Image Classification
timm
PyTorch
Safetensors
English
machine-learning
computer-vision
architecture
cnn
classification
food-classification
f1-score
precision
convnext
convnext-tiny
food-ai
multi-class-classification
multi-label-classification
multi-label-image-classification
multi-label
food-recognition
fine-tuning
feature-extraction
embeddings
transfer-learning
transfer-learning-with-cnn
attention-mechanism
cbam
cbam-cnn
cbam-attention
gem-pooling
food-ingredients
food-dataset
dataset-preparation
mm-food-100k
dataset-cleaning
cpu-inference
gpu-inference
huggingface
huggingface-models
huggingface-hub
model-hub
ml
ml-model
ml-architecture
model-architecture
inference
model-optimization
backbone
pretrained-model
pretrained-weights
imagenet-pretrained
timm-library
torchvision
foodtech
benchmark
research
production
deployment
open-source
Eval Results (legacy)
Instructions to use Alas-V/ConvNeXt-Food-CLF-75 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use Alas-V/ConvNeXt-Food-CLF-75 with timm:
import timm model = timm.create_model("hf_hub:Alas-V/ConvNeXt-Food-CLF-75", pretrained=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 846b4735b4b9c3c3a4f4d3e814b0c495af75e794d31b4ce5b09086fae8794a1a
- Size of remote file:
- 117 MB
- SHA256:
- ac63ec6ff15b90ac3f42931ef03bfa06cf7fe609d61e43cc83fa7265ee85e750
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.