Instructions to use FangDai/Tiger-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FangDai/Tiger-Model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FangDai/Tiger-Model", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| export MODELNAME="Thyroid_Benign&PTC" | |
| export ARCH="resnet" | |
| export IMAGEPATH="./dataset" | |
| export TRAIN="Benign&PTC_train" | |
| export VALID="Benign&PTC_valid" | |
| export TEST="Benign&PTC_test" | |
| export BATCH=64 | |
| python main_train.py \ | |
| --modelname $MODELNAME \ | |
| --architecture $ARCH \ | |
| --imagepath $IMAGEPATH \ | |
| --train_data $TRAIN \ | |
| --valid_data $VALID \ | |
| --test_data $TEST \ | |
| --learning_rat 0.0005 \ | |
| --batch_size $BATCH \ | |
| --num_epochs 200 \ | |
| --Class 2 | |
| export MODELNAME="Thyroid_Benign&FTC" | |
| export ARCH="resnet" | |
| export IMAGEPATH="./dataset" | |
| export TRAIN="Benign&FTC_train" | |
| export VALID="Benign&FTC_valid" | |
| export TEST="Benign&FTC_test" | |
| export BATCH=64 | |
| python main_train.py \ | |
| --modelname $MODELNAME \ | |
| --architecture $ARCH \ | |
| --imagepath $IMAGEPATH \ | |
| --train_data $TRAIN \ | |
| --valid_data $VALID \ | |
| --test_data $TEST \ | |
| --learning_rat 0.0005 \ | |
| --batch_size $BATCH \ | |
| --num_epochs 200 \ | |
| --Class 2 | |
| export MODELNAME="Thyroid_Benign&MTC" | |
| export ARCH="resnet" | |
| export IMAGEPATH="./dataset" | |
| export TRAIN="Benign&MTC_train" | |
| export VALID="Benign&MTC_valid" | |
| export TEST="Benign&MTC_test" | |
| export BATCH=64 | |
| python main_train.py \ | |
| --modelname $MODELNAME \ | |
| --architecture $ARCH \ | |
| --imagepath $IMAGEPATH \ | |
| --train_data $TRAIN \ | |
| --valid_data $VALID \ | |
| --test_data $TEST \ | |
| --learning_rat 0.0005 \ | |
| --batch_size $BATCH \ | |
| --num_epochs 200 \ | |
| --Class 2 | |