model_id
stringlengths 7
105
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|---|---|---|
prithivMLmods/AI-vs-Deepfake-vs-Real-ONNX
|
# **AI-vs-Deepfake-vs-Real-ONNX**
AI-vs-Deepfake-vs-Real is an image classification model for differentiating between artificial, deepfake, and real images. It is based on Google's ViT model (`google/vit-base-patch32-224-in21k`).
A reasonable number of training samples were used to achieve good efficiency in the final training process and its efficiency metrics. Since this task involves classifying images into three categories (artificial, deepfake, and real), the model was trained accordingly. Future improvements will be made based on the complexity of the task.
```python
id2label: {
"0": "Artificial",
"1": "Deepfake",
"2": "Real"
}
```
```python
Classification report:
precision recall f1-score support
Artificial 0.9897 0.9347 0.9614 1333
Deepfake 0.9409 0.9910 0.9653 1333
Real 0.9970 0.9993 0.9981 1334
accuracy 0.9750 4000
macro avg 0.9759 0.9750 0.9749 4000
weighted avg 0.9759 0.9750 0.9750 4000
```
# **Inference with Hugging Face Pipeline**
```python
from transformers import pipeline
# Load the model
pipe = pipeline('image-classification', model="prithivMLmods/AI-vs-Deepfake-vs-Real", device=0)
# Predict on an image
result = pipe("path_to_image.jpg")
print(result)
```
# **Inference with PyTorch**
```python
from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch
# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/AI-vs-Deepfake-vs-Real")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/AI-vs-Deepfake-vs-Real")
# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")
# Perform inference
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class = torch.argmax(logits, dim=1).item()
# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")
```
# **Limitations of AI-vs-Deepfake-vs-Real**
1. **Limited Generalization** – The model is trained on specific datasets and may not generalize well to unseen deepfake generation techniques or novel deepfake artifacts.
2. **Variability in Deepfake Quality** – Different deepfake creation methods introduce varying levels of noise and artifacts, which may affect model performance.
3. **Dependence on Training Data** – The model's accuracy is influenced by the quality and diversity of the training data. Biases in the dataset could lead to misclassification.
4. **Resolution Sensitivity** – Performance may degrade when analyzing extremely high- or low-resolution images not seen during training.
5. **Potential False Positives/Negatives** – The model may sometimes misclassify artificial, deepfake, or real images, limiting its reliability in critical applications.
6. **Lack of Explainability** – Being based on a ViT (Vision Transformer), the decision-making process is less interpretable than traditional models, making it harder to analyze why certain classifications are made.
7. **Not a Deepfake Detector** – This model categorizes images but does not specifically determine whether an image is fake; rather, it differentiates between artificial, deepfake, and real images.
# **Intended Use of AI-vs-Deepfake-vs-Real**
- **Quality Assessment for Research** – Used by researchers to analyze and improve deepfake generation methods by assessing output quality.
- **Dataset Filtering** – Helps filter out low-quality deepfake samples in datasets for better training of deepfake detection models.
- **Forensic Analysis** – Supports forensic teams in evaluating image authenticity and prioritizing high-quality deepfakes for deeper analysis.
- **Content Moderation** – Assists social media platforms and content moderation teams in assessing image authenticity before deciding on further actions.
- **Benchmarking Deepfake Models** – Used to compare and evaluate different deepfake generation models based on their output quality and authenticity.
|
[
"artificial",
"deepfake",
"real"
] |
ekosbg/results
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
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: 1.3406
- Accuracy: 0.5125
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7299 | 1.0 | 80 | 1.6712 | 0.3563 |
| 1.3736 | 2.0 | 160 | 1.5112 | 0.45 |
| 1.167 | 3.0 | 240 | 1.3714 | 0.5188 |
| 0.9786 | 4.0 | 320 | 1.3406 | 0.5125 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
prithivMLmods/Deepfake-QualityAssess2.1-85M-ONNX
|
# **Deepfake-QualityAssess2.1-85M-ONNX**
Deepfake-QualityAssess2.1-85M is an image classification model for quality assessment of good and bad quality deepfakes. It is based on Google's ViT model (`google/vit-base-patch32-224-in21k`).
A reasonable number of training samples were used to achieve good efficiency in the final training process and its efficiency metrics. Since this task involves classifying deepfake images with varying quality levels, the model was trained accordingly. Future improvements will be made based on the complexity of the task.
```python
id2label: {
"0": "Issue In Deepfake",
"1": "High Quality Deepfake"
}
```
# **Inference with Hugging Face Pipeline**
```python
from transformers import pipeline
# Load the model
pipe = pipeline('image-classification', model="prithivMLmods/Deepfake-QualityAssess2.1-85M", device=0)
# Predict on an image
result = pipe("path_to_image.jpg")
print(result)
```
# **Inference with PyTorch**
```python
from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch
# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-QualityAssess2.1-85M")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-QualityAssess2.1-85M")
# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")
# Perform inference
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class = torch.argmax(logits, dim=1).item()
# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")
```
# **Limitations of Deepfake-QualityAssess2.1-85M**
1. **Limited Generalization** – The model is trained on specific datasets and may not generalize well to unseen deepfake generation techniques or novel deepfake artifacts.
2. **Variability in Deepfake Quality** – Different deepfake creation methods introduce varying levels of noise and artifacts, which may affect model performance.
3. **Dependence on Training Data** – The model's accuracy is influenced by the quality and diversity of the training data. Biases in the dataset could lead to misclassification.
4. **Resolution Sensitivity** – Performance may degrade when analyzing extremely high- or low-resolution images not seen during training.
5. **Potential False Positives/Negatives** – The model may sometimes misclassify good-quality deepfakes as bad (or vice versa), limiting its reliability in critical applications.
6. **Lack of Explainability** – Being based on a ViT (Vision Transformer), the decision-making process is less interpretable than traditional models, making it harder to analyze why certain classifications are made.
7. **Not a Deepfake Detector** – This model assesses the quality of deepfakes but does not determine whether an image is real or fake.
# **Intended Use of Deepfake-QualityAssess2.1-85M**
- **Quality Assessment for Research** – Used by researchers to analyze and improve deepfake generation methods by assessing output quality.
- **Dataset Filtering** – Helps filter out low-quality deepfake samples in datasets for better training of deepfake detection models.
- **Forensic Analysis** – Supports forensic teams in evaluating deepfake quality to prioritize high-quality samples for deeper analysis.
- **Content Moderation** – Assists social media platforms and content moderation teams in assessing deepfake quality before deciding on further actions.
- **Benchmarking Deepfake Models** – Used to compare and evaluate different deepfake generation models based on their output quality.
|
[
"issue in deepfake",
"high quality deepfake"
] |
prithivMLmods/Deepfake-QualityAssess2.0-85M-ONNX
|
# **Deepfake-QualityAssess2.0-85M-ONNX**
Deepfake-QualityAssess2.0-85M is an image classification model for quality assessment of good and bad quality deepfakes. It is based on Google's ViT model (`google/vit-base-patch32-224-in21k`).
A reasonable number of training samples were used to achieve good efficiency in the final training process and its efficiency metrics. Since this task involves classifying deepfake images with varying quality levels, the model was trained accordingly. Future improvements will be made based on the complexity of the task.
```python
id2label: {
"0": "Issue In Deepfake",
"1": "High Quality Deepfake"
}
```
# **Inference with Hugging Face Pipeline**
```python
from transformers import pipeline
# Load the model
pipe = pipeline('image-classification', model="prithivMLmods/Deepfake-QualityAssess2.0-85M", device=0)
# Predict on an image
result = pipe("path_to_image.jpg")
print(result)
```
# **Inference with PyTorch**
```python
from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch
# Load the model and processor
model = ViTForImageClassification.from_pretrained("prithivMLmods/Deepfake-QualityAssess2.0-85M")
processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deepfake-QualityAssess2.0-85M")
# Load and preprocess the image
image = Image.open("path_to_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")
# Perform inference
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class = torch.argmax(logits, dim=1).item()
# Map class index to label
label = model.config.id2label[predicted_class]
print(f"Predicted Label: {label}")
```
# **Limitations of Deepfake-QualityAssess2.0-85M**
1. **Limited Generalization** – The model is trained on specific datasets and may not generalize well to unseen deepfake generation techniques or novel deepfake artifacts.
2. **Variability in Deepfake Quality** – Different deepfake creation methods introduce varying levels of noise and artifacts, which may affect model performance.
3. **Dependence on Training Data** – The model's accuracy is influenced by the quality and diversity of the training data. Biases in the dataset could lead to misclassification.
4. **Resolution Sensitivity** – Performance may degrade when analyzing extremely high- or low-resolution images not seen during training.
5. **Potential False Positives/Negatives** – The model may sometimes misclassify good-quality deepfakes as bad (or vice versa), limiting its reliability in critical applications.
6. **Lack of Explainability** – Being based on a ViT (Vision Transformer), the decision-making process is less interpretable than traditional models, making it harder to analyze why certain classifications are made.
7. **Not a Deepfake Detector** – This model assesses the quality of deepfakes but does not determine whether an image is real or fake.
# **Intended Use of Deepfake-QualityAssess2.0-85M**
- **Quality Assessment for Research** – Used by researchers to analyze and improve deepfake generation methods by assessing output quality.
- **Dataset Filtering** – Helps filter out low-quality deepfake samples in datasets for better training of deepfake detection models.
- **Forensic Analysis** – Supports forensic teams in evaluating deepfake quality to prioritize high-quality samples for deeper analysis.
- **Content Moderation** – Assists social media platforms and content moderation teams in assessing deepfake quality before deciding on further actions.
- **Benchmarking Deepfake Models** – Used to compare and evaluate different deepfake generation models based on their output quality.
|
[
"issue in deepfake",
"high quality deepfake"
] |
inginjadibuparto/image_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# image_classification
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: 1.3640
- Accuracy: 0.5188
## 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: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.5
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 5 | 2.0560 | 0.2 |
| 2.0089 | 2.0 | 10 | 1.9447 | 0.3 |
| 2.0089 | 3.0 | 15 | 1.6725 | 0.4062 |
| 1.4709 | 4.0 | 20 | 1.4705 | 0.4313 |
| 1.4709 | 5.0 | 25 | 1.3500 | 0.5 |
| 0.7193 | 6.0 | 30 | 1.4467 | 0.4562 |
| 0.7193 | 7.0 | 35 | 1.3640 | 0.5188 |
| 0.2139 | 8.0 | 40 | 1.5223 | 0.45 |
| 0.2139 | 9.0 | 45 | 1.4146 | 0.5188 |
| 0.1101 | 10.0 | 50 | 1.4256 | 0.5062 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu118
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
shawnmichael/vit-large-fire-smoke-detection-v1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-large-fire-smoke-detection-v1
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the None dataset.
