | 0.45 |
1.7294 | 114.0 | 2280 | 1.6982 | 0.3937 |
1.7294 | 115.0 | 2300 | 1.6812 | 0.4375 |
1.7294 | 116.0 | 2320 | 1.6751 | 0.45 |
1.7294 | 117.0 | 2340 | 1.6849 | 0.45 |
1.7294 | 118.0 | 2360 | 1.6690 | 0.4375 |
1.7294 | 119.0 | 2380 | 1.6583 | 0.4688 |
1.7294 | 120.0 | 2400 | 1.6666 | 0.4375 |
1.7294 | 121.0 | 2420 | 1.6651 | 0.45 |
1.7294 | 122.0 | 2440 | 1.6477 | 0.4688 |
1.7294 | 123.0 | 2460 | 1.6520 | 0.475 |
1.7294 | 124.0 | 2480 | 1.6642 | 0.4125 |
1.6198 | 125.0 | 2500 | 1.6633 | 0.475 |
1.6198 | 126.0 | 2520 | 1.6443 | 0.4375 |
1.6198 | 127.0 | 2540 | 1.6398 | 0.4813 |
1.6198 | 128.0 | 2560 | 1.6516 | 0.4437 |
1.6198 | 129.0 | 2580 | 1.6496 | 0.4562 |
1.6198 | 130.0 | 2600 | 1.6342 | 0.4625 |
1.6198 | 131.0 | 2620 | 1.6330 | 0.4437 |
1.6198 | 132.0 | 2640 | 1.6341 | 0.4625 |
1.6198 | 133.0 | 2660 | 1.6167 | 0.475 |
1.6198 | 134.0 | 2680 | 1.6270 | 0.4562 |
1.6198 | 135.0 | 2700 | 1.6322 | 0.4562 |
1.6198 | 136.0 | 2720 | 1.6252 | 0.4188 |
1.6198 | 137.0 | 2740 | 1.6045 | 0.4562 |
1.6198 | 138.0 | 2760 | 1.6107 | 0.5 |
1.6198 | 139.0 | 2780 | 1.6170 | 0.45 |
1.6198 | 140.0 | 2800 | 1.6067 | 0.4813 |
1.6198 | 141.0 | 2820 | 1.6116 | 0.4437 |
1.6198 | 142.0 | 2840 | 1.5962 | 0.4813 |
1.6198 | 143.0 | 2860 | 1.6096 | 0.425 |
1.6198 | 144.0 | 2880 | 1.6051 | 0.4562 |
1.6198 | 145.0 | 2900 | 1.5939 | 0.525 |
1.6198 | 146.0 | 2920 | 1.5871 | 0.5062 |
1.6198 | 147.0 | 2940 | 1.5917 | 0.4688 |
1.6198 | 148.0 | 2960 | 1.5911 | 0.4938 |
1.6198 | 149.0 | 2980 | 1.5950 | 0.4437 |
1.5343 | 150.0 | 3000 | 1.5974 | 0.4625 |
1.5343 | 151.0 | 3020 | 1.5713 | 0.4813 |
1.5343 | 152.0 | 3040 | 1.5637 | 0.5437 |
1.5343 | 153.0 | 3060 | 1.5830 | 0.4688 |
1.5343 | 154.0 | 3080 | 1.5770 | 0.4938 |
1.5343 | 155.0 | 3100 | 1.5800 | 0.5188 |
1.5343 | 156.0 | 3120 | 1.5782 | 0.4625 |
1.5343 | 157.0 | 3140 | 1.5769 | 0.4562 |
1.5343 | 158.0 | 3160 | 1.5751 | 0.5 |
1.5343 | 159.0 | 3180 | 1.5739 | 0.4938 |
1.5343 | 160.0 | 3200 | 1.5555 | 0.5 |
1.5343 | 161.0 | 3220 | 1.5561 | 0.475 |
1.5343 | 162.0 | 3240 | 1.5521 | 0.4938 |
1.5343 | 163.0 | 3260 | 1.5670 | 0.4688 |
1.5343 | 164.0 | 3280 | 1.5621 | 0.475 |
1.5343 | 165.0 | 3300 | 1.5622 | 0.4813 |
1.5343 | 166.0 | 3320 | 1.5497 | 0.4562 |
1.5343 | 167.0 | 3340 | 1.5535 | 0.4813 |
1.5343 | 168.0 | 3360 | 1.5526 | 0.4875 |
1.5343 | 169.0 | 3380 | 1.5301 | 0.5 |
1.5343 | 170.0 | 3400 | 1.5487 | 0.4813 |
1.5343 | 171.0 | 3420 | 1.5489 | 0.4625 |
1.5343 | 172.0 | 3440 | 1.5456 | 0.5 |
1.5343 | 173.0 | 3460 | 1.5355 | 0.5 |
1.5343 | 174.0 | 3480 | 1.5306 | 0.5 |
1.4643 | 175.0 | 3500 | 1.5436 |
đ License
This model is licensed under the Apache 2.0 license.
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