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"none",
"fire",
"smoke",
"smoke and fire"
] |
andyxhliu/vit-base-patch16-224-in21k-finetuned-SMD-shuffled-transformed
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-in21k-finetuned-SMD-shuffled-transformed
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0602
- Accuracy: 1.0
## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.524 | 1.0 | 40 | 1.5157 | 0.5915 |
| 0.8485 | 2.0 | 80 | 0.7746 | 0.8592 |
| 0.3255 | 3.0 | 120 | 0.3268 | 0.9577 |
| 0.1379 | 4.0 | 160 | 0.1649 | 0.9859 |
| 0.0948 | 5.0 | 200 | 0.1110 | 1.0 |
| 0.0749 | 6.0 | 240 | 0.0897 | 1.0 |
| 0.0654 | 7.0 | 280 | 0.0761 | 1.0 |
| 0.0536 | 8.0 | 320 | 0.0602 | 1.0 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"划伤",
"印字偏移",
"污染",
"污染手绘",
"磨光",
"非反光大污染"
] |
ekosbg/image_emotion_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# image_emotion_classification
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: 1.1401
- Accuracy: 0.6062
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8402 | 1.0 | 80 | 1.1834 | 0.5625 |
| 0.4108 | 2.0 | 160 | 1.1906 | 0.6062 |
| 0.2943 | 3.0 | 240 | 1.1393 | 0.6062 |
| 0.1748 | 4.0 | 320 | 1.1401 | 0.6062 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
cvmil/dinov2-base_augmented-v2_fft
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dinov2-base_rice-leaf-disease-augmented-v2_fft
This model is a fine-tuned version of [facebook/dinov2-base](https://huggingface.co/facebook/dinov2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6772
- Accuracy: 0.9018
## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8654 | 1.0 | 125 | 0.4470 | 0.8661 |
| 0.2009 | 2.0 | 250 | 0.5260 | 0.8631 |
| 0.1148 | 3.0 | 375 | 0.6247 | 0.8690 |
| 0.0412 | 4.0 | 500 | 0.5468 | 0.8988 |
| 0.0052 | 5.0 | 625 | 0.5418 | 0.9018 |
| 0.0001 | 6.0 | 750 | 0.5245 | 0.9077 |
| 0.0863 | 7.0 | 875 | 0.6622 | 0.8571 |
| 0.059 | 8.0 | 1000 | 0.6755 | 0.8869 |
| 0.0153 | 9.0 | 1125 | 0.6671 | 0.9048 |
| 0.0014 | 10.0 | 1250 | 0.6834 | 0.8988 |
| 0.0 | 11.0 | 1375 | 0.6805 | 0.9018 |
| 0.0 | 12.0 | 1500 | 0.6765 | 0.9048 |
| 0.0 | 13.0 | 1625 | 0.6773 | 0.9018 |
| 0.0 | 14.0 | 1750 | 0.6771 | 0.9018 |
| 0.0 | 15.0 | 1875 | 0.6772 | 0.9018 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"bacterial leaf blight",
"brown spot",
"healthy rice leaf",
"leaf blast",
"leaf scald",
"narrow brown leaf spot",
"rice hispa",
"sheath blight"
] |
sabrilben/image_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# image_classification
This model is a fine-tuned version of [google/vit-large-patch32-384](https://huggingface.co/google/vit-large-patch32-384) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8452
- Accuracy: 0.3
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1828 | 1.0 | 20 | 2.1899 | 0.1125 |
| 2.1419 | 2.0 | 40 | 2.1018 | 0.1625 |
| 2.078 | 3.0 | 60 | 2.1286 | 0.1625 |
| 2.0943 | 4.0 | 80 | 2.1462 | 0.15 |
| 2.0486 | 5.0 | 100 | 2.0665 | 0.2 |
| 1.9442 | 6.0 | 120 | 1.9868 | 0.2562 |
| 1.9307 | 7.0 | 140 | 1.9403 | 0.2375 |
| 1.8743 | 8.0 | 160 | 1.8866 | 0.275 |
| 1.7348 | 9.0 | 180 | 1.7927 | 0.3312 |
| 1.6455 | 10.0 | 200 | 1.7579 | 0.3187 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
afidzulinn/vit-emotion-model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-emotion-model
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1329
- Accuracy: 0.575
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5767 | 1.0 | 80 | 1.3573 | 0.4875 |
| 0.8289 | 2.0 | 160 | 1.1968 | 0.4938 |
| 0.3138 | 3.0 | 240 | 1.0986 | 0.5687 |
| 0.0858 | 4.0 | 320 | 1.1277 | 0.5687 |
| 0.0471 | 5.0 | 400 | 1.1329 | 0.575 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
han2512/emotion_model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion_model
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7333
- Accuracy: 0.3375
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.9132 | 0.275 |
| No log | 2.0 | 80 | 1.7753 | 0.3125 |
| No log | 3.0 | 120 | 1.7333 | 0.3375 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
yazidsupriadi/results
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0319
- Accuracy: 0.2313
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9934 | 1.0 | 40 | 2.0319 | 0.2313 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
yazidsupriadi/emotion
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2853
- Accuracy: 0.4938
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2088 | 1.0 | 10 | 2.1535 | 0.1 |
| 1.9641 | 2.0 | 20 | 1.9350 | 0.2687 |
| 1.6569 | 3.0 | 30 | 1.6240 | 0.3812 |
| 1.3748 | 4.0 | 40 | 1.5203 | 0.4062 |
| 1.1819 | 5.0 | 50 | 1.3649 | 0.4313 |
| 1.044 | 6.0 | 60 | 1.2983 | 0.4938 |
| 0.8987 | 7.0 | 70 | 1.3143 | 0.4938 |
| 0.7936 | 8.0 | 80 | 1.2641 | 0.5125 |
| 0.7081 | 9.0 | 90 | 1.2867 | 0.4875 |
| 0.6618 | 10.0 | 100 | 1.3179 | 0.4625 |
| 0.6147 | 11.0 | 110 | 1.2316 | 0.5375 |
| 0.5805 | 12.0 | 120 | 1.2446 | 0.5375 |
| 0.5597 | 13.0 | 130 | 1.2595 | 0.5 |
| 0.5366 | 14.0 | 140 | 1.2280 | 0.5312 |
| 0.5007 | 15.0 | 150 | 1.2602 | 0.5188 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
khrnnsal/results
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
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: 1.3618
- Accuracy: 0.5188
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8083 | 1.0 | 40 | 1.7085 | 0.4188 |
| 1.4207 | 2.0 | 80 | 1.5068 | 0.5188 |
| 1.1897 | 3.0 | 120 | 1.4288 | 0.5 |
| 0.9911 | 4.0 | 160 | 1.3755 | 0.5062 |
| 0.8888 | 5.0 | 200 | 1.3618 | 0.5188 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.0
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
grevalby/results
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
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: 1.6197
- Accuracy: 0.45
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.6848 | 0.375 |
| No log | 2.0 | 80 | 1.6389 | 0.4125 |
| No log | 3.0 | 120 | 1.6197 | 0.45 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
grevalby/grevalby-project
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# grevalby-project
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: 1.4472
- Accuracy: 0.5563
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.4116 | 0.55 |
| No log | 2.0 | 80 | 1.4192 | 0.5375 |
| No log | 3.0 | 120 | 1.3950 | 0.5563 |
| No log | 4.0 | 160 | 1.4198 | 0.55 |
| No log | 5.0 | 200 | 1.4346 | 0.5625 |
| No log | 6.0 | 240 | 1.4440 | 0.55 |
| No log | 7.0 | 280 | 1.4472 | 0.5563 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_GOOGLE_1_0
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_GOOGLE_1_0
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0808
- Accuracy: 0.9742
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2661 | 0.9886 | 65 | 0.2364 | 0.9158 |
| 0.1574 | 1.9924 | 131 | 0.1487 | 0.9567 |
| 0.1216 | 2.9962 | 197 | 0.1229 | 0.9583 |
| 0.1028 | 4.0 | 263 | 0.0992 | 0.9692 |
| 0.087 | 4.9430 | 325 | 0.0808 | 0.9742 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_GOOGLE_1_1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_GOOGLE_1_1
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0932
- Accuracy: 0.9725
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 1
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2989 | 0.9886 | 65 | 0.2502 | 0.9058 |
| 0.1747 | 1.9924 | 131 | 0.1621 | 0.9375 |
| 0.1098 | 2.9962 | 197 | 0.1298 | 0.9558 |
| 0.1095 | 4.0 | 263 | 0.1226 | 0.96 |
| 0.1128 | 4.9430 | 325 | 0.0932 | 0.9725 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_GOOGLE_1_2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_GOOGLE_1_2
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0972
- Accuracy: 0.9642
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.3087 | 0.9886 | 65 | 0.2334 | 0.9183 |
| 0.1442 | 1.9924 | 131 | 0.1368 | 0.95 |
| 0.1411 | 2.9962 | 197 | 0.1169 | 0.9575 |
| 0.1019 | 4.0 | 263 | 0.1048 | 0.9683 |
| 0.0822 | 4.9430 | 325 | 0.0972 | 0.9642 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_GOOGLE_1_3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_GOOGLE_1_3
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0913
- Accuracy: 0.9683
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 3
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2295 | 0.9886 | 65 | 0.2393 | 0.92 |
| 0.1422 | 1.9924 | 131 | 0.1413 | 0.9508 |
| 0.1083 | 2.9962 | 197 | 0.1082 | 0.9592 |
| 0.0966 | 4.0 | 263 | 0.1057 | 0.9583 |
| 0.0881 | 4.9430 | 325 | 0.0913 | 0.9683 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_GOOGLE_1_4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_GOOGLE_1_4
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0989
- Accuracy: 0.9642
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2888 | 0.9886 | 65 | 0.2387 | 0.9175 |
| 0.165 | 1.9924 | 131 | 0.1399 | 0.9567 |
| 0.1393 | 2.9962 | 197 | 0.0990 | 0.9692 |
| 0.0998 | 4.0 | 263 | 0.1154 | 0.96 |
| 0.0887 | 4.9430 | 325 | 0.0989 | 0.9642 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_GOOGLE_2_0
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_GOOGLE_2_0
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2216
- Accuracy: 0.9189
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.7954 | 0.96 | 18 | 0.6255 | 0.7842 |
| 0.4474 | 1.9733 | 37 | 0.3137 | 0.8890 |
| 0.2726 | 2.9867 | 56 | 0.2842 | 0.8996 |
| 0.2299 | 4.0 | 75 | 0.2312 | 0.9235 |
| 0.2007 | 4.8 | 90 | 0.2216 | 0.9189 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
D0te/finetuned-indian-food
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-indian-food
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 indian_food_images dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.9893
- eval_model_preparation_time: 0.0123
- eval_accuracy: 0.0659
- eval_runtime: 444.1939
- eval_samples_per_second: 2.118
- eval_steps_per_second: 0.266
- step: 0
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"burger",
"butter_naan",
"kaathi_rolls",
"kadai_paneer",
"kulfi",
"masala_dosa",
"momos",
"paani_puri",
"pakode",
"pav_bhaji",
"pizza",
"samosa",
"chai",
"chapati",
"chole_bhature",
"dal_makhani",
"dhokla",
"fried_rice",
"idli",
"jalebi"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_GOOGLE_2_1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_GOOGLE_2_1
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2058
- Accuracy: 0.9290
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 1
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.6412 | 0.96 | 18 | 0.5709 | 0.8015 |
| 0.4453 | 1.9733 | 37 | 0.3163 | 0.8924 |
| 0.2778 | 2.9867 | 56 | 0.2352 | 0.9211 |
| 0.2196 | 4.0 | 75 | 0.2056 | 0.9268 |
| 0.192 | 4.8 | 90 | 0.2058 | 0.9290 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_GOOGLE_2_2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_GOOGLE_2_2
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2186
- Accuracy: 0.9296
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.9163 | 0.96 | 18 | 0.7277 | 0.7586 |
| 0.496 | 1.9733 | 37 | 0.3519 | 0.8782 |
| 0.2984 | 2.9867 | 56 | 0.2645 | 0.9061 |
| 0.2294 | 4.0 | 75 | 0.2249 | 0.9239 |
| 0.2132 | 4.8 | 90 | 0.2186 | 0.9296 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_GOOGLE_2_3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_GOOGLE_2_3
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1967
- Accuracy: 0.9324
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 3
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.6165 | 0.96 | 18 | 0.5794 | 0.8006 |
| 0.4757 | 1.9733 | 37 | 0.3311 | 0.8839 |
| 0.3049 | 2.9867 | 56 | 0.2397 | 0.9187 |
| 0.2361 | 4.0 | 75 | 0.2149 | 0.9264 |
| 0.193 | 4.8 | 90 | 0.1967 | 0.9324 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_GOOGLE_2_4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_GOOGLE_2_4
This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2067
- Accuracy: 0.9283
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.6691 | 0.96 | 18 | 0.5408 | 0.8178 |
| 0.3891 | 1.9733 | 37 | 0.3347 | 0.8831 |
| 0.2607 | 2.9867 | 56 | 0.2480 | 0.9160 |
| 0.2224 | 4.0 | 75 | 0.2125 | 0.9249 |
| 0.1974 | 4.8 | 90 | 0.2067 | 0.9283 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
sabrilben/emotion_recognition
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion_recognition
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: 1.9225
- Accuracy: 0.2687
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.053 | 1.0 | 10 | 2.0367 | 0.1812 |
| 1.9519 | 2.0 | 20 | 1.9521 | 0.2812 |
| 1.8648 | 3.0 | 30 | 1.9228 | 0.2938 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
umairrkhn/fine-tuned-nsfw-classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine-tuned-nsfw-classification
This model is a fine-tuned version of [Falconsai/nsfw_image_detection](https://huggingface.co/Falconsai/nsfw_image_detection) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Accuracy: 1.0
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.0176 | 0.6083 | 500 | 0.0093 | 0.999 |
| 0.0031 | 1.2165 | 1000 | 0.0021 | 0.999 |
| 0.0015 | 1.8248 | 1500 | 0.0049 | 0.999 |
| 0.0001 | 2.4331 | 2000 | 0.0086 | 0.999 |
| 0.0 | 3.0414 | 2500 | 0.0087 | 0.999 |
| 0.0 | 3.6496 | 3000 | 0.0088 | 0.999 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"normal",
"nsfw"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_CHINA_1_0
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_CHINA_1_0
This model is a fine-tuned version of [Visual-Attention-Network/van-tiny](https://huggingface.co/Visual-Attention-Network/van-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1031
- Accuracy: 0.965
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.4684 | 0.9886 | 65 | 0.3592 | 0.89 |
| 0.22 | 1.9924 | 131 | 0.1755 | 0.9425 |
| 0.1846 | 2.9962 | 197 | 0.1364 | 0.9633 |
| 0.1452 | 4.0 | 263 | 0.1289 | 0.9567 |
| 0.1353 | 4.9430 | 325 | 0.1031 | 0.965 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_CHINA_1_1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_CHINA_1_1
This model is a fine-tuned version of [Visual-Attention-Network/van-tiny](https://huggingface.co/Visual-Attention-Network/van-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1153
- Accuracy: 0.965
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 1
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.4389 | 0.9886 | 65 | 0.3336 | 0.8867 |
| 0.2215 | 1.9924 | 131 | 0.1861 | 0.9408 |
| 0.1598 | 2.9962 | 197 | 0.1352 | 0.9517 |
| 0.1494 | 4.0 | 263 | 0.1236 | 0.9575 |
| 0.1565 | 4.9430 | 325 | 0.1153 | 0.965 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_CHINA_1_2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_CHINA_1_2
This model is a fine-tuned version of [Visual-Attention-Network/van-tiny](https://huggingface.co/Visual-Attention-Network/van-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1184
- Accuracy: 0.9617
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.4991 | 0.9886 | 65 | 0.3683 | 0.8967 |
| 0.1981 | 1.9924 | 131 | 0.1733 | 0.935 |
| 0.1855 | 2.9962 | 197 | 0.1410 | 0.95 |
| 0.1262 | 4.0 | 263 | 0.1118 | 0.9675 |
| 0.1217 | 4.9430 | 325 | 0.1184 | 0.9617 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_CHINA_1_3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_CHINA_1_3
This model is a fine-tuned version of [Visual-Attention-Network/van-tiny](https://huggingface.co/Visual-Attention-Network/van-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1131
- Accuracy: 0.9625
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 3
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.4661 | 0.9886 | 65 | 0.3667 | 0.8917 |
| 0.2259 | 1.9924 | 131 | 0.1830 | 0.94 |
| 0.152 | 2.9962 | 197 | 0.1395 | 0.9517 |
| 0.1363 | 4.0 | 263 | 0.1203 | 0.9592 |
| 0.1438 | 4.9430 | 325 | 0.1131 | 0.9625 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_CHINA_1_4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_CHINA_1_4
This model is a fine-tuned version of [Visual-Attention-Network/van-tiny](https://huggingface.co/Visual-Attention-Network/van-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1221
- Accuracy: 0.9625
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.4973 | 0.9886 | 65 | 0.3542 | 0.8917 |
| 0.2218 | 1.9924 | 131 | 0.1674 | 0.9483 |
| 0.1791 | 2.9962 | 197 | 0.1262 | 0.9608 |
| 0.1562 | 4.0 | 263 | 0.1409 | 0.955 |
| 0.147 | 4.9430 | 325 | 0.1221 | 0.9625 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
rty/autotrain-bxkni-ayqdw
|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metrics
No validation metrics available
|
[
"black_footed_albatross",
"laysan_albatross",
"sooty_albatross",
"groove_billed_ani",
"crested_auklet",
"least_auklet",
"parakeet_auklet",
"rhinoceros_auklet",
"brewer_blackbird",
"red_winged_blackbird",
"rusty_blackbird",
"yellow_headed_blackbird",
"bobolink",
"indigo_bunting",
"lazuli_bunting",
"painted_bunting",
"cardinal",
"spotted_catbird",
"gray_catbird",
"yellow_breasted_chat",
"eastern_towhee",
"chuck_will_widow",
"brandt_cormorant",
"red_faced_cormorant",
"pelagic_cormorant",
"bronzed_cowbird",
"shiny_cowbird",
"brown_creeper",
"american_crow",
"fish_crow",
"black_billed_cuckoo",
"mangrove_cuckoo",
"yellow_billed_cuckoo",
"gray_crowned_rosy_finch",
"purple_finch",
"northern_flicker",
"acadian_flycatcher",
"great_crested_flycatcher",
"least_flycatcher",
"olive_sided_flycatcher",
"scissor_tailed_flycatcher",
"vermilion_flycatcher",
"yellow_bellied_flycatcher",
"frigatebird",
"northern_fulmar",
"gadwall",
"american_goldfinch",
"european_goldfinch",
"boat_tailed_grackle",
"eared_grebe",
"horned_grebe",
"pied_billed_grebe",
"western_grebe",
"blue_grosbeak",
"evening_grosbeak",
"pine_grosbeak",
"rose_breasted_grosbeak",
"pigeon_guillemot",
"california_gull",
"glaucous_winged_gull",
"heermann_gull",
"herring_gull",
"ivory_gull",
"ring_billed_gull",
"slaty_backed_gull",
"western_gull",
"anna_hummingbird",
"ruby_throated_hummingbird",
"rufous_hummingbird",
"green_violetear",
"long_tailed_jaeger",
"pomarine_jaeger",
"blue_jay",
"florida_jay",
"green_jay",
"dark_eyed_junco",
"tropical_kingbird",
"gray_kingbird",
"belted_kingfisher",
"green_kingfisher",
"pied_kingfisher",
"ringed_kingfisher",
"white_breasted_kingfisher",
"red_legged_kittiwake",
"horned_lark",
"pacific_loon",
"mallard",
"western_meadowlark",
"hooded_merganser",
"red_breasted_merganser",
"mockingbird",
"nighthawk",
"clark_nutcracker",
"white_breasted_nuthatch",
"baltimore_oriole",
"hooded_oriole",
"orchard_oriole",
"scott_oriole",
"ovenbird",
"brown_pelican",
"white_pelican",
"western_wood_pewee",
"sayornis",
"american_pipit",
"whip_poor_will",
"horned_puffin",
"common_raven",
"white_necked_raven",
"american_redstart",
"geococcyx",
"loggerhead_shrike",
"great_grey_shrike",
"baird_sparrow",
"black_throated_sparrow",
"brewer_sparrow",
"chipping_sparrow",
"clay_colored_sparrow",
"house_sparrow",
"field_sparrow",
"fox_sparrow",
"grasshopper_sparrow",
"harris_sparrow",
"henslow_sparrow",
"le_conte_sparrow",
"lincoln_sparrow",
"nelson_sharp_tailed_sparrow",
"savannah_sparrow",
"seaside_sparrow",
"song_sparrow",
"tree_sparrow",
"vesper_sparrow",
"white_crowned_sparrow",
"white_throated_sparrow",
"cape_glossy_starling",
"bank_swallow",
"barn_swallow",
"cliff_swallow",
"tree_swallow",
"scarlet_tanager",
"summer_tanager",
"artic_tern",
"black_tern",
"caspian_tern",
"common_tern",
"elegant_tern",
"forsters_tern",
"least_tern",
"green_tailed_towhee",
"brown_thrasher",
"sage_thrasher",
"black_capped_vireo",
"blue_headed_vireo",
"philadelphia_vireo",
"red_eyed_vireo",
"warbling_vireo",
"white_eyed_vireo",
"yellow_throated_vireo",
"bay_breasted_warbler",
"black_and_white_warbler",
"black_throated_blue_warbler",
"blue_winged_warbler",
"canada_warbler",
"cape_may_warbler",
"cerulean_warbler",
"chestnut_sided_warbler",
"golden_winged_warbler",
"hooded_warbler",
"kentucky_warbler",
"magnolia_warbler",
"mourning_warbler",
"myrtle_warbler",
"nashville_warbler",
"orange_crowned_warbler",
"palm_warbler",
"pine_warbler",
"prairie_warbler",
"prothonotary_warbler",
"swainson_warbler",
"tennessee_warbler",
"wilson_warbler",
"worm_eating_warbler",
"yellow_warbler",
"northern_waterthrush",
"louisiana_waterthrush",
"bohemian_waxwing",
"cedar_waxwing",
"american_three_toed_woodpecker",
"pileated_woodpecker",
"red_bellied_woodpecker",
"red_cockaded_woodpecker",
"red_headed_woodpecker",
"downy_woodpecker",
"bewick_wren",
"cactus_wren",
"carolina_wren",
"house_wren",
"marsh_wren",
"rock_wren",
"winter_wren",
"common_yellowthroat"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_CHINA_2_0
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_CHINA_2_0
This model is a fine-tuned version of [Visual-Attention-Network/van-tiny](https://huggingface.co/Visual-Attention-Network/van-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3441
- Accuracy: 0.8901
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.7165 | 0.96 | 18 | 1.0539 | 0.7504 |
| 0.842 | 1.9733 | 37 | 0.5513 | 0.8507 |
| 0.4818 | 2.9867 | 56 | 0.4022 | 0.8832 |
| 0.3813 | 4.0 | 75 | 0.3546 | 0.8911 |
| 0.3359 | 4.8 | 90 | 0.3441 | 0.8901 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_CHINA_2_1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_CHINA_2_1
This model is a fine-tuned version of [Visual-Attention-Network/van-tiny](https://huggingface.co/Visual-Attention-Network/van-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3633
- Accuracy: 0.8942
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 1
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.747 | 0.96 | 18 | 1.1983 | 0.7056 |
| 0.9923 | 1.9733 | 37 | 0.6524 | 0.8361 |
| 0.5633 | 2.9867 | 56 | 0.4483 | 0.8729 |
| 0.4208 | 4.0 | 75 | 0.3762 | 0.8911 |
| 0.3879 | 4.8 | 90 | 0.3633 | 0.8942 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
sachink365/example
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# sachink365/example
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.2098
- Validation Loss: 0.7835
- Train Accuracy: 0.912
- Epoch: 1
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 8000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.7520 | 1.5760 | 0.864 | 0 |
| 1.2098 | 0.7835 | 0.912 | 1 |
### Framework versions
- Transformers 4.48.3
- TensorFlow 2.18.0
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
Saurav1500/ezzxample1
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Saurav1500/ezzxample1
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.2038
- Validation Loss: 0.8430
- Train Accuracy: 0.886
- Epoch: 1
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 8000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.7343 | 1.6098 | 0.826 | 0 |
| 1.2038 | 0.8430 | 0.886 | 1 |
### Framework versions
- Transformers 4.47.1
- TensorFlow 2.17.1
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_CHINA_2_2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_CHINA_2_2
This model is a fine-tuned version of [Visual-Attention-Network/van-tiny](https://huggingface.co/Visual-Attention-Network/van-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3552
- Accuracy: 0.8969
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.7225 | 0.96 | 18 | 1.1618 | 0.7176 |
| 0.9126 | 1.9733 | 37 | 0.6123 | 0.8403 |
| 0.5284 | 2.9867 | 56 | 0.4291 | 0.875 |
| 0.4014 | 4.0 | 75 | 0.3605 | 0.895 |
| 0.3638 | 4.8 | 90 | 0.3552 | 0.8969 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_CHINA_2_3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_CHINA_2_3
This model is a fine-tuned version of [Visual-Attention-Network/van-tiny](https://huggingface.co/Visual-Attention-Network/van-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3923
- Accuracy: 0.8849
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 3
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.743 | 0.96 | 18 | 1.1833 | 0.6872 |
| 0.9877 | 1.9733 | 37 | 0.6720 | 0.8146 |
| 0.595 | 2.9867 | 56 | 0.4741 | 0.8714 |
| 0.468 | 4.0 | 75 | 0.4127 | 0.8804 |
| 0.4039 | 4.8 | 90 | 0.3923 | 0.8849 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_CHINA_2_4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_CHINA_2_4
This model is a fine-tuned version of [Visual-Attention-Network/van-tiny](https://huggingface.co/Visual-Attention-Network/van-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3826
- Accuracy: 0.8851
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.7717 | 0.96 | 18 | 1.1875 | 0.68 |
| 0.953 | 1.9733 | 37 | 0.6558 | 0.8175 |
| 0.5564 | 2.9867 | 56 | 0.4640 | 0.8642 |
| 0.4218 | 4.0 | 75 | 0.3893 | 0.8838 |
| 0.3865 | 4.8 | 90 | 0.3826 | 0.8851 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
zaimaasshafa/vit-emotion
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-emotion
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: 1.5208
- Accuracy: 0.4313
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5941 | 1.0 | 20 | 1.6841 | 0.3812 |
| 1.3825 | 2.0 | 40 | 1.6029 | 0.4188 |
| 1.1974 | 3.0 | 60 | 1.5437 | 0.4 |
| 1.026 | 4.0 | 80 | 1.5208 | 0.4313 |
| 0.9109 | 5.0 | 100 | 1.5022 | 0.4188 |
| 0.8522 | 6.0 | 120 | 1.5014 | 0.4313 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_NVIDIA_1_0
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_NVIDIA_1_0
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1639
- Accuracy: 0.9425
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6055 | 0.9886 | 65 | 0.4532 | 0.8375 |
| 0.3677 | 1.9924 | 131 | 0.2445 | 0.9092 |
| 0.2872 | 2.9962 | 197 | 0.1933 | 0.9333 |
| 0.248 | 4.0 | 263 | 0.1750 | 0.9358 |
| 0.2551 | 4.9430 | 325 | 0.1639 | 0.9425 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_NVIDIA_1_1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_NVIDIA_1_1
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1794
- Accuracy: 0.9333
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 1
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.7146 | 0.9886 | 65 | 0.4610 | 0.8383 |
| 0.4079 | 1.9924 | 131 | 0.2907 | 0.8933 |
| 0.3155 | 2.9962 | 197 | 0.2410 | 0.9075 |
| 0.2416 | 4.0 | 263 | 0.1967 | 0.9308 |
| 0.2667 | 4.9430 | 325 | 0.1794 | 0.9333 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_NVIDIA_1_2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_NVIDIA_1_2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1870
- Accuracy: 0.9408
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6153 | 0.9886 | 65 | 0.4135 | 0.8475 |
| 0.3616 | 1.9924 | 131 | 0.2620 | 0.9008 |
| 0.3104 | 2.9962 | 197 | 0.2363 | 0.9233 |
| 0.2635 | 4.0 | 263 | 0.2104 | 0.9283 |
| 0.233 | 4.9430 | 325 | 0.1870 | 0.9408 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_NVIDIA_1_3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_NVIDIA_1_3
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1888
- Accuracy: 0.9383
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 3
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.7169 | 0.9886 | 65 | 0.5175 | 0.8183 |
| 0.4345 | 1.9924 | 131 | 0.3127 | 0.8933 |
| 0.3188 | 2.9962 | 197 | 0.2558 | 0.9142 |
| 0.2652 | 4.0 | 263 | 0.1941 | 0.9308 |
| 0.2689 | 4.9430 | 325 | 0.1888 | 0.9383 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_NVIDIA_1_4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_NVIDIA_1_4
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1810
- Accuracy: 0.9325
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6882 | 0.9886 | 65 | 0.4411 | 0.84 |
| 0.3807 | 1.9924 | 131 | 0.2653 | 0.8992 |
| 0.3015 | 2.9962 | 197 | 0.2205 | 0.9158 |
| 0.2776 | 4.0 | 263 | 0.2322 | 0.9192 |
| 0.2496 | 4.9430 | 325 | 0.1810 | 0.9325 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_NVIDIA_2_0
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_NVIDIA_2_0
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4089
- Accuracy: 0.8610
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.8019 | 0.96 | 18 | 1.3423 | 0.5681 |
| 1.0929 | 1.9733 | 37 | 0.6976 | 0.7643 |
| 0.7036 | 2.9867 | 56 | 0.4959 | 0.8324 |
| 0.5388 | 4.0 | 75 | 0.4270 | 0.8565 |
| 0.4946 | 4.8 | 90 | 0.4089 | 0.8610 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_NVIDIA_2_1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_NVIDIA_2_1
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4224
- Accuracy: 0.8507
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 1
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.7557 | 0.96 | 18 | 1.1910 | 0.6061 |
| 1.025 | 1.9733 | 37 | 0.6480 | 0.7718 |
| 0.6633 | 2.9867 | 56 | 0.4953 | 0.8293 |
| 0.5677 | 4.0 | 75 | 0.4435 | 0.8404 |
| 0.5076 | 4.8 | 90 | 0.4224 | 0.8507 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_NVIDIA_2_2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_NVIDIA_2_2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4042
- Accuracy: 0.8639
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.7403 | 0.96 | 18 | 1.0666 | 0.6410 |
| 0.9223 | 1.9733 | 37 | 0.6355 | 0.7797 |
| 0.6381 | 2.9867 | 56 | 0.4770 | 0.8392 |
| 0.5549 | 4.0 | 75 | 0.4200 | 0.8579 |
| 0.5032 | 4.8 | 90 | 0.4042 | 0.8639 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_NVIDIA_2_3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_NVIDIA_2_3
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4078
- Accuracy: 0.8592
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 3
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.785 | 0.96 | 18 | 1.2513 | 0.6293 |
| 1.0633 | 1.9733 | 37 | 0.6514 | 0.7624 |
| 0.6802 | 2.9867 | 56 | 0.4874 | 0.8179 |
| 0.5651 | 4.0 | 75 | 0.4249 | 0.8401 |
| 0.5188 | 4.8 | 90 | 0.4078 | 0.8592 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_NVIDIA_2_4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_NVIDIA_2_4
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4208
- Accuracy: 0.8610
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.7364 | 0.96 | 18 | 1.1654 | 0.6229 |
| 0.9982 | 1.9733 | 37 | 0.6633 | 0.7719 |
| 0.6537 | 2.9867 | 56 | 0.5285 | 0.8082 |
| 0.5492 | 4.0 | 75 | 0.4331 | 0.8556 |
| 0.5101 | 4.8 | 90 | 0.4208 | 0.8610 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
JackyWW/vit-finetuned
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-finetuned
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: 1.2270
- Accuracy: 0.5563
## 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: 5e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0026 | 1.0 | 64 | 1.3046 | 0.5125 |
| 0.6945 | 2.0 | 128 | 1.2227 | 0.5437 |
| 0.4462 | 3.0 | 192 | 1.2127 | 0.5563 |
| 0.2831 | 4.0 | 256 | 1.2013 | 0.55 |
| 0.2379 | 5.0 | 320 | 1.2270 | 0.5563 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
prithivMLmods/Guard-Against-Unsafe-Content-Siglip2
|
# **Guard-Against-Unsafe-Content-Siglip2**
> **Guard-Against-Unsafe-Content-Siglip2** is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to detect NSFW content, including vulgarity and nudity, using the SiglipForImageClassification architecture.
> [!Important]
> Accuracy : 99 [ nudity, vulgarity, explicit content]
The model categorizes images into two classes:
- **Class 0:** "Unsafe Content" – indicating that the image contains vulgarity, nudity, or explicit content.
- **Class 1:** "Safe Content" – indicating that the image is appropriate and does not contain any unsafe elements.
# **Run with Transformers🤗**
```python
!pip install -q transformers torch pillow gradio
```
```python
import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Guard-Against-Unsafe-Content-Siglip2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def nsfw_detection(image):
"""Predicts NSFW probability scores for an image."""
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
labels = model.config.id2label
predictions = {labels[i]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=nsfw_detection,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="NSFW Content Detection"),
title="NSFW Image Detection",
description="Upload an image to check if it contains unsafe content such as vulgarity or nudity."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
```
TrainOutput(global_step=376, training_loss=0.11756020403922872, metrics={'train_runtime': 597.6963, 'train_samples_per_second': 20.077, 'train_steps_per_second': 0.629, 'total_flos': 1.005065949855744e+18, 'train_loss': 0.11756020403922872, 'epoch': 2.0})
# **Intended Use:**
The **Guard-Against-Unsafe-Content-Siglip2** model is designed to detect **inappropriate and explicit content** in images. It helps distinguish between **safe** and **unsafe** images based on the presence of **vulgarity, nudity, or other NSFW elements**.
### Potential Use Cases:
- **NSFW Content Detection:** Identifying images containing explicit content to help filter inappropriate material.
- **Content Moderation:** Assisting platforms in filtering out unsafe images before they are shared publicly.
- **Parental Controls:** Enabling automated filtering of explicit images in child-friendly environments.
- **Safe Image Classification:** Helping AI-powered applications distinguish between safe and unsafe content for appropriate usage.
|
[
"safe",
"unsafe"
] |
friscaoctaviyana/image_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# image_classification
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: 1.1140
- Accuracy: 0.6125
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9952 | 1.0 | 10 | 2.0130 | 0.3063 |
| 1.9609 | 2.0 | 20 | 1.9619 | 0.3563 |
| 1.8939 | 3.0 | 30 | 1.8425 | 0.4188 |
| 1.7588 | 4.0 | 40 | 1.6837 | 0.45 |
| 1.6045 | 5.0 | 50 | 1.5389 | 0.4688 |
| 1.4959 | 6.0 | 60 | 1.4618 | 0.5062 |
| 1.3876 | 7.0 | 70 | 1.3693 | 0.5375 |
| 1.295 | 8.0 | 80 | 1.3286 | 0.575 |
| 1.2328 | 9.0 | 90 | 1.3112 | 0.5563 |
| 1.1447 | 10.0 | 100 | 1.2627 | 0.5813 |
| 1.0791 | 11.0 | 110 | 1.2462 | 0.5813 |
| 1.0378 | 12.0 | 120 | 1.2410 | 0.6 |
| 1.0013 | 13.0 | 130 | 1.2353 | 0.5687 |
| 0.9512 | 14.0 | 140 | 1.2324 | 0.5625 |
| 0.8505 | 15.0 | 150 | 1.2216 | 0.575 |
| 0.8193 | 16.0 | 160 | 1.2061 | 0.6 |
| 0.7379 | 17.0 | 170 | 1.1829 | 0.5563 |
| 0.7133 | 18.0 | 180 | 1.2131 | 0.5625 |
| 0.6582 | 19.0 | 190 | 1.1882 | 0.5625 |
| 0.6663 | 20.0 | 200 | 1.0910 | 0.6188 |
| 0.589 | 21.0 | 210 | 1.1769 | 0.5687 |
| 0.5865 | 22.0 | 220 | 1.1242 | 0.6375 |
| 0.5336 | 23.0 | 230 | 1.1933 | 0.5375 |
| 0.5168 | 24.0 | 240 | 1.1956 | 0.575 |
| 0.4937 | 25.0 | 250 | 1.1943 | 0.6 |
| 0.487 | 26.0 | 260 | 1.1298 | 0.575 |
| 0.4582 | 27.0 | 270 | 1.1004 | 0.6312 |
| 0.4611 | 28.0 | 280 | 1.1108 | 0.5875 |
| 0.4386 | 29.0 | 290 | 1.2242 | 0.5813 |
| 0.4255 | 30.0 | 300 | 1.1560 | 0.5875 |
| 0.4136 | 31.0 | 310 | 1.2545 | 0.5437 |
| 0.4204 | 32.0 | 320 | 1.1661 | 0.6125 |
| 0.3959 | 33.0 | 330 | 1.1248 | 0.5875 |
| 0.3661 | 34.0 | 340 | 1.1475 | 0.6062 |
| 0.3603 | 35.0 | 350 | 1.1463 | 0.6125 |
| 0.3617 | 36.0 | 360 | 1.2161 | 0.5563 |
| 0.3761 | 37.0 | 370 | 1.2575 | 0.5312 |
| 0.3452 | 38.0 | 380 | 1.1720 | 0.575 |
| 0.3665 | 39.0 | 390 | 1.1165 | 0.6 |
| 0.3471 | 40.0 | 400 | 1.2233 | 0.5375 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
vissutagunawan/vit-emotion-classifier
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-emotion-classifier
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: 1.3506
- Accuracy: 0.525
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 20 | 2.0656 | 0.1938 |
| No log | 2.0 | 40 | 2.0408 | 0.2625 |
| No log | 3.0 | 60 | 1.9845 | 0.275 |
| No log | 4.0 | 80 | 1.8774 | 0.35 |
| 1.9717 | 5.0 | 100 | 1.7409 | 0.45 |
| 1.9717 | 6.0 | 120 | 1.6349 | 0.4437 |
| 1.9717 | 7.0 | 140 | 1.5541 | 0.4437 |
| 1.9717 | 8.0 | 160 | 1.5007 | 0.5188 |
| 1.9717 | 9.0 | 180 | 1.4531 | 0.525 |
| 1.4968 | 10.0 | 200 | 1.4263 | 0.5312 |
| 1.4968 | 11.0 | 220 | 1.3975 | 0.5188 |
| 1.4968 | 12.0 | 240 | 1.3915 | 0.525 |
| 1.4968 | 13.0 | 260 | 1.3270 | 0.5375 |
| 1.4968 | 14.0 | 280 | 1.3360 | 0.575 |
| 1.2146 | 15.0 | 300 | 1.3185 | 0.5437 |
| 1.2146 | 16.0 | 320 | 1.3288 | 0.55 |
| 1.2146 | 17.0 | 340 | 1.3262 | 0.5563 |
| 1.2146 | 18.0 | 360 | 1.3142 | 0.55 |
| 1.2146 | 19.0 | 380 | 1.2982 | 0.5625 |
| 1.0644 | 20.0 | 400 | 1.2704 | 0.5625 |
| 1.0644 | 21.0 | 420 | 1.2862 | 0.55 |
| 1.0644 | 22.0 | 440 | 1.2941 | 0.55 |
| 1.0644 | 23.0 | 460 | 1.2876 | 0.5312 |
| 1.0644 | 24.0 | 480 | 1.3066 | 0.5625 |
| 1.0161 | 25.0 | 500 | 1.2734 | 0.55 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
MonitorKarma/finetuned-indian-food
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-indian-food
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2731
- Accuracy: 0.9256
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6673 | 0.3003 | 100 | 0.6440 | 0.8725 |
| 0.5605 | 0.6006 | 200 | 0.5161 | 0.8842 |
| 0.4987 | 0.9009 | 300 | 0.4620 | 0.8831 |
| 0.4189 | 1.2012 | 400 | 0.4331 | 0.8799 |
| 0.5467 | 1.5015 | 500 | 0.4510 | 0.8767 |
| 0.3063 | 1.8018 | 600 | 0.4201 | 0.8916 |
| 0.2835 | 2.1021 | 700 | 0.3326 | 0.9182 |
| 0.2514 | 2.4024 | 800 | 0.4134 | 0.8874 |
| 0.2146 | 2.7027 | 900 | 0.3187 | 0.9129 |
| 0.2022 | 3.0030 | 1000 | 0.2949 | 0.9235 |
| 0.2299 | 3.3033 | 1100 | 0.2753 | 0.9309 |
| 0.2333 | 3.6036 | 1200 | 0.2699 | 0.9288 |
| 0.1469 | 3.9039 | 1300 | 0.2731 | 0.9256 |
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"burger",
"butter_naan",
"kaathi_rolls",
"kadai_paneer",
"kulfi",
"masala_dosa",
"momos",
"paani_puri",
"pakode",
"pav_bhaji",
"pizza",
"samosa",
"chai",
"chapati",
"chole_bhature",
"dal_makhani",
"dhokla",
"fried_rice",
"idli",
"jalebi"
] |
Oldy2008/Alice-image-Cc
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Team-me-2008
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1014
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.218 | 1.0 | 65 | 0.1154 |
| 0.0481 | 2.0 | 130 | 0.0741 |
| 0.0278 | 3.0 | 195 | 0.0286 |
| 0.0202 | 4.0 | 260 | 0.0213 |
| 0.0168 | 5.0 | 325 | 0.0178 |
| 0.0146 | 6.0 | 390 | 0.0159 |
| 0.0133 | 7.0 | 455 | 0.0149 |
| 0.0124 | 8.0 | 520 | 0.0141 |
| 0.0118 | 9.0 | 585 | 0.0137 |
| 0.0115 | 10.0 | 650 | 0.0136 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"label_0",
"label_1",
"label_2"
] |
dariel36/emotion_model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion_model
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: 1.3934
- Accuracy: 0.55
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8544 | 1.0 | 40 | 1.8094 | 0.3312 |
| 1.5093 | 2.0 | 80 | 1.5869 | 0.4625 |
| 1.2956 | 3.0 | 120 | 1.4686 | 0.5125 |
| 1.141 | 4.0 | 160 | 1.4099 | 0.55 |
| 0.9953 | 5.0 | 200 | 1.3934 | 0.55 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
pang-pang/vit-base-emotion-recognition
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-emotion-recognition
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:
- eval_loss: 1.5803
- eval_accuracy: 0.4938
- eval_runtime: 79.8377
- eval_samples_per_second: 2.004
- eval_steps_per_second: 0.251
- epoch: 10.9
- step: 436
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
shawnmichael/vit-fire-smoke-detection-v4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-fire-smoke-detection-v4
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 None dataset.
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"none",
"fire",
"smoke",
"smoke and fire"
] |
defikaalviani/results
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
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: 1.7324
- Accuracy: 0.3937
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 80 | 1.9211 | 0.3625 |
| No log | 2.0 | 160 | 1.7707 | 0.4062 |
| No log | 3.0 | 240 | 1.7324 | 0.3937 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7"
] |
inginjadibuparto/results
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
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: 1.4603
- Accuracy: 0.4562
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2036 | 1.0 | 10 | 1.5604 | 0.425 |
| 1.007 | 2.0 | 20 | 1.4904 | 0.4313 |
| 0.8453 | 3.0 | 30 | 1.4603 | 0.4625 |
| 0.6992 | 4.0 | 40 | 1.4409 | 0.4562 |
| 0.5703 | 5.0 | 50 | 1.4206 | 0.4688 |
| 0.4656 | 6.0 | 60 | 1.4128 | 0.4625 |
| 0.3896 | 7.0 | 70 | 1.4181 | 0.4625 |
| 0.3274 | 8.0 | 80 | 1.4162 | 0.475 |
| 0.2839 | 9.0 | 90 | 1.4199 | 0.45 |
| 0.2512 | 10.0 | 100 | 1.4261 | 0.4625 |
| 0.2278 | 11.0 | 110 | 1.4347 | 0.4625 |
| 0.2106 | 12.0 | 120 | 1.4319 | 0.4688 |
| 0.1973 | 13.0 | 130 | 1.4400 | 0.4688 |
| 0.1874 | 14.0 | 140 | 1.4453 | 0.4625 |
| 0.1797 | 15.0 | 150 | 1.4488 | 0.4562 |
| 0.1738 | 16.0 | 160 | 1.4542 | 0.4562 |
| 0.1693 | 17.0 | 170 | 1.4592 | 0.4625 |
| 0.166 | 18.0 | 180 | 1.4602 | 0.4562 |
| 0.1638 | 19.0 | 190 | 1.4607 | 0.4562 |
| 0.1627 | 20.0 | 200 | 1.4603 | 0.4562 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
bhumong/vit-age-classifier
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-age-classifier
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: 1.9590
- Accuracy: 0.2938
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0536 | 1.0 | 40 | 2.0348 | 0.25 |
| 1.9151 | 2.0 | 80 | 1.9824 | 0.3125 |
| 1.8544 | 3.0 | 120 | 1.9590 | 0.2938 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
dedesudiahna2803/results
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
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:
- eval_loss: 2.0673
- eval_model_preparation_time: 0.003
- eval_accuracy: {'accuracy': 0.16875}
- eval_runtime: 22.6244
- eval_samples_per_second: 7.072
- eval_steps_per_second: 0.442
- step: 0
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
dandiseptiandi/results
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8022
- Accuracy: 0.3187
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9171 | 1.0 | 40 | 1.9463 | 0.2375 |
| 1.512 | 2.0 | 80 | 1.8332 | 0.3 |
| 1.2369 | 3.0 | 120 | 1.8022 | 0.3187 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
saccanip/emotion-vit
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2708
- Accuracy: 0.625
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7925 | 1.0 | 80 | 1.2881 | 0.5625 |
| 0.8274 | 2.0 | 160 | 1.2765 | 0.525 |
| 0.3026 | 3.0 | 240 | 1.1460 | 0.55 |
| 0.0789 | 4.0 | 320 | 1.1513 | 0.5938 |
| 0.0204 | 5.0 | 400 | 1.2303 | 0.5938 |
| 0.0178 | 6.0 | 480 | 1.2826 | 0.6188 |
| 0.0118 | 7.0 | 560 | 1.2708 | 0.625 |
| 0.0089 | 8.0 | 640 | 1.2886 | 0.6188 |
| 0.0107 | 9.0 | 720 | 1.3031 | 0.6062 |
| 0.0086 | 10.0 | 800 | 1.3060 | 0.6062 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
paacamo/image_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# image_classification
This model is a fine-tuned version of [facebook/dinov2-base](https://huggingface.co/facebook/dinov2-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2697
- Accuracy: 0.5062
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.5374 | 1.0 | 10 | 2.2324 | 0.1688 |
| 1.9423 | 2.0 | 20 | 1.8484 | 0.35 |
| 1.6386 | 3.0 | 30 | 1.8398 | 0.3312 |
| 1.3437 | 4.0 | 40 | 1.4253 | 0.475 |
| 1.1703 | 5.0 | 50 | 1.4136 | 0.4625 |
| 1.0267 | 6.0 | 60 | 1.3867 | 0.4562 |
| 0.8702 | 7.0 | 70 | 1.2915 | 0.525 |
| 0.7696 | 8.0 | 80 | 1.2238 | 0.5687 |
| 0.6327 | 9.0 | 90 | 1.2732 | 0.5312 |
| 0.5017 | 10.0 | 100 | 1.2810 | 0.5188 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
adlynfakhreyz/results
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4399
- Accuracy: 0.525
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.3780 | 0.5125 |
| No log | 2.0 | 80 | 1.3870 | 0.5312 |
| No log | 3.0 | 120 | 1.4259 | 0.4938 |
| No log | 4.0 | 160 | 1.4361 | 0.525 |
| No log | 5.0 | 200 | 1.4399 | 0.525 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_FACEBOOK_2_0
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_FACEBOOK_2_0
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1606
- Accuracy: 0.9428
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.6577 | 0.96 | 18 | 0.5417 | 0.84 |
| 0.4139 | 1.9733 | 37 | 0.2571 | 0.9137 |
| 0.192 | 2.9867 | 56 | 0.1958 | 0.9314 |
| 0.1641 | 4.0 | 75 | 0.1630 | 0.9469 |
| 0.1181 | 4.8 | 90 | 0.1606 | 0.9428 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_FACEBOOK_2_1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_FACEBOOK_2_1
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1541
- Accuracy: 0.9472
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 1
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.7072 | 0.96 | 18 | 0.5959 | 0.8268 |
| 0.4469 | 1.9733 | 37 | 0.2756 | 0.9072 |
| 0.2358 | 2.9867 | 56 | 0.1952 | 0.9365 |
| 0.1674 | 4.0 | 75 | 0.1528 | 0.9487 |
| 0.1377 | 4.8 | 90 | 0.1541 | 0.9472 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_FACEBOOK_2_2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_FACEBOOK_2_2
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1423
- Accuracy: 0.9521
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.6004 | 0.96 | 18 | 0.5308 | 0.8460 |
| 0.3522 | 1.9733 | 37 | 0.2454 | 0.9169 |
| 0.2129 | 2.9867 | 56 | 0.1788 | 0.9435 |
| 0.1683 | 4.0 | 75 | 0.1525 | 0.9510 |
| 0.1288 | 4.8 | 90 | 0.1423 | 0.9521 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_FACEBOOK_2_3
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_FACEBOOK_2_3
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1468
- Accuracy: 0.9535
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 3
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.6687 | 0.96 | 18 | 0.5401 | 0.8504 |
| 0.3548 | 1.9733 | 37 | 0.2316 | 0.9214 |
| 0.1884 | 2.9867 | 56 | 0.1796 | 0.9406 |
| 0.1692 | 4.0 | 75 | 0.1614 | 0.945 |
| 0.1312 | 4.8 | 90 | 0.1468 | 0.9535 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_FACEBOOK_2_4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_FACEBOOK_2_4
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1577
- Accuracy: 0.9472
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.6033 | 0.96 | 18 | 0.5320 | 0.8403 |
| 0.3596 | 1.9733 | 37 | 0.2491 | 0.9213 |
| 0.1973 | 2.9867 | 56 | 0.1948 | 0.9354 |
| 0.1588 | 4.0 | 75 | 0.1542 | 0.9494 |
| 0.1333 | 4.8 | 90 | 0.1577 | 0.9472 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
LaLegumbreArtificial/SPIE_MULTICLASS_FACEBOOK_1_4
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SPIE_MULTICLASS_FACEBOOK_1_4
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0774
- Accuracy: 0.9733
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2258 | 0.9886 | 65 | 0.1581 | 0.9483 |
| 0.1392 | 1.9924 | 131 | 0.1028 | 0.9642 |
| 0.108 | 2.9962 | 197 | 0.0815 | 0.9717 |
| 0.0789 | 4.0 | 263 | 0.0878 | 0.9683 |
| 0.0597 | 4.9430 | 325 | 0.0774 | 0.9733 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
[
"burn through",
"contamination",
"good weld",
"lack of fusion",
"lack of penetration",
"misalignment"
] |
prithivMLmods/Deepfake-Quality-Assess-Siglip2
|
# **Deepfake-Quality-Assess-Siglip2**
**Deepfake-Quality-Assess-Siglip2** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to assess the quality of deepfake images using the **SiglipForImageClassification** architecture.
The model categorizes images into two classes:
- **Class 0:** "Issue in Deepfake" – indicating that the deepfake image has noticeable flaws or inconsistencies.
- **Class 1:** "High-Quality Deepfake" – indicating that the deepfake image is of high quality and appears more realistic.
# **Run with Transformers🤗**
```python
!pip install -q transformers torch pillow gradio
```
```python
import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Deepfake-Quality-Assess-Siglip2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def deepfake_detection(image):
"""Predicts deepfake probability scores for an image."""
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
labels = model.config.id2label
predictions = {labels[i]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=deepfake_detection,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Prediction Scores"),
title="Deepfake Quality Detection",
description="Upload an image to check its deepfake probability scores."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
```
# **Intended Use:**
The **Deepfake-Quality-Assess-Siglip2** model is designed to evaluate the quality of deepfake images. It helps distinguish between high-quality deepfakes and those with noticeable issues. Potential use cases include:
- **Deepfake Quality Assessment:** Identifying whether a generated deepfake meets high-quality standards or contains artifacts and inconsistencies.
- **Content Moderation:** Assisting in filtering low-quality deepfake images in digital media platforms.
- **Forensic Analysis:** Supporting researchers and analysts in assessing the credibility of synthetic images.
- **Deepfake Model Benchmarking:** Helping developers compare and improve deepfake generation models.
|
[
"issue in deepfake",
"high quality deepfake"
] |
cvmil/vit-base-patch16-224_augmented-v2_fft
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224_rice-leaf-disease-augmented-v2_fft
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3621
- Accuracy: 0.9226
## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 19
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9482 | 1.0 | 125 | 1.5012 | 0.5685 |
| 0.9894 | 2.0 | 250 | 0.6444 | 0.7976 |
| 0.3321 | 3.0 | 375 | 0.3859 | 0.8958 |
| 0.1115 | 4.0 | 500 | 0.3081 | 0.9107 |
| 0.0387 | 5.0 | 625 | 0.2980 | 0.9137 |
| 0.0204 | 6.0 | 750 | 0.2936 | 0.9137 |
| 0.0169 | 7.0 | 875 | 0.2953 | 0.9196 |
| 0.0078 | 8.0 | 1000 | 0.3067 | 0.9226 |
| 0.0034 | 9.0 | 1125 | 0.3087 | 0.9286 |
| 0.0025 | 10.0 | 1250 | 0.3139 | 0.9196 |
| 0.0023 | 11.0 | 1375 | 0.3142 | 0.9196 |
| 0.0019 | 12.0 | 1500 | 0.3288 | 0.9196 |
| 0.0013 | 13.0 | 1625 | 0.3359 | 0.9196 |
| 0.001 | 14.0 | 1750 | 0.3413 | 0.9226 |
| 0.0009 | 15.0 | 1875 | 0.3425 | 0.9226 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"bacterial leaf blight",
"brown spot",
"healthy rice leaf",
"leaf blast",
"leaf scald",
"narrow brown leaf spot",
"rice hispa",
"sheath blight"
] |
vlafoor/results
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3838
- Accuracy: 0.4688
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8225 | 1.0 | 64 | 1.5716 | 0.4531 |
| 0.8065 | 2.0 | 128 | 1.4313 | 0.4531 |
| 0.3993 | 3.0 | 192 | 1.3838 | 0.4688 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.5.1+cpu
- Datasets 3.2.0
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
prithivMLmods/Fire-Detection-Siglip2
|
# **Fire-Detection-Siglip2**
> **Fire-Detection-Siglip2** is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to detect fire, smoke, or normal conditions using the SiglipForImageClassification architecture.
Classification report:
precision recall f1-score support
fire 0.9940 0.9881 0.9911 1010
normal 0.9892 0.9941 0.9916 1010
smoke 0.9990 1.0000 0.9995 1010
accuracy 0.9941 3030
macro avg 0.9941 0.9941 0.9941 3030
weighted avg 0.9941 0.9941 0.9941 3030
The model categorizes images into three classes:
- **Class 0:** "Fire" – The image shows active fire.
- **Class 1:** "Normal" – The image depicts a normal, fire-free environment.
- **Class 2:** "Smoke" – The image contains visible smoke, indicating potential fire risk.
# **Run with Transformers🤗**
```python
!pip install -q transformers torch pillow gradio
```
```python
import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Fire-Detection-Siglip2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def fire_detection(image):
"""Classifies an image as fire, smoke, or normal conditions."""
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
labels = model.config.id2label
predictions = {labels[i]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=fire_detection,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Detection Result"),
title="Fire Detection Model",
description="Upload an image to determine if it contains fire, smoke, or a normal condition."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
```
# **Intended Use:**
The **Fire-Detection-Siglip2** model is designed to classify images into three categories: **fire, smoke, or normal conditions**. It helps in early fire detection and environmental monitoring.
### Potential Use Cases:
- **Fire Safety Monitoring:** Detecting fire and smoke in surveillance footage.
- **Early Warning Systems:** Helping in real-time fire hazard detection in public and private areas.
- **Disaster Prevention:** Assisting emergency response teams by identifying fire-prone areas.
- **Smart Home & IoT Integration:** Enhancing automated fire alert systems in smart security setups.
|
[
"fire",
"normal",
"smoke"
] |
prithivMLmods/AI-vs-Deepfake-vs-Real-Siglip2
|
# **AI-vs-Deepfake-vs-Real-Siglip2**
**AI-vs-Deepfake-vs-Real-Siglip2** is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to distinguish AI-generated images, deepfake images, and real images using the SiglipForImageClassification architecture.
The model categorizes images into three classes:
- **Class 0:** "AI" – The image is fully AI-generated, created by machine learning models.
- **Class 1:** "Deepfake" – The image is a manipulated deepfake, where real content has been altered.
- **Class 2:** "Real" – The image is an authentic, unaltered photograph.
# **Run with Transformers🤗**
```python
!pip install -q transformers torch pillow gradio
```
```python
import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/AI-vs-Deepfake-vs-Real-Siglip2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def image_classification(image):
"""Classifies an image as AI-generated, deepfake, or real."""
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
labels = model.config.id2label
predictions = {labels[i]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=image_classification,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Classification Result"),
title="AI vs Deepfake vs Real Image Classification",
description="Upload an image to determine whether it is AI-generated, a deepfake, or a real image."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
```
Classification report:
precision recall f1-score support
AI 0.9794 0.9955 0.9874 1334
Deepfake 0.9931 0.9782 0.9856 1333
Real 0.9992 0.9977 0.9985 1333
accuracy 0.9905 4000
macro avg 0.9906 0.9905 0.9905 4000
weighted avg 0.9906 0.9905 0.9905 4000
# **Intended Use:**
The **AI-vs-Deepfake-vs-Real-Siglip2** model is designed to classify images into three categories: **AI-generated, deepfake, or real**. It helps in identifying whether an image is fully synthetic, altered through deepfake techniques, or an unaltered real image.
### Potential Use Cases:
- **Deepfake Detection:** Identifying manipulated deepfake content in media.
- **AI-Generated Image Identification:** Distinguishing AI-generated images from real or deepfake images.
- **Content Verification:** Supporting fact-checking and digital forensics in assessing image authenticity.
- **Social Media and News Filtering:** Helping platforms flag AI-generated or deepfake content.
|
[
"ai",
"deepfake",
"real"
] |
prithivMLmods/Deepfake-Detect-Siglip2
|
# **Deepfake-Detect-Siglip2**
**Deepfake-Detect-Siglip2** is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to detect whether an image is real or a deepfake using the SiglipForImageClassification architecture.
The model categorizes images into two classes:
- **Class 0:** "Fake" – The image is detected as a deepfake or manipulated.
- **Class 1:** "Real" – The image is classified as authentic and unaltered.
# **Run with Transformers🤗**
```python
!pip install -q transformers torch pillow gradio
```
```python
import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Deepfake-Detect-Siglip2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def deepfake_detection(image):
"""Classifies an image as Fake or Real."""
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
labels = model.config.id2label
predictions = {labels[i]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=deepfake_detection,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Detection Result"),
title="Deepfake Detection Model",
description="Upload an image to determine if it is Fake or Real."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
```
# **Intended Use:**
The **Deepfake-Detect-Siglip2** model is designed to distinguish between **real and fake (deepfake) images**. It is useful for identifying AI-generated or manipulated content.
### Potential Use Cases:
- **Deepfake Detection:** Identifying AI-generated fake images.
- **Content Verification:** Assisting social media platforms in filtering manipulated content.
- **Forensic Analysis:** Supporting cybersecurity and investigative research on fake media.
- **Media Authenticity Checks:** Helping journalists and fact-checkers verify image credibility.
|
[
"fake",
"real"
] |
MarfinF/emotion_classification_adjusted
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion_classification_adjusted
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: 0.8104
- Accuracy: 0.8875
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 60
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 2.0787 | 1.0 | 20 | 0.1625 | 2.0753 |
| 2.073 | 2.0 | 40 | 0.1187 | 2.0737 |
| 2.0599 | 3.0 | 60 | 0.1938 | 2.0585 |
| 2.0363 | 4.0 | 80 | 0.1938 | 2.0368 |
| 2.0051 | 5.0 | 100 | 0.2625 | 1.9921 |
| 1.9348 | 6.0 | 120 | 0.3375 | 1.9185 |
| 1.8466 | 7.0 | 140 | 0.375 | 1.8056 |
| 1.755 | 8.0 | 160 | 0.4313 | 1.7292 |
| 1.676 | 9.0 | 180 | 0.45 | 1.6674 |
| 1.6244 | 10.0 | 200 | 0.475 | 1.6237 |
| 1.5661 | 11.0 | 220 | 0.5062 | 1.5973 |
| 1.5252 | 12.0 | 240 | 0.5 | 1.5262 |
| 1.4729 | 13.0 | 260 | 0.55 | 1.5050 |
| 1.4203 | 14.0 | 280 | 0.55 | 1.4784 |
| 1.364 | 15.0 | 300 | 0.525 | 1.5131 |
| 1.3262 | 16.0 | 320 | 0.5125 | 1.4776 |
| 1.3102 | 17.0 | 340 | 0.5563 | 1.4200 |
| 1.2595 | 18.0 | 360 | 0.5563 | 1.4329 |
| 1.2188 | 19.0 | 380 | 0.5375 | 1.4213 |
| 1.1991 | 20.0 | 400 | 0.525 | 1.4077 |
| 1.1526 | 21.0 | 420 | 0.6062 | 1.3625 |
| 1.1225 | 22.0 | 440 | 0.5437 | 1.3745 |
| 1.1283 | 23.0 | 460 | 0.5375 | 1.3677 |
| 1.0856 | 24.0 | 480 | 0.5625 | 1.3283 |
| 1.0559 | 25.0 | 500 | 0.5687 | 1.3440 |
| 1.0102 | 26.0 | 520 | 0.5437 | 1.3357 |
| 0.9915 | 27.0 | 540 | 0.5813 | 1.3377 |
| 0.9807 | 28.0 | 560 | 0.55 | 1.3824 |
| 0.9382 | 29.0 | 580 | 0.4938 | 1.4468 |
| 0.9857 | 30.0 | 600 | 0.8125 | 0.9923 |
| 0.9956 | 31.0 | 620 | 0.7625 | 1.0361 |
| 0.9875 | 32.0 | 640 | 0.775 | 1.0310 |
| 0.9582 | 33.0 | 660 | 0.7625 | 1.0572 |
| 0.9649 | 34.0 | 680 | 0.8063 | 0.9725 |
| 0.9099 | 35.0 | 700 | 0.7562 | 1.0355 |
| 0.9339 | 36.0 | 720 | 0.7937 | 1.0129 |
| 0.9045 | 37.0 | 740 | 0.7562 | 1.0315 |
| 0.8903 | 38.0 | 760 | 0.8187 | 0.9923 |
| 0.8799 | 39.0 | 780 | 0.7625 | 1.0386 |
| 0.8664 | 40.0 | 800 | 0.7438 | 1.0626 |
| 0.8351 | 41.0 | 820 | 0.7688 | 0.9885 |
| 0.8514 | 42.0 | 840 | 0.7875 | 0.9975 |
| 0.857 | 43.0 | 860 | 0.75 | 1.0169 |
| 0.8331 | 44.0 | 880 | 0.7937 | 0.9763 |
| 0.8093 | 45.0 | 900 | 0.7937 | 0.9645 |
| 0.8303 | 46.0 | 920 | 0.8 | 0.9880 |
| 0.8077 | 47.0 | 940 | 0.8063 | 1.0094 |
| 0.8082 | 48.0 | 960 | 0.7937 | 0.9757 |
| 0.8088 | 49.0 | 980 | 0.7438 | 1.0451 |
| 0.7985 | 50.0 | 1000 | 0.7875 | 0.9850 |
| 0.8013 | 51.0 | 1020 | 0.7688 | 1.0362 |
| 0.7882 | 52.0 | 1040 | 0.775 | 1.0007 |
| 0.8051 | 53.0 | 1060 | 0.7438 | 1.0314 |
| 0.812 | 54.0 | 1080 | 0.8 | 0.9782 |
| 0.7895 | 55.0 | 1100 | 0.725 | 1.0396 |
| 0.8012 | 56.0 | 1120 | 0.7688 | 0.9894 |
| 0.7973 | 57.0 | 1140 | 0.7875 | 0.9981 |
| 0.7946 | 58.0 | 1160 | 0.8063 | 0.9754 |
| 0.8437 | 59.0 | 1180 | 0.85 | 0.8544 |
| 0.8489 | 60.0 | 1200 | 0.7991 | 0.9062 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1
- Datasets 3.2.0
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
shawnmichael/vit-base-fire-smoke-detection-v6
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-fire-smoke-detection-v6
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 None dataset.
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"none",
"fire",
"smoke",
"smoke and fire"
] |
shawnmichael/efficienetb2-fire-smoke-detection-v1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# efficienetb2-fire-smoke-detection-v1
This model is a fine-tuned version of [google/efficientnet-b2](https://huggingface.co/google/efficientnet-b2) on the None dataset.
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"none",
"fire",
"smoke",
"smoke and fire"
] |
koray6/swin-tiny-patch4-window7-224-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0763
- Accuracy: 0.9753
## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2842 | 1.0 | 57 | 0.1351 | 0.9562 |
| 0.1742 | 2.0 | 114 | 0.0844 | 0.9735 |
| 0.1412 | 3.0 | 171 | 0.0763 | 0.9753 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.4.1+cu121
- Datasets 2.14.5
- Tokenizers 0.21.0
|
[
"annual crop",
"forest",
"herbaceous vegetation",
"highway",
"industrial buildings",
"pasture",
"permanent crop",
"residential buildings",
"river",
"sealake"
] |
KMH158/swin_large_neurofusion_classification
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"left",
"right",
"parietal",
"temporal",
"frontal",
"occipital",
"basal_ganglia",
"necrotic",
"cystic",
"glioblastoma_multiforme",
"astrocytoma",
"glioma",
"metastasis",
"abscess",
"dysembryoplastic_neuroepithelial_tumor"
] |
x43982938/my_awesome_food_model
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6065
- Accuracy: 0.9
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.712 | 1.0 | 63 | 2.4950 | 0.846 |
| 1.8359 | 2.0 | 126 | 1.7743 | 0.886 |
| 1.6182 | 2.96 | 186 | 1.6065 | 0.9 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu118
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"apple_pie",
"baby_back_ribs",
"bruschetta",
"waffles",
"caesar_salad",
"cannoli",
"caprese_salad",
"carrot_cake",
"ceviche",
"cheesecake",
"cheese_plate",
"chicken_curry",
"chicken_quesadilla",
"baklava",
"chicken_wings",
"chocolate_cake",
"chocolate_mousse",
"churros",
"clam_chowder",
"club_sandwich",
"crab_cakes",
"creme_brulee",
"croque_madame",
"cup_cakes",
"beef_carpaccio",
"deviled_eggs",
"donuts",
"dumplings",
"edamame",
"eggs_benedict",
"escargots",
"falafel",
"filet_mignon",
"fish_and_chips",
"foie_gras",
"beef_tartare",
"french_fries",
"french_onion_soup",
"french_toast",
"fried_calamari",
"fried_rice",
"frozen_yogurt",
"garlic_bread",
"gnocchi",
"greek_salad",
"grilled_cheese_sandwich",
"beet_salad",
"grilled_salmon",
"guacamole",
"gyoza",
"hamburger",
"hot_and_sour_soup",
"hot_dog",
"huevos_rancheros",
"hummus",
"ice_cream",
"lasagna",
"beignets",
"lobster_bisque",
"lobster_roll_sandwich",
"macaroni_and_cheese",
"macarons",
"miso_soup",
"mussels",
"nachos",
"omelette",
"onion_rings",
"oysters",
"bibimbap",
"pad_thai",
"paella",
"pancakes",
"panna_cotta",
"peking_duck",
"pho",
"pizza",
"pork_chop",
"poutine",
"prime_rib",
"bread_pudding",
"pulled_pork_sandwich",
"ramen",
"ravioli",
"red_velvet_cake",
"risotto",
"samosa",
"sashimi",
"scallops",
"seaweed_salad",
"shrimp_and_grits",
"breakfast_burrito",
"spaghetti_bolognese",
"spaghetti_carbonara",
"spring_rolls",
"steak",
"strawberry_shortcake",
"sushi",
"tacos",
"takoyaki",
"tiramisu",
"tuna_tartare"
] |
thenewsupercell/me_Emotion_DF_Image_VIT_V2
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Louis_Emotion_DF_Image_VIT_V2
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: 0.9591
- Accuracy: 0.6776
## 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: 5e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.148 | 1.0 | 1795 | 1.1148 | 0.5982 |
| 0.8387 | 2.0 | 3590 | 1.0005 | 0.6411 |
| 0.8989 | 3.0 | 5385 | 0.9685 | 0.6486 |
| 0.8048 | 4.0 | 7180 | 0.9354 | 0.6559 |
| 0.6871 | 5.0 | 8975 | 0.9494 | 0.6559 |
| 0.5955 | 6.0 | 10770 | 0.9346 | 0.6693 |
| 0.472 | 7.0 | 12565 | 0.9493 | 0.6746 |
| 0.4086 | 8.0 | 14360 | 0.9603 | 0.6776 |
| 0.3915 | 9.0 | 16155 | 0.9929 | 0.6773 |
| 0.3441 | 10.0 | 17950 | 1.0307 | 0.6760 |
| 0.3019 | 11.0 | 19745 | 1.0561 | 0.6768 |
| 0.3528 | 12.0 | 21540 | 1.0845 | 0.6743 |
| 0.1964 | 13.0 | 23335 | 1.1124 | 0.6734 |
| 0.3125 | 14.0 | 25130 | 1.1289 | 0.6734 |
| 0.1854 | 15.0 | 26925 | 1.1372 | 0.6704 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|
[
"angry",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
amilah1605/image_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# image_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5809
- Accuracy: 0.45
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0715 | 1.0 | 10 | 2.0701 | 0.1313 |
| 2.0623 | 2.0 | 20 | 2.0531 | 0.2 |
| 2.0302 | 3.0 | 30 | 2.0127 | 0.25 |
| 1.9632 | 4.0 | 40 | 1.9530 | 0.2812 |
| 1.8736 | 5.0 | 50 | 1.8625 | 0.325 |
| 1.7788 | 6.0 | 60 | 1.7627 | 0.3625 |
| 1.677 | 7.0 | 70 | 1.7067 | 0.3625 |
| 1.5986 | 8.0 | 80 | 1.6461 | 0.4313 |
| 1.5581 | 9.0 | 90 | 1.6029 | 0.45 |
| 1.5082 | 10.0 | 100 | 1.6011 | 0.4188 |
| 1.4822 | 11.0 | 110 | 1.5765 | 0.4625 |
| 1.4599 | 12.0 | 120 | 1.5717 | 0.4562 |
| 1.451 | 13.0 | 130 | 1.5575 | 0.4313 |
| 1.446 | 14.0 | 140 | 1.5642 | 0.4125 |
| 1.4422 | 15.0 | 150 | 1.5705 | 0.4437 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
Snppuzzle/Lanna-modelUo
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"aicchdii",
"aihlhlng",
"aikhaik",
"aikhpaa",
"aisaicch",
"aithyrath",
"araephs",
"binbn",
"borm",
"bupheph",
"cchanghan",
"chaaanaay",
"chataa",
"chingchang",
"dkbaw",
"edkelk",
"ekbehd",
"fanhan",
"khmaicch",
"khwaamsukh",
"nakeriiyn",
"nanglng",
"ngaanbaan",
"ochkhdii",
"omaanaacch",
"siththi",
"smkhwr"
] |
shawnmichael/vit-base-fashion-classification-v1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-fashion-classification-v1
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 None dataset.
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"red shirt",
"yellow shirt",
"blue shirt",
"black shirt",
"white shirt",
"red hoodie",
"yellow hoodie",
"blue hoodie",
"black hoodie",
"white hoodie"
] |
Pkonal/convNext-finetuned-skingpt
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
[
"acanthosis_nigricans",
"acne",
"acne_vulgaris",
"acquired_autoimmune_bullous_diseaseherpes_gestationis",
"acrodermatitis_enteropathica",
"actinic_keratosis",
"allergic_contact_dermatitis",
"aplasia_cutis",
"basal_cell_carcinoma",
"basal_cell_carcinoma_morpheiform",
"becker_nevus",
"behcets_disease",
"calcinosis_cutis",
"cheilitis",
"congenital_nevus",
"dariers_disease",
"dermatofibroma",
"dermatomyositis",
"disseminated_actinic_porokeratosis",
"drug_eruption",
"drug_induced_pigmentary_changes",
"dyshidrotic_eczema",
"eczema",
"ehlers_danlos_syndrome",
"epidermal_nevus",
"epidermolysis_bullosa",
"erythema_annulare_centrifigum",
"erythema_elevatum_diutinum",
"erythema_multiforme",
"erythema_nodosum",
"factitial_dermatitis",
"fixed_eruptions",
"folliculitis",
"fordyce_spots",
"granuloma_annulare",
"granuloma_pyogenic",
"hailey_hailey_disease",
"halo_nevus",
"hidradenitis",
"ichthyosis_vulgaris",
"incontinentia_pigmenti",
"juvenile_xanthogranuloma",
"kaposi_sarcoma",
"keloid",
"keratosis_pilaris",
"langerhans_cell_histiocytosis",
"lentigo_maligna",
"lichen_amyloidosis",
"lichen_planus",
"lichen_simplex",
"livedo_reticularis",
"lupus_erythematosus",
"lupus_subacute",
"lyme_disease",
"lymphangioma",
"malignant_melanoma",
"melanoma",
"milia",
"mucinosis",
"mucous_cyst",
"mycosis_fungoides",
"myiasis",
"naevus_comedonicus",
"necrobiosis_lipoidica",
"nematode_infection",
"neurodermatitis",
"neurofibromatosis",
"neurotic_excoriations",
"neutrophilic_dermatoses",
"nevocytic_nevus",
"nevus_sebaceous_of_jadassohn",
"papilomatosis_confluentes_and_reticulate"
] |
shawnmichael/beit-fashion-classification-v1
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# beit-fashion-classification-v1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the None dataset.
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"red shirt",
"yellow shirt",
"blue shirt",
"black shirt",
"white shirt",
"red hoodie",
"yellow hoodie",
"blue hoodie",
"black hoodie",
"white hoodie"
] |
ayooke97/emotion_classifier
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion_classifier
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: 1.6092
- Accuracy: 0.4125
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 2.0750 | 0.15 |
| No log | 2.0 | 80 | 2.0046 | 0.1875 |
| No log | 3.0 | 120 | 1.8909 | 0.3063 |
| No log | 4.0 | 160 | 1.7726 | 0.3563 |
| No log | 5.0 | 200 | 1.6970 | 0.3438 |
| No log | 6.0 | 240 | 1.6562 | 0.3937 |
| No log | 7.0 | 280 | 1.6269 | 0.4062 |
| No log | 8.0 | 320 | 1.6092 | 0.4125 |
| No log | 9.0 | 360 | 1.6012 | 0.4125 |
| No log | 10.0 | 400 | 1.5955 | 0.4125 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.5.1+cpu
- Datasets 3.2.0
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
koray6/convnext-tiny-224-finetuned-eurosat
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# convnext-tiny-224-finetuned-eurosat
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3390
- Accuracy: 0.9414
## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2852 | 1.0 | 57 | 0.9943 | 0.8728 |
| 0.5203 | 2.0 | 114 | 0.4478 | 0.9327 |
| 0.3931 | 3.0 | 171 | 0.3390 | 0.9414 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.4.1+cu121
- Datasets 2.14.5
- Tokenizers 0.21.0
|
[
"annual crop",
"forest",
"herbaceous vegetation",
"highway",
"industrial buildings",
"pasture",
"permanent crop",
"residential buildings",
"river",
"sealake"
] |
sk8erider/results
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
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: 1.6819
- Accuracy: 0.4375
## 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: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.7232 | 0.3875 |
| No log | 2.0 | 80 | 1.6919 | 0.4313 |
| No log | 3.0 | 120 | 1.6819 | 0.4375 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"label_0",
"label_1",
"label_2",
"label_3",
"label_4",
"label_5",
"label_6",
"label_7"
] |
prithivMLmods/Deepfake-Real-Class-Siglip2
|
# **Fake-Real-Class-Siglip2**
**Fake-Real-Class-Siglip2** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to **classify images as either Fake or Real** using the **SiglipForImageClassification** architecture.
The model categorizes images into two classes:
- **Class 0:** "Fake" – The image is detected as AI-generated, manipulated, or synthetic.
- **Class 1:** "Real" – The image is classified as authentic and unaltered.
```python
!pip install -q transformers torch pillow gradio
```
```python
import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Deepfake-Real-Class-Siglip2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def classify_image(image):
"""Classifies an image as Fake or Real."""
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
labels = model.config.id2label
predictions = {labels[i]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Classification Result"),
title="Fake vs Real Image Classification",
description="Upload an image to determine if it is Fake or Real."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
```
# **Intended Use:**
The **Fake-Real-Class-Siglip2** model is designed to classify images into two categories: **Fake or Real**. It helps in detecting AI-generated or manipulated images.
### Potential Use Cases:
- **Fake Image Detection:** Identifying AI-generated or altered images.
- **Content Verification:** Assisting platforms in filtering misleading media.
- **Forensic Analysis:** Supporting research in detecting synthetic media.
- **Authenticity Checks:** Helping journalists and investigators verify image credibility.
|
[
"fake",
"real"
] |
tofuyaki/training_emotion_classification
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# training_emotion_classification
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: 1.2897
- Accuracy: 0.5312
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 80 | 2.0647 | 0.1875 |
| No log | 2.0 | 160 | 1.9337 | 0.3688 |
| No log | 3.0 | 240 | 1.7220 | 0.4437 |
| No log | 4.0 | 320 | 1.5526 | 0.4938 |
| No log | 5.0 | 400 | 1.4479 | 0.5125 |
| No log | 6.0 | 480 | 1.4023 | 0.4938 |
| 1.4964 | 7.0 | 560 | 1.3220 | 0.5375 |
| 1.4964 | 8.0 | 640 | 1.2965 | 0.525 |
| 1.4964 | 9.0 | 720 | 1.2884 | 0.525 |
| 1.4964 | 10.0 | 800 | 1.2782 | 0.5437 |
| 1.4964 | 11.0 | 880 | 1.3028 | 0.5062 |
| 1.4964 | 12.0 | 960 | 1.2909 | 0.5375 |
| 0.3712 | 13.0 | 1040 | 1.2888 | 0.5375 |
| 0.3712 | 14.0 | 1120 | 1.2907 | 0.5312 |
| 0.3712 | 15.0 | 1200 | 1.2897 | 0.5312 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
[
"anger",
"contempt",
"disgust",
"fear",
"happy",
"neutral",
"sad",
"surprise"
] |
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